2017 CONNECTOR - Homepage | MIT EECS · 2017-07-06 · 22017CON72ET 2017 CONNECTOR perspectives 1...

CONNECTORNews from the MIT Department of Electrical Engineering and Computer Science

2017

Anantha P. ChandrakasanDepartment Head

Nancy LynchAssociate Department Head

Asu Ozdaglar Associate Department Head

Anne Stuart Communications Officer Connector Editor

Suzana Lisanti Special Projects Manager Connector Photo Editor

Connector Production Team Design by Wing Ngan Printing by Puritan Capital

ContactMIT EECS Connector77 Massachusetts AvenueRoom 38-467 Cambridge, MA, 02139eecs.mit.edu [email protected]

1 A Letter from the Department Head

FEATURES

4 SuperUROP: Bots, Bit Flips, and Catching the Bus

6 When USAGE Speaks, EECS Listens

8 Helping Technology and Policy Work Together: Keertan Kini

10 The Balancing Act: Alyssa Cartwright

11 Three from EECS Win Lemelson-MIT Student Prizes

13 EECS Senior Wins ‘Jeopardy’ College Championship

15 StartMIT: Make It Your Business

17 StartMIT’s Innovation Night

19 StartMIT: Entrepreneurship in Action – on Two Coasts

21 The Engine: Up and Running

23 Masterworks and EECScon: Showcasing Students’ Work

RESEARCH UPDATES

26 Michael Carbin: Verifying Application-Specific Fault Tolerance via First-Class Fault Models

29 Stefanie Mueller: Interacting with Personal Fabrication Machines

32 Devavrat Shah: Social Data Processing

35 Max Shulaker: Next-Generation Nanosystems Q & A

CONNECTOR2017

FACULTY FOCUS

39 Faculty Awards

45 Faculty Research Innovation Fellowships (FRIFs)

46 New EECS Associate Department Heads

47 EECS Professorships

51 New Career Development Chairs

51 New Faculty

54 Remembering EECS Faculty: Mildred S. Dresselhaus, Robert Fano

EDUCATION NEWS

58 Machine Learning for Just About Everyone

60 The Internet of (Play) Things

63 Talk Science to Me

ALUMNI NEWS

66 Michal Depa: An Innovation ‘Ecosystem’ for Better Health Care

68 Dario Gil: On the Cutting Edge of the Cutting Edge

70 Philip Guo: Making Programming Accessible for All

72 Cal Newport: Dual Careers

74 Martin F. Schlecht: Life Beyond MIT

76 Lisa Su: An Industry Leader Returns to MIT

78 Margaret Guo: Swimming Toward Success

DONOR RECOGNITION

CONTENTS

15 StartMIT: Make It Your Business 60 The Internet of (Play) Things

eecs.mit.edu 2017 CONNECTOR perspectives 1

Greetings from MIT! This has been an exciting year for EECS as we celebrate our community’s achievements. We’ve developed new courses on in-demand topics, increased opportunities for research and entrepreneurship, and expanded efforts to enhance student and postdoc experiences. Following are several highlights from the past year, all explored in more depth in this publication’s pages.

Undergraduates as Researchers: SuperUROP

SuperUROP is a program designed to provide a more in-depth experience for juniors and seniors who have already completed a traditional undergraduate research opportunity program (UROP) project. Through participation in graduate-level research, and weekly guest lectures from distinguished speakers, the year-long program prepares students for work in academia, industry, and start-ups. The 12-credit Seminar in Undergraduate Advanced Research (6.UAR), offered in conjunction with SuperUROP, teaches students valuable technical communication skills. Each student is eligible to receive a named stipend that is generously funded by gifts from industry sources and alumni. In the 2016-2017 academic year, more than 140 students completed SuperUROP projects. Launched by EECS in 2012, the SuperUROP is now offered to all School of Engineering (SoE) departments.

Ongoing Dialogue Between Students and EECS Leadership: USAGE

The department continues to benefit from the regular input of the Undergraduate Student Advisory Group in EECS (USAGE), which I formed in the 2011-2012 academic year as part of the department’s strategic planning process. I’m grateful to this year’s 30-plus USAGE members, who shared their thoughts on everything from faculty advising to training for teaching assistants, met with our biennial Visiting Committee, and helped design the recently reopened EECS student lounge.

Entrepreneurship: StartMIT

Now in its fourth year, StartMIT is designed to shorten the learning curve for aspiring entrepreneurs, teaching them about startup culture and ethics, effective team-building, intellectual property issues, value propositions, and more. The program includes an intensive for-credit workshop held during MIT’s winter Independent Activities Period (IAP) and site visits to startups and other companies.

This year’s StartMIT students and postdocs heard from nearly 70 leading innovators. Students learned to develop and pitch their ideas, refined their projects in hands-on activities, and met with MIT alumni and other entrepreneurs.

During the 2017 spring break, some StartMIT participants traveled to California, where they visited leading San Francisco and Silicon Valley companies and networked with MIT alumni and local professionals. In addition, StartMIT students can leverage the MIT Sandbox Innovation Fund, a program offering tailored educational experiences, mentoring, and seed funding of up to $25,000 for qualified teams.

Entrepreneurship: The Engine

MIT established The Engine, an initiative that combines an accelerator, a network of facilities and experts, and a fund that will provide startups with stable financial support and access to costly resources. The Engine, which was announced in October 2016, closed its first investment fund of $150 million in April 2017. It will focus on startups that are developing “tough” technologies — such as robotics, manufacturing, energy, and biotech — which need time to commercialize. Charged by Provost Martin A. Schmidt, I led several MIT Working Groups focused on the development of Institute policies and procedures related to working with The Engine. Expect to hear much more about this exciting initiative in the coming months and years.

A LETTER FROM THE DEPARTMENT HEAD

Anantha Chandrakasan

LEADERSHIP UPDATEAs this issue of the Connector went to press, Anantha Chandrakasan was named Dean of the MIT School of Engineering, effective July 1, 2017. Chandrakasan succeeded Ian A. Waitz, who became MIT’s vice chancellor. A new EECS department head is expected to be named this fall. Asuman Ozdaglar, associate department head, will serve as interim department head during the search.For details, visit eecs.mit.edu or news.mit.edu.

eecs.mit.edu2 perspectives CONNECTOR 2017

Building Communication Skills: Comm6 Initiative

Effective communication skills are in high demand among employers today, so we continue to strengthen our offerings in that area. All EECS students have access to the department’s Communication Lab, where 10 peer advisors provide free coaching and feedback. Since September 2016, more than 250 students have visited the lab for assistance with everything from giving oral presentations to formatting their resumes, and more than 270 have attended workshops on posters, pitches, proposals, and other topics. We expect demand to keep growing here as well.

Enhancing the Postdoc Experience: Postdoc6

Through our Postdoc6 initiative, we’ve been increasing mentoring and networking opportunities for the postdocs who work in the four EECS-affiliated labs. Several times annually, we offer two-day offsite workshops to help small groups of postdocs learn leadership, management, and communication skills. In collaboration with the four labs, we offer regular social hours to help postdocs meet their colleagues. Feedback has been extremely positive, and future postdocs will benefit from these offerings as well.

Education, Research, Faculty News

In this issue, you’ll find articles on several EECS courses covering high-profile topics such as machine learning, mobile and sensor computing, and the Internet of Things, along with updates on the department’s new undergraduate curriculum and computer science minor. You’ll also find updates from researchers in the four EECS-affiliated labs, introductions to new faculty, details on appointments to professorships and career-development chairs, and an impressive list of our faculty’s latest awards, honors, and achievements.

Sadly, the department lost two giants this past year. The Faculty Focus section includes tributes to Institute Professor Emerita Mildred Dresselhaus, and to communication and computing pioneer Robert Fano, the founding director of Project MAC, which evolved into today’s Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.

EECS Alumni News

A special highlight of this issue is the collection of stories about EECS alumni (including Advanced Micro Devices CEO Lisa Su, a three-time alumna, who served as guest speaker

for MIT’s 2017 PhD hooding ceremony). I hope you’ll enjoy these accounts of some truly remarkable members of the EECS community.

EECS Leadership

Last year, I announced that I would be stepping down as department head, but have stayed on at the request of SoE Dean Ian A. Waitz to address some key issues facing the department. It has been a pleasure serving the department over the past year, collaborating with associate department heads Nancy Lynch and Asu Ozdaglar, who succeeded Silvio Micali and David Perreault in those roles. Nancy and Asu have had a busy year, contributing to the department in many ways, and in particular with the hiring of new faculty.

Diversity in Enrollments

EECS enrollments continue to set new records, in terms of both numbers and diversity. A total of 1,270 undergrads enrolled for Fall 2016 (up from 1,205 the previous year); of these, 39 percent are women and 12 percent identify as under-represented minorities (URMs). Thirty-three percent of this year’s MEng students are women. Of the 118 SM/PhD students who joined the department in Fall 2016, 21 percent are women and 5 percent identify as URMs. Finally, among 613 total graduate students for 2016-2017, 21 percent are women and 58 percent hold international citizenship. I’m also pleased to note that, for the fourth consecutive year, our entire entering graduate class received financial assistance via fellowships, research or teaching assistantships, or EECS-provided support.

As always, we’re eager to hear from EECS alumni, supporters, and friends, especially in exploring ways for you to share your expertise with current students and faculty. I welcome your input. Please stay in touch directly or through our website and other social-network channels.

Sincerely,

Anantha P. Chandrakasan Vannevar Bush Professor of Electrical Engineering and Computer Science Department Head, MIT Electrical Engineering and Computer Science

“ Effective communication skills are in high demand among employers today, so we continue to strengthen our offerings in that area.”

YOUR TURNWe’d love to hear your feedback on and story ideas for the Connector. Which articles did you most enjoy? What suggestions do you have for future features or profiles? Would you prefer to read future Connectors in print or online or both? Please send your thoughts to [email protected]. Thank you!

SuperUROP: Bots, Bit Flips, and Catching the Bus 4

When USAGE Speaks, EECS Listens 6

Helping Technology and Policy Work Together: Keertan Kini 8

The Balancing Act: Alyssa Cartwright 10

Three from EECS Win Lemelson-MIT Student Prizes 11

EECS Senior Wins ‘Jeopardy’ College Championship 13

StartMIT: Make It Your Business 15

StartMIT’s Innovation Night 17

StartMIT: Entrepreneurship in Action – on Two Coasts 19

The Engine: Up and Running 21

Masterworks and EECScon: Showcasing Students’ Work 23

FEATURES

4 features CONNECTOR 2017 eecs.mit.edu

BOTS, BIT FLIPS, AND CATCHING THE BUS

Imagine you’re walking in your dimly lit hallway. You’vedonned a pair of glasses that augment your reality. But the

new object in your environment — a sleeping dragon the size of a cat — looks disappointingly flat and cartoonish.

“You can tell it’s really fake, because the lighting [on it] doesn’t match your environment,” explains Elisa Young. A senior in MIT’s Department of Electrical Engineering and Computer Science (EECS), Young is researching how to make simulated objects reflect the light in a user’s environment. That way, they could look more like objects in the real world.

“I have such love for visual things in combination with computer science,” says Young, who is enthused to erode the boundary between virtual and concrete realities. She smiles at the thought. “It’s really cool to be like, ‘Oh, I’m living in a movie.’”

Young was among 151 students presenting their work during the December 2016 SuperUROP Research Review. Students enrolled in the School of Engineering SuperUROP program — an advanced Undergraduate Research Opportunities Program (UROP) — who undertake yearlong research projects shared their first term’s progress with faculty and graduate student mentors.

The relevance of the students’ work — research to enable faster commutes, use robots to help people, optimize buildings, and bolster defenses against massive cyberattacks — made an impression. “The students show incredible enthusiasm for their work,” says Anantha Chandrakasan, EECS department head. “It’s amazing to see them tackling

Engineering undergrads showcase their research after one semester of yearlong SuperUROP projects.

By Alison F. Takemura | EECS

More than 150 students presented their work during the midyear SuperUROP Research Review.

eecs.mit.edu 2017 CONNECTOR features 5

these tough problems.” Chandrakasan created the program in 2012 to provide students with an immersive, graduate-level research experience.

Cooper Sloan wants to make catching the bus easier using machine learning, a technique that allows computers to pick out subtle patterns. Using the last five years of GPS data from Boston buses, Sloan is unraveling the dynamics of the bus system, which has some quirks. “Slow buses tend to get slower and fast buses tend to get faster, which results in clumping,” says the EECS senior. Training a neural net, the architecture by which machines learn, will enable software to better predict bus arrival times — something commuters are waiting for.

Machines could also better assist people right in their homes, including senior citizens who have trouble walking. To that end, Alex LaGrassa is training robots to understand human speech. Right now, robots can easily act on pre-programmed information, she explains — for example, “Grab the Blue Ribbon muffin mix.” Yet in the flurry of a real kitchen, a person might say “grab the blue box,” using a description rather than a proper name. An EECS junior, LaGrassa is programming a robot to understand natural speech and actively search for objects that meet the human speaker’s criteria. “It has that extra step of figuring out ‘What is this person talking about?’” she says. Having robots bridge that linguistic divide between human and machine would be helpful “because, you know, requiring people to know how to program is problematic,” LaGrassa says.

Brenda Stern, a senior in civil and environmental engineering, is looking to design buildings with a smaller carbon footprint. Most people of think of the energy consumed during construction or once a building is up and running. But Stern focuses on the energy that went into creating the building materials, such as the concrete or steel columns, slabs, and

framework. They all have embodied carbon, or the carbon emissions associated with their production. “I’m finding ways to minimize these materials to create more sustainable structures,” she says.

Valerie Sarge is working on hardware, trying to do more with less data. An EECS junior, Sarge is researching how to transform low-resolution images into high-resolution facsimiles. By using field-programmable gate arrays, or FPGAs, she can program hardware with a neural net to extrapolate information, mimicking the way a human would fill in the dots: “We quickly hallucinate the information that your brain expects to see.” That means you could download a low-resolution video onto your phone, and watch it in high-definition.

The goal is to push current limits of computing power by moving some of the computational demand from software into hardware. If scientists can achieve that, Sarge says, “every type of research, every type of analysis, every type of study that requires computing power will become easier.”

Another SuperUROP is preparing hardware for space. Madeleine Waller, an EECS senior, is running tests to make the Transiting Exoplanet Survey Satellite (TESS), scheduled for launch in December 2017, more resilient.

The satellite will contend with meddlesome cosmic rays, which can cause computer chip bits to flip, creating errors in the data. Waller is testing the satellite’s FPGA by intentionally injecting errors into a model data stream. “We’re trying to trigger all the watchdog protocols in the system,” she says. Ensuring they work would give researchers a better chance of confidently detecting exoplanets.

But while some threats to systems are random, others are maliciously calculated. In October, Hyunjoon Song, a senior in EECS, saw news that millions of infected bots had attacked Akamai, a network server company. The strike had wielded an army of “Internet of Things” devices, such as digital cameras and video recorders. Collectively dubbed the Mirai botnet, the bots battered Akamai with 620 Gb per second to attempt to overload the company’s servers in what’s known as a distributed denial of service (DDoS) attack. “This was one of the most powerful DDoS attacks ever,” he says. “And the problem is that there are billions more of these devices that are just as vulnerable. The Mirai botnet is just the beginning.”

Song is looking at the source code and interacting with the Mirai botnet to understand its architecture and sift for vulnerabilities, in the hope of preventing more serious attacks.

SuperUROP continued through the spring semester, with many students presenting the results of their research at EECScon, MIT’s annual undergraduate research conference, in April and an awards and certificate program in mid-May.

For more about SuperUROP, visit superurop.mit.edu and eecs.mit.edu

The event gives SuperUROP participants a chance to discuss their research projects with faculty, graduate students, and other attendees.


When big changes are afoot in Course 6 — and evenwhen the changes are small — students involved in the

Undergraduate Student Advisory Group in EECS (USAGE) have their fingers on the pulse of the department.

Founded during the 2011–2012 academic year, USAGE is an advisory committee of about 30 students who provide the leaders of MIT’s Electrical Engineering and Computer Science Department (EECS, also known as Course 6) with insight into the how the department’s nearly 1,500 undergraduates view curriculum changes, workload, and more.

“I see us as kind of a sounding board for the department,” says Natalie Lao, a graduate student in the Master of Engineering (MEng) program, who has served on the committee since her freshman year. “Empowerment is a big part of USAGE. If you want to see something change in Course 6 and it’s reasonable — and other people agree with you — it’s one of the best ways to have your voice heard.”

Over the years, USAGE has helped shape such signature EECS offerings as SuperUROP, the fast-growing advanced Undergraduate Research Opportunities Program (UROP), and StartMIT, an intensive workshop on entrepreneurism offered during MIT’s between-semesters Independent Activities Period (IAP). In 2014, feedback from USAGE prompted the creation of a new undergraduate student lounge in Building 36; this year, members have helped guide the renovation of another lounge in Building 38.

“It’s great to get student input on issues of importance to them before we implement a program,” says Anantha P. Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science and EECS department head. “They give us a different perspective and bring up things to think about.”

Among its many contributions, USAGE regularly represents the student perspective on Course 6 to the Visiting Committee that evaluates the department every two years on behalf of the MIT Corporation (the Institute’s governing board). USAGE surveys students about such issues as workload, curriculum, and advising, and then produces a brief report; members later meet with the Visiting Committee to present the group’s findings.

Lao found the experience of preparing a report for the 2015 Visiting Committee quite valuable. “That was a big project, and I learned a lot from doing it,” she says, noting she particularly enjoyed relating USAGE’s findings to the impressive roster of academics and professionals who serve on the Visiting Committee. “That was really awesome because we got to present our thoughts to all these world leaders in tech.”

Members of USAGE also met with this year’s Visiting Committee, providing input that is “extremely valuable,” Chandrakasan says. “This helps us address issues of importance to students, such as class size, workload, and ways we can make the department more inclusive.”

USAGE meets every few weeks during the school year, which can be a significant time commitment for any student, but members say they participate to give back to the department. “It’s a way for me to contribute and make Course 6 a better place,” Lao says. “I’m part of this community, and it’s great to see it growing and becoming better.”

In addition, USAGE provides students with “an exceptional opportunity to see how the department functions at a high

WHEN USAGE SPEAKS, EECS LISTENSStudent group provides first-hand feedback on Course 6 issues ranging from class sizes to curriculum changes.

By Kathryn O’Neill | Connector Contributor

Front row, left to right: Lisette Tellez, Sravya Vishnubhatla, Sarah Hensley, Allison Lemus | Back row, left to right: Matthew Kalinowski, Jimmy Mawdsley, Ignacio Estay Forno, Natalie Lao, Anish Athalye


level,” says Kai Aichholz, a group member and senior in electrical engineering and computer science.

For example, just over the course of this year, USAGE has discussed such department concerns as faculty advising and teaching loads, training for teaching assistants, and the system for flagging students based on academic performance. The group also heard several presentations. Chancellor Cynthia Barnhart described how MIT is working to become more attractive to admitted students. Clinical Director for Campus Life Maryanne Kirkbride discussed the Institute’s efforts to improve students’ overall well-being. Asuman Ozdaglar, a professor of electrical engineering and computer science and EECS associate department head, outlined a proposed new interdisciplinary major.

Nalini Singh, a senior in electrical engineering and computer science, says she values her USAGE participation because it gives students a direct line of communication to EECS leaders. “This is an efficient way to raise concerns with the department,” says Singh, who is also president of MIT’s chapter of the national honor society Eta Kappa Nu (HKN).

For example, HKN was able to approach USAGE this fall to advocate for the Chu Lounge renovation. With USAGE’s support, the project quickly gained ground; students discussed how the space could be repurposed and then worked together to help redesign the lounge to include new furniture, new electronics, and card-reader access.

“We really pushed for it to be a dedicated social space, and the department accepted that,” says Alisha Saxena, a junior in electrical engineering and computer science and a USAGE member who is also president of the MIT IEEE/ACM Club. “It’s going to be great for my club. We can hold more social events.”

Ultimately, USAGE’s impact is “a lot of small things that add up,” says Anish Athalye, who is completing both his senior year in computer science and engineering and his MEng degree. “The department does take our feedback into account, which I think is great.”

Front row, left to right: Uttara Chakraborty, Alyssa Cartwright, Nalini Singh, Nancy Hung Back row, left to right: Allan Sadun, Daniel Richman, Billy Caruso, Kai Aichholz, Alexander Sludds, Isaac Kontomah | Not pictured: Efe Akengin, Suma Anand, Logan Engstrom, Hassan Kane, Keertan Kini, Aneliese Newman, Alisha Saxena, Alex Sloboda, Tejas Sundaresan, Sarah Wooders

Photos: Anne Stuart

“ Empowerment is a big part of USAGE. If you want to see something change in Course 6—and other people agree with you—it’s one of the best ways to have your voice heard.”

—Natalie Lao, long-time USAGE member


Keertan Kini can sum up his approach to life at MIT in onesentence. “When you’re part of a community, you want to

leave it better than you found it,” says Kini, a graduate student in the Department of Electrical Engineering and Computer Science (EECS, also known as Course 6). That philosophy has guided Kini throughout his years at MIT, as he works to improve policy both within the Institute and beyond.

As a member of the Undergraduate Student Advisory Group (USAGE), former chair of the Course 6 Underground Guide Committee, and a member of the Internet Policy Research Initiative (IPRI) and the Advanced Network Architecture Group, Kini has focused his research on finding ways that technology and policy can work together. As he puts it: “There can be unintended consequences when you don’t have technology makers who are talking to policymakers and you don’t have policymakers talking to technologists.” His goal is to allow them to talk to each other.

At 14, Kini first started to get interested in politics. He volunteered for President Obama’s 2008 campaign, making calls and putting up posters. After that, he was campaigning for a ballot initiative to raise more funding for his high school. He hasn’t stopped being interested in public policy since.

High school was also where Kini became interested in computer science. He took a computer science class in high school at his sister’s recommendation, and in his senior year, he started watching computer science lectures on MIT OpenCourseWare (OCW) by Hal Abelson, the Class of 1922 Professor of EECS.

HELPING POLICY AND TECHNOLOGY WORK TOGETHEREECS graduate student Keertan Kini is working to strengthen the intersection between the two fields.

By Rachel van Heteren | EECS

Photo: Rachel van Heteren


“That lecture reframed what computer science was. I loved it,” Kini recalls. “The professor said, ‘It’s not about computers, and it’s not about science.’ It might be an art or engineering, but it’s not science, because what we’re working with are idealized components, and ultimately the power of what we can actually achieve with them is not based so much on physical limitations so much as the limitations of the mind.”

In part thanks to Abelson’s OCW lectures, Kini came to MIT to study electrical engineering and computer science. He received an SB in EECS in 2016 and is now completing a master of engineering (MEng) degree.

Combining two disciplines

Kini set his policy interest to the side his freshman year, until he took the Foundations of Information Policy class (6.805J) with Abelson, the same professor whose lectures had attracted him to computer science in the first place. After that, Kini joined Abelson and Daniel Weitzner, a principal research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL), in putting together a big data and privacy workshop for the White House in the wake of the Edward Snowden leak of classified information from the National Security Agency. Later, Kini became a teaching assistant for 6.805J.

With Weitzner as his advisor, Kini went on to work on a SuperUROP, an advanced version of the Undergraduate Research Opportunities Program (UROP) in which students undertake an intensive research project for a full year. Kini’s project focused on making it easier for organizations that had experienced a cybersecurity breach to share how the breach happened with other organizations, without accidentally releasing private or confidential information as well.

Typically, when a security breach happens, there is a “human bottleneck,” Kini says. Humans have to manually check all information they exchange with other organizations to ensure they don’t share private information or get themselves into legal hot water. The process is time-consuming for all organizations involved. Kini created a prototype of a system that could automatically screen information about cybersecurity breaches, determining what data had to be checked by a human, and what was safe to send along.

Once finished with his SuperUROP, Kini became involved in the development of Votemate, a web app designed to simplify voter registration nationwide. But Kini’s interest in Votemate wasn’t only about increasing the number of people who register. “I think most people in this nation are centrist, and one of the reasons our political system gets polarized is because people who are polarized primarily turn out to vote,” he says. He believes the only reliable solution is increasing the number of people who actually cast ballots.

Shaping policy on campus

Kini is also involved in making changes within the Institute. As a member of the Undergraduate Student Advisory Group (USAGE), Kini has been involved in exploring ways to revitalize the electrical engineering curriculum, redesigning the

undergraduate lounge, and compiling a list of the resources available to EECS students. He is especially interested in making sure students know about the MIT resources for prospective entrepreneurs. Among them: StartMIT, an intensive Independent Activities Period (IAP) workshop designed to help students learn what’s involved in launching a startup.

“At MIT, we try to solve very difficult challenges, we try to solve very meaningful technical problems,” Kini says. “But what gets lost in the shuffle is: After you come up with a great idea, how do you get it out of your head and into the world?” StartMIT helps bridge that gap, he says.

Thanks to his own StartMIT experience, Kini knows that he wants to launch a business one day. “I see starting a company not only as an option, but the option,” he says. “It’s a way to make sustainable change in the world.”

Editor’s Note: In May 2017, EECS recognized Keertan Kini’s contributions with a Paul L. Penfield Student Service Award.

“ There can be unintended consequences when you don’t have technology makers who are talking to policymakers and you don’t have policymakers talking to technologists.”

—Keertan Kini


Alyssa Cartwright’s first order of business at MIT didn’tinvolve science or technology. “One of the biggest things

I had to do when I got here was learn to prioritize,” says Cartwright, a member of the class of 2017. “I had to learn to do my work in an efficient manner to have time left for what was important to me.”

Apparently, she aced that lesson, managing to balance her studies in electrical science and engineering (6-1) with other activities that included co-chairing MIT’s largest student-run research conference and serving on an elite group that meets regularly with the leadership of the Department of Electrical Engineering and Computer Science (EECS). Along the way, she spent a summer conducting research in a National Science Foundation-funded program at Vanderbilt University in Nashville, Tenn., collected two EECS awards for her Undergraduate Research Opportunities Program (UROP) and SuperUROP projects, served as a teaching assistant — and still found time to play clarinet in the MIT Symphony Orchestra all four years.

Cartwright, who is from Williamsville, N.Y., just outside Buffalo, jokes that she grew up with both sides of Course 6: her father is an electrical engineer; her mother is a system administrator. “From an early age, I thought engineering was a very cool field,” she recalls.

Music has also long been part of her life. She began playing the clarinet in the fourth grade, which led not only to a spot in MIT’s orchestra, but to a minor in music as well. “Music is a very important part of my personality and my worldview and how I approach things,” she says. “It’s really important to me to have a creative outlet.”

Cartwright found her research focus in 2015, when she was among 17 students from throughout the United States who participated in Research Experience for Undergraduates (REU) at Vanderbilt. Her project involved designing, simulating, and testing photonic crystals for biosensing. “That was my first

experience with optics and biology, and that’s where I learned that I wanted to focus in that area,” she says. She also loved collaborating with her peers at Vanderbilt: “You live with the other students who are doing the program; you go to the labs and do your work and talk about it later,” she recalls. “It’s like a mini-graduate school.”

In the fall of 2017, Cartwright will start graduate school for real, working toward a PhD in electrical engineering at Stanford University. She plans on an academic career, but isn’t sure yet where that will take her.

Meanwhile, she offers this recommendation to future EECS students: “Get involved with research as soon as you can — even if you feel you are wildly underqualified,” she says. “The faculty are invested in their students. If you put your time into the experiences, they’ll put in the time to help you.” And, as a member of two engineering honor societies, she acknowledges that academics are crucial — but only part of the overall MIT picture. “Try not to stress too much about classes,” she advises. “There are other parts of life here.”

Editor’s Note: In May 2017, EECS recognized Alyssa Cartwright’s contributions with a Paul L. Penfield Student Service Award.

THE BALANCING ACT Alyssa Cartwright engineered a rich MIT experience that included coursework, research, music — and more.

By Anne Stuart | EECS

Serving on the EECS student advisory committee was just one of Alyssa Cartwright’s activities at MIT.


Three students in the Department of Electrical Engineeringand Computer Science were among the winners of the 2017

Lemelson-MIT Student Prizes, which are designed to honor the nation’s most inventive college students.

The prizes, presented in April by the Lemelson-MIT Program, honored the EECS students for their inventions in the “Use it!” category, which focuses on technology that can improve consumer devices.

Chandani Doshi and Tania Yu, both seniors in EECS, were part of MIT’s Team Tactile, the $10,000 Lemelson-MIT “Use it!” Undergraduate Team Winner. The six-member team developed Tactile, a portable device that converts text to braille in real time. The technology allows people who are visually impaired to take a picture of printed text, which is then transcribed to braille on a refreshable display. Other Team Tactile members include Grace Li, Jessica (Jialin) Shi, and Charlene Xia, all seniors in the Department of Mechanical Engineering (MechE), and Chen (Bonnie) Wang, a senior in the Department of Materials Science and Engineering.

Apoorva Murarka, a PhD candidate in electrical engineering, was the $15,000 Lemelson-MIT “Use it!” Graduate Winner.

Murarka developed a 125-nanometer-thick membrane —approximately one-thousandth the width of a human hair — to produce high-fidelity sound more efficiently. This technology can be applied to hearing aids, earphones, or other consumer electronic devices, resulting in superior sound quality and longer battery life. Murarka previously received bachelor’s and master’s degrees in electrical engineering from MIT.

Celebrating young inventors

The Lemelson-MIT Student Prize is a national collegiate invention prize program, supported by the Lemelson Foundation, which celebrates young inventors who have designed and built prototypes of inventions to solve social problems. For 2017, the Lemelson-MIT Program honored four undergraduate teams and five individual graduate inventors. “These students display the brilliance and hope of their generation,” said Dorothy Lemelson, Lemelson Foundation chair. “We are proud to recognize them for their achievements.”

Students entered their technology-based inventions in “Use it!” and three other categories: “Cure it!” (for improving health care), “Drive it!” (for improving transportation), and “Eat it!” (for improving food or agriculture). Other 2017

Left to right: Chandra Doshi, Jessica (Jialin) Shi, Chen (Bonnie) Wang, Charlene Xia, Tania Yu, and Grace Li of MIT’s winning undergraduate Team Tactile

THREE FROM EECS WIN LEMELSON-MIT STUDENT PRIZESEECS seniors Chandani Doshi and Tania Yu were part of Team Tactile, which invented a portable, real-time text to braille converter. PhD candidate Apoorva Murarka developed technology designed to more efficiently produce high-fidelity sound.

By Anne Stuart | EECSPhoto: Brian Smale, Microsoft


Lemelson-MIT Student Prize winners from MIT included two PhD candidates in mechanical engineering and one in aeronautics and astronautics. The Lemelson-MIT Program also honored graduate students or undergraduate teams from Stanford University, the University of California Berkeley, the University of Iowa, and the University of Maryland.

Lemelson-MIT Student Prize applicants were evaluated by screening committees with expertise in the invention categories as well as by a national judging panel of industry leaders. Screeners and judges assessed entries on the breadth and depth of inventiveness and creativity, potential for societal benefit and economic commercial success, impact on community and environmental systems, and the candidates’ experience as role models for youth.

To learn more about the Lemelson-MIT Program, including instructions on applying for future prizes, visit lemelson.mit.edu

Graduate winner Apoorva Murarka, PhD candidate in electrical engineering

“ These students display the brilliance and hope of their generation.”

—Dorothy Lemelson, Chair, The Lemelson Foundation

StartMIT, covered elsewhere in this section, offers another opportunity for young entrepreneurs and inventors to hone their ideas and hear from the pros. Following are some tweets from the 2017 StartMIT experience.

@ahamino: Awesome startup vibe @medialab StartMIT innovation night.

@MIT_Alumni: @drewhouston ’05 stops by @MITEECS’s #StartMIT to talk collaboration, managing scale and @Dropbox.

@jpenswick: Proud to represent @cmtelematics at the #StartMIT Innovation Event tonight.

@kochinstitute: Drop and give me career advice: Bob Langer & Susan Hockfield impart wisdom at StartMIT’s entrepreneurship boot camp.

@TriciaCotter:#StartMIT @eship @aulet Great teams presenting from the MIT ecosystem.

@juanleungli: Amazing @MIT support for Boston founders. Inspiring speakers… Fired up for future #startmit

@iza_wit: Feel[s] strange in some ways and so wonderful, having 6 women on a panel at a tech conference and it’s not a [women’s conference] #StartMIT @MITEECS @MIT

@jeanhammond: @ktrae Katie Rae telling how entrepreneurship thinking can drive change as a part of a panel of innovative women #StartMIT


Let’s be honest: Even the most disciplined college studentsdon’t pay attention all the time during their classes. The

temptations of a hushed conversation with a classmate, a daydream, or any number of digital distractions can be hard to resist.

But one could argue that senior Lilly Chin had an excellent excuse for tuning out during one of her comparative media studies classes in October 2016: She was taking a 10-minute online test to qualify for the popular TV quiz show “Jeopardy!” It was only offered on one specific date, at one specific time — which happened to be during class — so she had little choice. “I was trying not to get caught by the teacher while I was answering the questions,” she later confided to a “Jeopardy!” film crew, barely suppressing a giggle.

In the end, it was worth the risk. Chin went on to become a contestant on the show, made it to the finals, and walked away with the college championship title and the tidy sum of $100,000.

The making of a “Jeopardy!” champion

Chin was one of thousands of students from schools around the United States who applied to be contestants on the show. Of those, 250 were invited to in-person auditions in New York City in November, which consisted of a written test, gameplay, and an interview. In December, Chin learned she’d made the cut — a total of 15 students and one alternate — and the show was taped in Jan. 10-11 in Los Angeles. Sworn to secrecy for several weeks after that, Chin was able to savor the victory in late February at a final episode screening held in Room 4-237, where she was cheered on by dozens of friends and other fans from the MIT community.

Chin, an electrical engineering and computer science major with a minor in mechanical engineering, credits part of her success to her curiosity about media, which led her to also minor in comparative media studies. She loves “investigating different forms of media, whether it’s film, video games, or children’s literature — [it’s] the same curiosity which leads me to seek out factoids about these media, and which tend to get asked about more on ‘Jeopardy!’”

EECS senior Lilly Chin, the newly crowned “Jeopardy!” College Champion, with program host Alex Trebek

EECS SENIOR WINS ‘JEOPARDY’ COLLEGE CHAMPIONSHIP Lilly Chin takes the $100,000 grand prize, surpassing 14 on-air contestants and thousands of applicants from colleges around the United States.

By Elizabeth Durant | Office of the Dean for Undergraduate Education Photo: Jeopardy Productions Inc.


A native of Decatur, Georgia, Chin is no stranger to trivia competitions; she participated in quiz bowls from fifth grade through high school. She prepared for “Jeopardy!” in a myriad of ways, such as reviewing her old trivia books, reading web comics, listening to Top 40 music, and generally spending lots of time “goofing off on the Internet.” She found creative ways to bolster her knowledge of subjects she didn’t know well; to address a weakness in history, she crammed The Cartoon History of the Modern World.

Chin enlisted the help of MIT friends to study and practice her gameplay, including playing Protobowl, a real-time, multi-player quiz bowl application created by her classmate, senior Kevin Kwok. She also sought advice from two MIT connections who had “Jeopardy!” experience: her former graduate resident tutor, Philip Arevalo (who motivated her to apply for the show), and Pranjal Vachaspati ’14.

Preparation aside, Chin also had a few tricks up her sleeve. One of them was buzzer strategy. “I think the game is actually more about buzzer strategy than trivia,” she says. Timing is everything: buzz too soon, before show host Alex Trebek finishes reading the clue, and your buzzer will get locked out for a fraction of a second — enough time for an opponent to buzz in. The key is to time it precisely when Trebek is done speaking.

Her board strategy paid off, too. In the more conventional approach, contestants work their way through one category, moving from lower-value clues at the top of the board to higher-value clues at the bottom. Others, like Chin, prefer to jump between categories and choose clues further down the board. “It’s a bit of a controversial strategy,” she says. But the advantage is that skipping around the board can throw off your opponents and increase your odds of finding the clue with the Daily Double. “The Daily Doubles aren’t evenly distributed,” Chin explains. “People have run stats and found they tend to be in the fourth row or so.”

Being on the show was “surreal,” Chin recalls, smiling broadly. “There was a moment when all the contestants realized that this was actually happening. After the game, everyone’s hands were shaking.” To combat her own nerves, she channeled her experience on the trap shooting team (part of the MIT Sporting Clays Association), in which players shoot moving clay targets with a shotgun. “The coach is always like, ‘Don’t keep track of the score, just take it one shot at a time,’’ she says, “because especially for shooting, any sport, you need to be calm. As soon as you start thinking, ‘Oh no, what-ifs,’ then your game gets off and you miss everything. So I think that really helped.”

“Nerd pride”

Throughout the course of the two-week tournament, as word spread about her progress, Chin developed quite a following on the MIT campus. “The best part of being on the show has definitely been the great outpouring of support the MIT community has given me,” she says. “At first, I was a bit embarrassed about being on national television and tried to keep the whole thing under wraps. But soon, I found that the more people that I told, the more I found that people were eager to help and support me.”

President L. Rafael Reif, Chancellor Cynthia Barnhart, and Vice President and Dean for Student Life Suzy Nelson were among those rooting for her. “If the clue is ‘Nerd Pride,’ the answer must be, ‘What was our overwhelming reaction when we learned that Lilly Chin just won College Jeopardy?’” Reif wrote in an email to Chin. “Even better, for this longtime professor: You’re not just MIT, you’re EECS! Lilly, I hope you have a moment to savor this terrific achievement.”

“[Provost] Marty Schmidt and I have decided you are the Tom Brady of ‘Jeopardy’ — great job!” Barnhart said in an email to Chin, following Chin’s second-to-last appearance on the show. Nelson wrote Chin after her strong showing in the first week: “I’m so proud of your Jeopardy performance…Plus, love your strategy of finding those Daily Doubles — bold and fearless.”

Chin developed a large fan base among MIT students, who found creative ways to show their support, throwing screening parties and sharing their favorite moments on social media. One friend, Shi-Ke Xue ’16, created a series of GIFs of Chin on the show and shared them on Reddit.

Looking back, and ahead

In retrospect, Chin admits she feels a bit bad about taking the “Jeopardy!” online test during class back in October, adding, “That is one of my favorite classes.” Luckily, her professor — T.L. Taylor, professor of comparative media studies, who also followed Chin’s progress on “Jeopardy!” and is now privy to Chin’s secret about the test — loves the anecdote. “How very apropos,” she says, “considering it was a class on games and culture!”

Chin, who plans to begin a PhD program in robotics after graduation, says she’ll use the prize money to pay off college loan debt and to travel to a few research conferences around the world on video game studies — a form of media that continues to pique her boundless curiosity.

“ The best part of being on the show has definitely been the great outpouring of support the MIT community has given me.”

—Lilly Chin, EECS senior and “Jeopardy!” champion


If you daydream about founding a startup, know this: CEOsare made, not born. Theodora Koullias ’13 — founder of the

tech-fashion company Jon Luu — summed it up this way: “You learn on the job all the time.”

Koullias candidly shared her experience with students and postdocs during StartMIT 2017. The short course, which is packed with practical instruction and mentorship, is designed to give aspiring entrepreneurs a boost up the founder learning curve. Held during MIT’s Independent Activities Period (IAP) in January, StartMIT gave participants a chance to form teams and develop their ideas into venture capital-worthy pitches. Students learn about the smorgasbord of ingredients that go into making a startup: creating a value proposition, staking a claim to intellectual property, working with the press, networking, creating culture, and, of course, raising money.

“StartMIT featured some amazing speakers who engaged actively with our students on all aspects of starting a company, giving them a glimpse of what an entrepreneurship career is,” said StartMIT lead organizer Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science (EECS) and EECS department head. The course was developed by EECS and supported by the MIT Innovation Initiative.

Scientists and engineers often find themselves wanting to turn their research into a product, said Institute Professor Robert Langer, an invited speaker. “We want to see it get out to the world and help people.”

But founders need grit, said Langer, who holds more than 1,000 pending and issued patents. In his view, the defining characteristic of students who have become successful business leaders is their willingness “to walk through walls to get their technology out into the world.”

Ray Stata ’57, cofounder of Analog Devices, Inc. and leader in the design and manufacture of analog and digital signal processing semiconductors, didn’t sugar-coat the entrepreneur’s brand of determination. “When you start a company, there is no work-life balance,” he told students. “You continue to drink from the fire hose not only because you have to, but because you are so committed and motivated to succeed.”

Wen Jie Ong, a PhD candidate in chemistry, is determined to get his innovation to the public because he sees his technology’s relevance. He’s been developing a polymer that removes lead from contaminated water, making it safe to drink. To underscore the need, Ong pointed not only to the recent water crisis in Flint, Mich. — in which the city’s 100,000

As part of StartMIT, Dropbox CEO Drew Houston ’05 (seated, front left) visited campus to meet student innovators and discuss his latest projects.

MAKE IT YOUR BUSINESSStartMIT, a boot camp on entrepreneurship, gives students an intimate look into what it takes to build a company.

By Alison F. Takemura | EECSPhoto: Rose Lincoln


residents have had to grapple with lead, a neurotoxin, in their drinking water — but also to examples that are closer to home. For example, as of November 2016, drinking water at 164 public schools in Massachusetts had lead levels above regulatory limits.

StartMIT, Ong said, “is a large time commitment, but it’s totally worth it.” The course exposed him to new ideas through its star speakers, and put him in touch with mentors, including venture capitalists, who gave him “very frank” advice, he added. He couldn’t have gotten that anywhere else.

Direct feedback is priceless, said Susan Hockfield, who served as president of MIT from 2004 to 2012. She told the class that, at her very first poster presentation as a graduate student, she hadn’t realized people would actually want to talk with her about her work. So, unprepared, she rambled. Afterward, she spoke to her advisor standing nearby, telling him: “I really felt stupid over there.” His response? “Yeah, you sounded pretty stupid.”

Hockfield appreciated that unfettered honesty. She encouraged the audience to “find someone who’s willing to tell you as it really is.”

Lyric Jain hopes to make the media that people consume every day into a similarly unbiased resource. “Polarization in the media is a big problem,” the Cambridge University-MIT exchange student in mechanical engineering said during one of the course’s networking lunches.

To broaden readers’ perspectives, Jain is working on a web platform that delivers news from across the liberal-conservative political spectrum of media outlets, from MSNBC to Fox News. In addition, his technology is designed to automatically prune the stories to their facts, lining them up for readers as different sides of a debate.

During StartMIT, Jain’s project was still in its early stages, and he started the course with his guard up. “Initially, I was quite suspicious that if I talk to someone, they’re going to steal my idea,” he said. “But now I know the idea is only one small part of it. What matters is how you build on the idea, put your twist on it, and build a team around it.”

Besides, there are pluses to being open, he added: “Talking to people about your idea, you’re going to get input to make it better. They might even be a potential customer.”

Rabia Yazicigil, a postdoctoral associate in EECS, has noted the risk posed by devices that communicate with each other — components of the so-called Internet of things (IoT). A hacked pacemaker, for example, could be a potential murder weapon. To prevent that gruesome possibility, Yazicigil is developing a new kind of secure wireless communications system for IoT devices.

Before StartMIT, Yazicigil had some reservations about starting a company. Hearing from scientists who became business leaders — including Hockfield, Langer, Stata, and Michael Stonebraker, adjunct professor of computer science at MIT and co-director of the Intel Science and Technology Center — helped Yazicigil see a path for herself.

“I want to stay in academia more than become an entrepreneur,” Yazicigil said. “But I see how they’re able to still do both at the same time.”

Dozens of other pioneers also shared their experiences during the course, including emphasizing the importance of communicating, building trust with funders, and assembling an excellent team.

In addition, StartMIT allowed students to explore the entrepreneurial ecosystem beyond MIT. Students took field trips to Ministry of Supply, an innovative fashion company founded by MIT alumni; venture capital firms Pillar VC, Bolt, and Project 11 Ventures; the non-profit startup accelerator MassChallenge; and the Cambridge Innovation Center, which houses several MIT startups. The Institute also has a wealth of opportunities to support student ventures. Several teams will be using MIT’s Venture Mentoring Service. Some have applied to MIT’s Sandbox Innovation Fund, an Institute-wide program providing support, mentor matching, and funding to help qualified students and teams nurture their creative brainstorms.

Interpersonal connections represent a big part of what makes StartMIT so useful, said Anna Fountain, a senior in mechanical engineering. “It makes me feel a lot more comfortable knowing that there are people who can help us and who have done this before, right here in the greater MIT community.”

For more about StartMIT, visit startmit.mit.edu

StartMIT lead organizer and EECS department head Anantha Chandrakasan and former MIT President Susan Hockfield discuss leadership.


Innovators in materials chemistry, online care, naturallanguage processing, social robotics, and venture capital

shared their founders’ journeys in a lively panel discussion during StartMIT.

Led by moderator Kym McNicholas, an Emmy Award-winning anchor/reporter/producer and entrepreneur, the cast included MIT faculty members Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science (EECS); Angela Belcher, the James Mason Crafts Professor in the Department of Materials Science and Engineering and the Department of Biological Engineering; and Cynthia Breazeal, associate professor of media arts and sciences. Also participating were Donna Levin, co-founder of Care.com, entrepreneur-in-residence at the Martin Trust Center for Entrepreneurship, and lecturer at MIT Sloan School of Management; and Katie Rae, then founder and general partner at Project 11 Ventures (later named president and CEO of The Engine, MIT’s new startup accelerator).

Common themes of passion and timing emerged throughout the conversation as McNicholas asked the panel about topics related to risk, research, inspiration, and failure.

For Belcher, risk wasn’t a factor when she decided to direct her career into the idea of genetically programming organisms to grow into electronics and batteries — a proposal that was met with much skepticism when she was a young professor getting her start. “I didn’t think of it as risky at the time.

I thought ‘this is the only thing that I want to do,’” she recalled. Since then, she’s applied her accomplishments to other fields she’s never explored before, such as cancer. “You’re going to run into obstacles along the way, but you learn these aren’t failures; these are learning experiences,” she said. “Not everyone’s going to agree with you. If more people don’t agree, it’s probably a better idea. You keep going.”

Like Belcher, Barzilay followed her passion by taking her core research focus of natural learning processing in a new direction after undergoing treatment for breast cancer, when as a patient, she was surprised to learn that data science and machine learning weren’t used in cancer care in the United States. “The technology used on Amazon to recommend products was much more sophisticated than what we have today in cancer care,” she said. Barzilay set about on a new journey: in collaboration with researchers at Massachusetts General Hospital, Barzilay and her team built a system that takes breast pathology and automates the data analysis in a new way with a high level of accuracy. The team is using deep learning to analyze mammogram readings with the goal of using this data to make predictions which humans currently cannot do.

Shifting the focus to timing, McNicholas turned to Breazeal, a pioneer in social robotics and the founder and chief scientist of Jibo, which recently introduced the world’s first social robot for the home. When asked about the moment the research went from the lab to commercialized product, Breazeal said:

As part of StartMIT, six distinguished women discussed their experiences with innovation — and shared their advice with next-generation entrepreneurs.

STARTMIT’S INNOVATION NIGHTWomen entrepreneurs from a range of fields discuss their pioneering work and what they learned along the way.

By Terri Park | MIT Innovation Initiative Photos: Rose Lincoln


“I didn’t know when I was starting this work that I would be an entrepreneur. But over time, watching technology, watching the cloud-computing revolution, watching the mobile-computing revolution, [I started] thinking the elements are coming together. All of these really hard subfields were starting to get to the level that you could start building on to create this new kind of medium. When you think about a social robot, it’s a new kind of medium for social communication and interaction.” Bottom line: “Now was the time to jump in,” she said. “Timing is everything.”

McNicholas asked Levin, the cofounder of Care.com, how to know when the time is right. “You don’t want to be too early,” Levin replied. “For us, the technology existed to find people online and make matches, but it was a highly fragmented market and therefore an opportunity.” Care.com is now the world’s leading online site for helping people find and manage family care, but when Levin and her partners started pitching the idea, many people questioned its viability. “Who would ever go online to find care for their loved one?” Levin asked rhetorically. “We decided to do something about that. Every member of the team believes he or she is going to change the world. It’s hard for others to compete with you if you believe you’re going to change the world.”

Sharing the perspective from the venture capital side, Rae spoke about taking the leap again and again. One early leap involved the decision to leave her job at Microsoft to start her own company and pursue investing in early-stage technology and software companies, where she worked side-by-side with founders to increase their rate of success. “I always thought of myself as in service to the idea of the entrepreneurial team, and that has led me all along the way,” she said. “Being an early-stage investor, my role is often to say to the founders, ‘You’re onto something amazing. Do you see the progress you made? Have you met this awesome person?’ My role is to keep that inspiration alive. I’m there to suggest, to present opportunities and collisions.”

On the topic of failure, Levin said: “It’s not ‘if’ you fail; of course you are going to fail. It always feels like failure until it’s a success. The important thing is to keep going.” To which Breazeal added: “I think resiliency to failure is important. I don’t view failures as failures. I really do view them as something that helps make me smarter. You also have to learn to distinguish thoughtful critique, which is so valuable, from just ‘squashing.’ You have to trust your gut. It’s okay not to know how. You’ll figure it out.”

McNicholas asked the panelists about the greatest lessons they’ve learned. “The process I am privileged to observe is taking things that are impossible to do and translating them into the real product that impacts people’s lives,” Barzilay replied. “It’s important to find problems which impact the bigger world, and at MIT, we’re really privileged to have this capacity.”

In closing, McNicholas advised the audience of aspiring entrepreneurs to “never let what you don’t know or have never done before get in the way of achieving your dreams.”

Innovation Night was the capstone event of StartMIT, an intensive Independent Activities Period (IAP) workshop that

exposed students to the basics of entrepreneurship. During the two-week program, undergraduates, graduate students, and postdocs heard about entrepreneurship from a variety of viewpoints.

Before the discussion, guests mingled with alumni entrepreneurs and innovators during a Startup Showcase and learned about early-stage ventures from MIT. In addition, EECS department head Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science, welcomed the audience and introduced The Engine, which focuses on providing support for the biggest and most transformative technology-based ideas that require more time to commercialize.

Joi Ito, director of the MIT Media Lab, described the innovative “antidisciplinary” research happening at the lab. A longtime entrepreneur and venture capitalist, Ito spoke of the lab’s four-pronged approach to learning: projects, peers, passion, play. “This is a lot of the spirit of MIT, and it’s the spirit of entrepreneurship,” he said. “The best startups have all four of these, and a lot of what we’re doing at the Media Lab is instilling the values that we need in entrepreneurship so that we will hopefully spin out many entrepreneurs.”

Developed by the Department of Electrical Engineering and Computer Science, StartMIT is supported by the MIT Innovation Initiative and chaired by Chandrakasan.

Front row, left to right: Angela Belcher, Kym McNicholas, Katie Rae Back row, left to right: Joi Ito, Regina Barzilay, Anantha Chandrakasan, Cynthia Breazeal, Donna Levin


For some students and postdocs, the StartMIT experience extended well beyond Cambridge and Boston — about 3,000

miles beyond.

In late March, 25 StartMIT participants traveled to California for an intensive two-day entrepreneurship program in San Francisco and Silicon Valley. They visited established high- tech companies, startups, and venture-capital firms, including some founded or led by MIT alumni, as well as a three-year- old nonprofit organization dedicated to supporting both current and future entrepreneurs. They even stopped by the historic HP Garage, the one-car structure in Palo Alto where William Hewlett and David Packard launched their famous company.

Several students said the expenses-paid trip provided an important complement to the StartMIT workshop held during MIT’s Independent Activities Period (IAP) in January. “The IAP class gave me a chance to start looking at possible startup paths. The trip was a good incentive to continue,” said Oscar Moll, a PhD student in EECS. “Understanding the Bay Area ecosystem is important for anyone considering startups as a career path.”

Participants appreciated the chance to interact directly with executives and entrepreneurs, especially those with MIT connections. One such opportunity came during a reception hosted by MIT alumnus Michael Cassidy (SB ’85, SM ’86,

Nicola Corzine, executive director of the Nasdaq Entrepreneurial Center in San Francisco, spoke with StartMIT participants during their intensive two-day trip.

ENTREPRENEURSHIP IN ACTION — ON TWO COASTSStartMIT participants see innovation up close during whirlwind tour of Bay Area business ecosystem.

By Anne Stuart | EECS

Photo: Mary Ellen Sinkus


aerospace engineering), a serial entrepreneur, Google vice president, and project leader on GoogleX Project Loon (“balloon-powered Internet for everyone”). More than 200 people attended the event and heard StartMIT participants present their ideas. “One important takeaway for me was how willing alumni are to listen to pitches and offer feedback,” Moll said.

For junior Ihssan Tiwani, a group session with Dropbox co-founder Drew Houston ’05 was a trip highlight. “It was so casual that it felt like we were getting candid advice from an upperclassman at MIT who really cared about our success and understood the experiences we were going through as MIT students,” said Tiwani, who is double-majoring in computer science and engineering (6-3) and economics.

Tiwani also found value in reconnecting with IAP classmates during the trip. “It was nice to get updates on the latest things they are working on and the problems they are facing in launching their new ventures,” he said. “I think it also solidified our friendship, making us feel like we are part of a community of entrepreneurs at MIT.”

In San Francisco, in addition to Dropbox, the group visited Cisco Meraki, CodeFights, Lemnos Labs, the Nasdaq Entrepreneurial Center, and Thunkable. In Silicon Valley, participants visited Andreessen Horowitz, Apple, GoDaddy, and Lightspeed Venture Partners. Accompanying the group were StartMIT lead organizer Anantha Chandrakasan, EECS department head and

Vannevar Bush Professor of Electrical Engineering and Computer Science; EECS Administrative Officer Mary Ellen Sinkus; and EECS Program Coordinator Myung-Hee Vabulas. Also on the trip was Jinane Abounadi, executive director of MIT’s Sandbox Innovation Fund Program, which provides seed funding, mentoring, and other support for student-initiated ventures.

Melody Cao, a graduate student in EECS, said she would highly recommend the trip to future StartMIT participants, adding: “You will get a ton of first-hand insight from stepping out of the academic bubble” and into the entrepreneurial ecosystem. Tiwani agreed. “It’s a free trip to the Bay Area in which you meet some of the most accomplished and smartest MIT alums,” he said. “How could anyone possibly say ‘no’ to that?”

The StartMIT group also visited Cisco Meraki, a cloud networking company, along with several other major technology companies in San Francisco and Silicon Valley.

“ The IAP class gave me a chance to start looking at possible startup paths. The trip was a good incentive to continue. Understanding the Bay Area ecosystem is important for anyone considering startups.”

—Oscar Moll, PhD Candidate, EECS


In April 2017, MIT’s new startup accelerator The Engine closed its first investment fund for more than $150 million.

That sum will support startups developing breakthrough scientific and technological innovations with potential for societal impact.

That was just the latest milestone for The Engine, which combines an accelerator, an open network of technical facilities, and a fund, which together will provide startups with stable financial support and access to costly resources. The initiative will focus on startups developing “tough” technologies — breakthrough ideas that require time to commercialize — in sectors such as robotics, manufacturing, health technology, biotechnology, and energy.

“From the beginning, our vision for The Engine has been to foster the success of ‘tough-tech’ startups with great potential for positive impact for humanity,” MIT President L. Rafael Reif said in April. “By enabling crucial investments in The Engine’s first portfolio of companies, the funds announced today will also strengthen the local innovation ecosystem and the regional economy.”

Of the total capital raised for the fund — officially named The Engine Accelerator Fund I, L.P. — MIT invested $25 million. The remainder came from a small group of investors aligned with the fund’s mission.

Members of The Engine’s Board of Directors and Investment Advisory Committee at The Engine headquarters in Central Square (from left to right): Katie Rae, Robert Kraft, Israel Ruiz, Anantha Chandrakasan, Linda Pizzuti Henry, Amir Nashat, Sue Siegel, David Fialkow, Jeremy Wertheimer, Brad Powell, Felipe Chico, and Jonathan Kraft

THE ENGINE: UP AND RUNNING With funding secured and leadership in place, MIT’s new accelerator is now focusing on selecting its first investments.

Photo: Courtesy of The Engine


The Engine was unveiled at a high-profile launch event in October 2016. At that time, Reif described the underlying reasons for the initiative: “If we hope for serious solutions to the world’s great challenges, we need to make sure the innovators working on those problems see a realistic pathway to the marketplace,” he said. “The Engine can provide that pathway by prioritizing breakthrough ideas over early profit, helping to shorten the time it takes these startups to become ‘VC-ready,’ providing comprehensive support in the meantime, and creating an enthusiastic community of inventors and supporters who share a focus on making a better world.”

In February 2017, The Engine named Katie Rae, a veteran technology entrepreneur and investor, as its president and CEO and as managing partner of its first investment fund. The Engine also announced membership of its board of directors and investment advisory committee.

Among The Engine’s inaugural board members is Anantha Chandrakasan, head of the Department of Electrical Engineering and Computer Science (EECS) and the Vannevar Bush Professor of Electrical Engineering and Computer Science. Chandrakasan also headed up MIT’s Engine Working Groups, which consisted of faculty, postdocs, students, and staff with specialized expertise. The groups were charged with helping guide development of Engine-related policies and procedures in areas such as technology licensing and facilities access, among others.

Closing The Engine’s first fund so soon after its public announcement shows great promise, noted Rae, who previously served as managing director of the popular startup accelerator Techstars Boston. “There is strong interest, and people are bullish on what’s coming out of MIT and Boston. We’re looking for startups with breakout technologies and great founding teams that want to build their companies in the New England region,” she said. With funding secured and leadership established, The Engine is now focusing on selecting its first group of investments.

For more about The Engine, visit engine.xyz

“ If we hope for serious solutions to the world’s great challenges, we need to make sure innovators working on those prob-lems see a realistic path-way to the marketplace.”

—MIT President L. Rafael Reif

MIT’s Department of Electrical Engineering and Computer Science (EECS) unveiled a new undergraduate curriculum for 2016–17, and the first year has gone very smoothly, says EECS Undergraduate Officer Chris Terman.

The new degree requirements, which apply to students in the Class of 2020 and beyond, are designed to:

• enable majors to engage earlier with core EECS materialby cutting back the introductory requirement

• serve students with a broader range of backgrounds bymaking a smoother introduction to software

• allow more flexibility within the curriculum

• sharpen the specification of Laboratory and AdvancedUndergraduate Subjects requirements, and

• improve the major-project experience for students andfaculty.

“The new curriculum puts more choice in students’ hands, while providing a solid grounding in the essential elements of an education in electrical engineering and computer science,” EECS department head Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science, said in announcing the change last year.

Students in the classes of 2017, 2018, and 2019 could choose to continue using the old requirements or switch to the new requirements in fall 2016. Of the 1,580 undergraduate majors and master’s of engineering (MEng) students in the department’s database, 572 have chosen the new program, and 1,008 have remained in the old program, Terman says: “Upperclassmen are allowed to switch, but, of course, seniors and MEng students who are close to finishing under the old program would probably not find switching to their advantage.”

Key changes to the curriculum include reducing the number of introductory subjects and math foundation courses from two each to one each. The new program also adds two elective subjects to the 6-1 (Electrical Science and Engineering) and 6-2 (Electrical Engineering and Computer Science) majors, and one elective subject to the 6-3 (Computer Science and Engineering) major. The 6-7 (Computer Science and Molecular Biology) major requirements were revised to refer to the next generation of software and biology subjects, but the overall scope of the 6-7 major is unchanged, Terman says.

Going forward, two department committees — one for electrical engineering and one for computer science — are considering additional new subjects at the foundation and header levels. “I think we’ll see these courses start to appear in the coming academic year,” Terman says.

For more information on the new undergraduate curriculum, visit eecs.mit.edu/curriculum2016

EECS Introduces New, More Flexible, Undergraduate Curriculum


More than 60 undergraduates spoke or gave posterpresentations during EECScon, the annual undergraduate

research conference sponsored by the Department of Electrical Engineering and Computer Science (EECS) and MIT Lincoln Labs. About 45 students presented posters or demonstrations during Masterworks, the annual EECS celebration of thesis research leading to the master of science (SM) and master of engineering (MEng) degrees.

Dozens of other students, faculty members, and industry guests joined the back-to-back events on April 18 to learn about students’ work (and enjoy a free lunch during EECScon and an ice-cream buffet during Masterworks). Both events featured prizes for the best presentations. EECScon winners received cash prizes of $50 to $200, while Masterworks winners received prizes donated from Apple and Samsung.

For more photos and a full list of winners, visit eecs.mit.edu

MASTER-WORKS AND EECSCON: SHOWCASING STUDENTS’ WORKApril 18, 2017, was a day to talk, think, and learn about student research.

Photos: Gretchen Ertl


RESEARCHUPDATES

Michael Carbin: Verifying Application-Specific Fault Tolerance via First-Class Fault Models 26

Stefanie Mueller: Interacting with Personal Fabrication Machines 29

Devavrat Shah: Social Data Processing 32

Max Shulaker: Next-Generation Nanosystems Q & A 35

26 research CONNECTOR 2017 eecs.mit.edu

Due to the aggressive scaling of technology sizes in moderncomputer processor fabrication, modern processors have

become more vulnerable to errors that result from natural variations in processor manufacturing, natural variations in transistor reliability as processors physically age over time, and natural variations in these processors’ operating environments (e.g., temperature variation and cosmic/environmental radiation).

Large distributed systems composed of these processors — such as emerging designs for exascale supercomputers — are anticipated to encounter errors frequently enough that traditional techniques for building high-reliability applications will be too resource-intensive (in terms of time, storage, and energy consumption) to be practical. Applications will, instead, need to be architected to execute through errors[1].

Traditional Fault Tolerance

Researchers have long sought methods to enable reliable computation on unreliable computing substrates. For example, in the 1950s, vacuum-tube-based computing systems experienced vacuum-tube failures as frequently as every 8 hours[2]. In response, the industrial and academic community sought to resolve this issue both by designing more reliable computing substrates (modern CMOS transistors) and by designing fault-tolerance mechanisms. Of these latter techniques, popular methods include:

1) n-modular redundancy to implement majority voting(where n > 2),

2) dual-modular (2-modular) redundancy (DMR) toenable error detection and subsequent restart, and

3) algorithm-based fault-tolerance methods to provide

low-overhead application-specific detection and correct schemes.

A major aspect of the design of such mechanisms is the trade-off between the overhead (in performance, memory consumption, and energy consumption) of these techniques, the frequency and distribution of hardware faults,

and the coverage of a specific error-detection and correction scheme. For example, standard methods for DMR duplicate the entire execution of a computation and check if the two executions of the computation agree on their results. This technique introduces significant computational and energy overhead.

VERIFYING APPLICATION-SPECIFIC FAULT TOLERANCE VIA FIRST-CLASS FAULT MODELSBy Michael Carbin, Jamieson Career Development Assistant Professor of Electrical Engineering and Computer Science; Member, Computer Science and Artificial Intelligence Laboratory (CSAIL)

In contrast, algorithm-based fault-tolerance techniques — such as those for linear algebra — produce lightweight checksums that can be used to validate whether the computation produced the correct results. For some applications, these checksums are exact, enabling the exact error detection of capabilities of DMR with lower overhead. However, for other applications, these checksums either are not known to exist or, at best, compromise on their error coverage.

Application-Specific Fault Tolerance

Modern large computing systems have begun to operate a point in the trade-off space between performance/energy and error rates that traditional, application-oblivious fault-tolerance techniques are too resource-intensive to deploy at scale for large numerical computation. In response, researchers have begun to expand on historical results for algorithm-based fault tolerance — alternatively, application-specific fault tolerance — to identify new opportunities for low-overhead mechanisms that can steer an application’s execution to produce acceptable results: that is, results that are within some tolerance of the result expected from a fully reliable execution.

Figure 1: Reliable versus Unreliable Execution of Jacobi Iterative Method for Oil

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eecs.mit.edu 2017 CONNECTOR research 27

Figure 2: Leto Fault Model Specification

Such techniques include selective n-modular redundancy in which a developer either manually or with the support of a fault-injection tool identifies instructions or regions of code that do not need to be protected for the application to produce an acceptable result, as determined by an empirical evaluation. Another class of techniques are fault-tolerant algorithms that, through the addition of algorithm-specific checking and correction code, are tolerant to faults.

For example, Figure 1 shows the results of executing a self-stabilizing iterative solver (Jacobi iterative method) for a system of linear equations that corresponds to an oil reservoir simulation problem. The graph presents the norm of the error of the solution vector (y-axis) as a function of the number of iterations (x-axis) for a reliable execution (in blue) and an unreliable execution (in red) that encounters a fault on iteration 10,000. The unreliable execution eventually converges to the correct solution. Moreover, it recovers quickly relative to the algorithm’s overall convergence from a solution vector of similar error (as evidenced by previous iterations).

While these techniques and algorithms are promising, a major barrier to implementing either of these techniques is that their results either rely on empirical guarantees or — for algorithm-specific techniques — hinge on the assumption that the fault model of the underlying computing substrate matches the modeling assumptions of the algorithmic formalization.

Verifying Application-Specific Fault Tolerance with Leto

In our recent research, Brett Boston and I have developed Leto[3], a verification system that supports reasoning about unreliably executed programs. Leto enables a developer to build confidence in an application-specific fault-tolerance mechanism by 1) enabling a developer to programmatically specify the behavior of the computing substrate’s fault model and 2) enabling a developer to verify relational assertions that relate the behavior of the unreliably executed program to that of a reliable execution.

Leto enables a developer to specify the behavior of the underlying hardware system as a program that Leto automatically weaves into the execution of the main program. In addition, Leto enables a developer to specify relational assertions that, for example, constrain the difference in results of the unreliable execution of the program from that of its reliable execution.

First-Class Fault Models: Leto enables a developer to programmatically specify a stateful fault model. For example, a common fault model that application developers use is the single-event-upset model. In this model, at most one fault can occur during the execution of the program. While simple, this model can capture real fault models in which it is possible for errors to happen during execution, but with small probability.

Figure 2 presents a specification in Leto of a single-event upset model that affects only addition operations. This model includes a boolean valued state variable that records whether a fault has occurred during the execution of the program. The model then exports two versions of the addition operator. Line 4 specifies the reliable implementation of addition, while Line 6 specifies an unreliable version. For each addition operation

in a program, Leto dynamically makes a non-deterministic choice between the set of operation implementations that are currently enabled in the model as indicated by their guards evaluating to true. Namely, an operation’s guard is the optionally specified boolean expression that occurs after the when keyword. For these two versions of addition, the reliable version is always enabled and the unreliable version is enabled only if upset — indicating that a fault has yet to occur in the program.

Relational Verification: Verifying an application that has been protected with an application-specific fault-tolerance mechanism typically requires reasoning about two types of properties of the resulting application: safety properties and accuracy properties.

Safety properties are standard properties of the execution of the application that must be true of a single execution of the application. Such properties include, for example, memory safety and the assertion that the application returns results that are within some range. For example, a computation that computes a distance metric must, regardless of the accuracy of its results, return a value that is non-negative. In Leto, a developer specifies safety properties with the standard assertion statement, assert e, as typically seen in verification systems. For example, to assert that the result of a distance metric is non-negative, a developer may write in the program the statement assert 0 <= x, where x is the result of the metric.

Accuracy properties are properties of the unreliable or relaxed execution of the application that relate its behavior and results to that of a reliably executed version. Accuracy properties are relational in that they relate values of the state of the program between its two semantic interpretations. For example, the assertion -epsilon < x<o> - x<r> < epsilon in Leto specifies that the difference in value of x between the program’s reliable execution (denoted by x<o>) and relaxed execution (denoted by x<r>) is at most epsilon.

Key Insights: Leto relies on an Asymmetric Relational Hoare Logic[4] as its core program logic. Relational Hoare Logics are a variant of the standard Hoare Logic that natively refer to the values of variables between two executions of the program. Leto’s use of a relational program logic serves two goals: 1) it gives a semantics to accuracy properties and 2) it enables tractable verification of safety properties.

As an example of the latter, proving the memory safety of an application outright can be challenging for many applications. However, application-specific fault-tolerance mechanisms can typically be designed and deployed such that it is possible to verify that for any given array access or memory access, errors in the application do not interfere with the accessed address. Such properties are typically easier to verify for a protected


application than verifying the safety of the memory access outright.

Leto therefore enables developers to tractably verify a strong relative safety guarantee: if the original application satisfies all of the specified safety properties, then relaxed executions of the application with its deployed application-specific fault-tolerance mechanisms also satisfy these safety properties.

Results: We have used Leto to verify key correctness properties on several self-stabilizing algorithms: Jacobi, Self-stabilizing Conjugate Gradient, and Self-stabilizing Steepest Descent. Our results show that is possible to verify the key invariants required to prove that these algorithms’ self-stability guarantees hold for their implementations. In general, Leto enables developers to specify and verify the rich fault-aware properties seen in applications with application-specific fault-tolerance mechanisms.

Future Approximate Computing Systems

As we continue to scale the size of our systems to include larger collections of increasingly less reliable components, our methods for architecting software systems will need scalable and verifiable methods to manage the uncertainty in the underlying execution substrates of the systems. Leto, along with other work in my Programming Systems Group[5, 6, 7], directly provides new programming languages, methodologies, and systems that enable developers to reason about unreliable, continuous, and probabilistic computation.

References

[1] S. Amarasinghe, et al. ExaScale Software Study: Software Challenges in Extreme Scale Systems. DARPA IPTO, Air Force Research Labs, Technical Report (2009).

[2] J. Von Neumann. Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components. Automata Studies, 35 (1956).

[3] B. Boston and M. Carbin. Verifying Application-Specific Fault Tolerance via First-Class Models. In submission.

[4] M. Carbin, D. Kim, S. Misailovic, and M. Rinard. Proving Acceptability Properties of Relaxed Nondeterminisic Approximate Programs. PLDI (2012).

[5] E. Atkinson and M. Carbin. Towards Correct-by-Construction Probabilistic Inference. NIPS Workshop on Machine Learning Systems (2016).

[6] E. Atkinson and M. Carbin. Towards Typesafety to Explicitly-Coded Probabilistic Inference Procedures. In submission.

[7] B. Sherman, L. Sciarappa, M. Carbin, and A. Chlipala. Overlapping Pattern Matching for Programming with Continuous Functions. In preparation.


Personal fabrication tools, such as 3D printers, are on theway to enabling a future in which non-technical users will

be able to create custom objects. With the recent drop in price for 3D printing hardware, these tools are about to enter the mass market: While the average consumer 3D printer was priced at $14,000 in 2007, today’s hardware costs, on average, only $500[10]. Given the decreasing price, it is not surprising that the number of consumer 3D printers sold has doubled every year[10].

While the hardware is now affordable and the number of people who own a 3D printer is increasing, only a few users actually create new 3D models. Most download models from a platform, such as Thingiverse, and fabricate them on their 3D printers. At most, users adjust a few parameters of the model, such as changing its color or browsing between predetermined shape options.

I believe that personal fabrication has the potential for more: I envision a future in which, rather than just consuming existing content, 3D printers will allow non-technical users to create objects that only trained experts can create today. While there are many open challenges, I will use this article to discuss how we can improve the interaction model underlying current fabrication devices.

1.1 Interaction Model with 3D Printers Today

In the current interaction model, users sit at a computer and use a digital 3D editor to create a digital 3D model. Only at the end of the design process do users send the file to the 3D printer, which creates the object in one go. Because 3D printing is slow, this process tends to take hours of printing time for small objects and may even require overnight printing.

1.2 Drawing a Parallel to Personal Computing

Looking back in history, this interaction model with the delayed feedback was also common with early computers[1]. In the early ’60s, computers were so slow that the average program had to be executed overnight. Feedback was delayed until the next morning and if a program failed, users had to repeat the entire process, potentially waiting another night for results.

Similar to 3D printers today, early computers were limited to expert users because when programs were executed in one go overnight, users had to know what they were doing to succeed.

1.3 Towards Turn-Taking and Direct Manipulation

However, today we are at a point at which even non-technical users can use personal computers. Beside many technical developments, two advances in the interaction model enabled this: (1) the move from executing in one go to turn-taking, and (2) the move from turn-taking to direct manipulation[3].

1) Turn-taking: By decreasing the interaction unit to singlerequests, turn-taking systems, such as the command line, provided users with feedback after every input. This enabled the trial-and-error process that non-technical users tend to employ: quickly iterating through potential solutions and building each step onto the results of previous ones[2]. However, while the turn-taking interaction model provided a great step forward to making the technology available for non-technical users, the feedback cycle was still limited in that it consisted of two discrete steps: users first had to create an input and only afterwards received feedback.

2) Direct manipulation: With the invention of directmanipulation[9] that further decreased the interaction unit to a single feature, users finally received real-time feedback: Input by the user and output by the system are so tightly coupled that no visible lag exists. This tightened feedback cycle has many benefits, among others that “novices can learn basic functionality quickly” and “retain operational concepts”[8]. (See Figure 1.)

INTERACTING WITH PERSONAL FABRICATION MACHINESBy Stefanie Mueller, X-Consortium Career Development Assistant Professor of Electrical Engineering and Computer Science; Member, Computer Science and Artificial Intelligence Laboratory (CSAIL)

Figure 1


Figure 1. Server Feedback

As described above, the current interaction model of 3D printers requires objects to be fabricated in one go. Thus, from a human-computer interaction standpoint, we are today at the point at which we were with personal computers in the 1960s: Only few users are able to use the technology, and even for experts, it is a cumbersome process due to the delayed feedback.

1.4 Bringing Direct Manipulation to Fabrication

I argue that by repeating the evolution of the interaction model from personal computing, we will see the same benefits for personal fabrication: Direct manipulation will allow non-technical users to create physical objects as easily as they manipulate digital data with today’s personal computers.

A direct manipulation system for personal fabrication needs to have four main characteristics: 1) the physical environment is the workspace, not a digital editor; 2) users work hands-on on the physical workpiece through physical tools as known from traditional crafting; 3) each physical action results in immediate physical change, which can also be reversed; and 4) in contrast to traditional crafting, users receive support froma computer system that helps to achieve precision.

In the following section, we show examples of two systems that implement the requirements listed above and iteratively decrease the interaction unit from entire objects, to single elements, to features to achieve real-time physical feedback.

1) Turn-taking: Interactive Laser-Cutting. To illustrate whata turn-taking system for personal fabrication might look like, we decrease the interaction unit from entire objects to single elements. In our system constructable[6], users draw with a laser pointer onto the workpiece inside a laser cutter. The drawing is captured with a camera. As soon as the user finishes drawing an element, such as a line, the constructable system beautifies the path and cuts it, resulting in physical output after every editing step. Different tools allow users to accomplish different tasks, such as copy-pasting physical shapes or creating matching finger joints between two edges. In addition, constructable ensures that all physical output is aligned (see Figure 2).

Figure 2: constructable

While constructable allows for fast physical feedback, the interaction is still best described as turn-taking because it consists of two discrete steps: users first perform a command and then the system responds with physical feedback.

2) Direct Manipulation: Continuous Forming. By decreasingthe interaction unit even further to a single feature, we explore how to make the workpiece change while the user is manipulating it, resulting in real-time physical feedback: Input by the user and output by the fabrication device are so tightly coupled that no visible lag exists. Our system FormFab[7] provides such continuous physical feedback (see Figure 3). To accomplish this, FormFab neither adds nor subtracts material, but instead reshapes it (formative fabrication). A heat gun attached to a robotic arm warms up a thermoplastic sheet until it becomes compliant; users then control a pneumatic system that applies either pressure or vacuum, thereby pushing the material outwards or pulling it inwards. As users interact, they see the workpiece change continuously.

Figure 3

“ I envision a future in which, rather than just consuming existing content, 3D printers will allow non-technical users to create objects that only trained experts can create today.”

Figure 2


1.5 Discussion

Direct manipulation systems for personal fabrication extend the range of problems novice users can tackle, but they are subject to the same limitations as those for personal computing: While they are useful for some design problems, they are less so for others. As Norman et al.[4] point out, direct manipulation interfaces are limited to operations that can be done on “visible objects” and have “difficulties handling variables” and “distinguishing an individual element from a representation of a set or class of elements.” Thus design problems that require more abstract thinking for which users must first sit down with a piece of paper and make a detailed plan are better handled with traditional digital 3D editing. In addition, the systems presented in this article inherently scale 1:1 and do not offer a way of inspecting a detail in magnification, which limits users to projects that fit a particular scale. The same way that a saw and a wood chisel cannot replace a detailed design process, our systems cannot replace a complex 3D editing tool for trained engineers.

1.6 Outlook

We focused on using technology available today to explore interaction paradigms that will become possible in the future when fabrication actually gets faster. In recent years, we have begun rapidly advancing towards such a future. The recently introduced 3D printer Carbon3D, for instance, speeds up fabrication by up to 200 times.

While there is little data today that could prove a Moore’s law for 3D printers, an executive from 3D Systems, a leading manufacturer, noted in 2014 that 3D printing speed had, on average, doubled every 24 months over the previous 10 years[5]. If such a trend should materialize, it is not far-fetched to assume that fabrication technology will be able to provide feedback even for large high-resolution objects within seconds or even in real time, thereby enabling a future in which digital displays will be replaced with physical displays that transform virtual reality into actual physical reality.

References

[1] P. Ceruzzi, A History of Modern Computing, 2nd edition. The MIT Press (2003).

[2] M. Csikszentmihalyi, Flow: The Psychology of Optimal Experience. Harper Perennial Modern Classics (1990).

[3] J. Grudin, A Moving Target: The Evolution of Human-Computer Interaction. Human-Computer Interaction Handbook, 3rd edition, Taylor and Francis (2012).

[4] E. Hutchins, J. Hollan, and D. Norman. Direct Manipulation Interfaces. Human-Computer-Interaction (1985), vol. 1, pp. 311-338.

[5] Moore’s law for 3D printing: https://3dprint.com/7543/ 3d-printing-moores-law

[6] S. Mueller, P. Lopes, P. Baudisch. Interactive Construction: Interactive Fabrication of Functional Mechanical Devices. Proceedings of the ACM UIST 2012, pp. 599-606.

[7] S. Mueller, A. Seufert, H. Peng, R. Kovacs, K. Reuss, T. Wollowski, F. Guimbetiere, P. Baudisch. Continuous Interactive Fabrication. Under review for ACM UIST 2017.

[8] B. Shneiderman. Direct Manipulation: A Step Beyond Programming Languages. Computer (1983), vol. 16, issue 8, pp. 57-69.

[9] B. Shneiderman. The Future of Interactive Systems and the Emergence of Direct Manipulation. Proceedings of the NYU Symposium on User Interfaces on Human Factors and Interactive Computer Systems (1984), pp. 1–28.

[10] Wohlers Report (2016). https://wohlersassociates.com/ 2016report.htm

“ By repeating the evolution of the interaction model from personal computing, we will see the same benefits for personal fabrication: direct manipulation will allow non-technical users to create physical objects as easily as they manipulate digital data with today’s personal computers.”


Background. What went wrong with the election polls in the2016 U.S. presidential election? How can the online activity

of the population help curate better life experiences for all? Can we utilize online personas for reaching out to individuals in a targeted manner? What about predicting the demand for espadrilles this summer? Or ranking the performance of your favorite sports team? And what happened to the promise of using collective wisdom for stopping the spread of ‘’fake news’’ on Facebook?

Answers to all those questions depend on our ability to process “social” data to extract meaningful information. For the past few decades, and even more so recently, everything online is being recorded. If aliens came to earth and inspected the social data — generated by us as a society (not by machines) — they would learn, for instance, that Patriots’ Day is when the Boston Marathon is held.

Put another way: Social data presents us with an enormous opportunity for making data-driven decisions for better living, more efficient operations, more effective policy making, and overall uplifting of societies. Here, data is the enabler. Access to social data has been democratized; the key to success lies in the ability to process it so that we can extract meaningful information from it. This makes it feasible for someone like me as an “ivory-tower” academic to carefully think through a solution, test it out, and then have a chance of making an impact in the real world — and, in the process, advance the foundations of statistics and machine learning. In that sense, social-data processing presents an unusual, potentially once-in-a-generational, opportunity that can lead to a remarkable convergence of academia and industry.

Challenge. The standard approach for data-driven decisions following statistical decision theory is to use an appropriate model that connects data to decision variables, helps make desired predictions, and eventually facilitates optimization over decision choices. Here, data is generated by humans, so modeling social behavioral aspects is essential. Any social scientist can attest that modeling human behavior is an extremely intricate task and the resulting models can be highly context-dependent. That makes it especially challenging to come up with effective, meaningful models. The only hope is for gaining access to lots of social data to decipher the right model for a particular interest from a large class of models. In other words, we need a model that is flexible enough to capture a wide array of social scenarios. But the model must be sufficiently tractable so that with enough data it can capture the ground truth faithfully, and it is important that such a system can computationally scale along with the data.

In short, the key intellectual challenge is in finding a sufficiently flexible model for social data that is both statistically and computationally tractable. This is a major challenge, and its successful resolution can have substantial impact on all the previously mentioned scenarios — and many others.

Turning Weakness to Strength. To progress toward such a grand challenge, it is essential to identify the properties of social data that are ubiquitous across a variety of scenarios and that can be captured to develop meaningful models.

We have identified one such property: social data is (or should be) anonymous. That is, from the data-processing perspective, it should not matter who has generated the data. To put it another way, the overall conclusion should remain invariant if we re-name the individuals who have generated the data. For example, the results of a democratic election should not change even if the voters’ names change, as long as the total number of votes for each candidate remains the same. In the same way, the popularity of a specific style of espadrilles does not depend on which specific individuals bought them, only on how many pairs are being purchased.

Anonymity seems like a constraint or a weakness from any angle you look at it.

After all, anonymity and privacy protections restrict the type of information that we can mine from data. But we derive strength from this apparent weakness. It will help us address the challenge of developing tractable and flexible models for social data.

Mathematically, anonymity can be viewed as the underlying “probabilistic model” having a certain “exchangeability” property. A remarkable development in mathematical statistics, starting with the work of de Finetti in the 1930s with further developments in the 1970s and 1980s, provides a crisp non-parametric characterization for such models: the Latent Variable Model. We utilize the Latent Variable Model for social-data processing for a variety of scenarios, including some of those discussed in the questions that opened this article.

Taking the First Steps. We start by examining the question of designing personalization or recommendation systems such as those used by Netflix, YouTube, Amazon, and Spotify. Here, the goal is using the history of an entire population’s preferences to predict which movies, music, books, or other products that individual consumers may like and that they have not already experienced. On one hand, the question is: What is the best algorithm to design for that end goal using the non-parametric model emerging from exchangeability? On the other hand, that question has been with us since the dawn of the e-commerce era.

There is a popular algorithm, called Collaborative Filtering[0], that has been with us from the start and that continues to be used due to its simplicity and empirical success. In a nutshell,

SOCIAL DATA PROCESSINGBy Devavrat Shah, Professor of Electrical Engineering and Computer Science; Member, Laboratory for Information and Decision Sciences (LIDS), Institute for Data, System and Society (IDSS); Director, Statistics and Data Science Center (SDSC)


the algorithm embodies the following time-tested insight: If your friend likes a new movie, and that friend’s tastes are similar to your own, you will likely enjoy the new movie as well. Such a simple, intuitive — or, may I say, social — algorithm has been used in practice successfully but with little understanding of why it works. Ultimately, the goal is to understand the Collaborative Filtering algorithm and, in the process, try to find ways to improve it — and, if feasible, achieve the best performance.

In our work[1], we have precisely addressed this question. We use the non-parametric model arising from exchangeability to study this question. We find that the Collaborative Filtering algorithm, somewhat miraculously, is solving the right statistical problem. In a nutshell, it implicitly performs “local’’ approximation for the non-parametric functional model underlying the data without knowing the function! The nature of the algorithm leads to accurate learning when the available data is sufficient enough. However, this is not the “best” performance in terms of the data required for accurate learning.

The above framework provides systematic means to improve upon the Collaborative Filtering algorithm. To begin with, in the regime where a lot of data is available, a simple improvement to the Collaborative Filtering algorithm can lead to superior performance (see Figure 1). This improved algorithm has the best-known performance for the most generic model class as argued in[1].

A well-known limitation of the Collaborative Filtering algorithm in practice is its inability to work well in the presence of very sparse data. Imagine the scenario of YouTube where 300 hours of video are uploaded every minute, or a shoe retailer where completely new designs of espadrilles are introduced every season. There is very little data about new shows or espadrilles across the population. In the context of Collaborative Filtering, you may find that none of your friends has watched the new shows or tried the new espadrilles. Therefore, the algorithm may not able to provide meaningful recommendations.

In a recent work[2], we extend the Collaborative Filtering algorithm to overcome this sparse data limitation by using the following “social insight’’ guided by the non-parametric statistical model: your friend’s friend can be your friend; or more generally, if you and your friend have similar preferences, and your friend’s friend has similar preferences to your friend, then your friend’s friend may have preferences similar to yours. The resulting “iterative’’ Collaborative Filtering algorithm turns out to have (near) optimal statistical performance in sparse data regime. And it’s remarkably simple.

Where to Go from Here. The non-parametric Latent Variable model is useful beyond the setting of recommendation or personalization. Over the past decade, as a community, we have developed solutions for a variety of scenarios. Notable ones include:

(a) finding aggregate ranking over a collection of choices such as teams, players, or faculty candidates by synthesizing data available in the form of partial rankings or preferences such as pair-wise comparisons[3];

(b) finding accurate answers to questions such as those collected through polls and surveys or crowd-sourcing platforms based on noisy answers[4]; and

(c) finding communities in a society based on noisy pair-wise interaction data[5].

It turns out that for each of these scenarios (and more), the Latent Variable Model is a less restrictive, or more flexible, model while being tractable. Subsequently, this provides a way to develop a data-processing algorithm for a wide variety of social settings simultaneously. For example, I am very excited about our ongoing project, where we use the Latent Variable Model for time-series data in high-dimension for accurate forecasting in real time.

The Latent Variable Model has applicability beyond social data (cf. see [1] and [2]). For example, it can help de-noise image data by viewing images as 3-order tensor of RGB values and connecting to a Latent Variable Model. An example of the efficacy of the Collaborative Filtering algorithm for de-noising image over academic benchmark images is described in Figure 2.

Summary. Social data presents us with a tremendous opportunity. To realize the opportunity, it is essential to develop statistical models “universal’’ enough to faithfully capture a broad class of “social” scenarios; and inference algorithms for such models that are statistically and computationally tractable. This is a grand challenge, because modeling social behavior that generates data is extremely hard. The non-parametric Latent Variable Model naturally arising due to the anonymity property of social data is a promising candidate in making progress towards this challenge, especially given the initial progress made.

Figure 1. This is an experiment representing the performance of various recommendation algorithms using the MovieLens dataset. The performance of the algorithm is measured in Root-Mean-Squared-Error (RMSE) — the lower, the better. Algorithm performance is evaluated for different fractions of test data (the rest of the data is training). The orange curve corresponds to spectral method (soft-impute), the blue curve corresponds to user-user variant of Collaborative Filtering, the red curve corresponds to item-item variant of Collaborative Filtering, and the purple curve corresponds to the improved Collaborative Filtering algorithm using the Latent Variable Model.


References

[0] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry. Using Collaborative Filtering to Weave an Information Tapestry. Communications of ACM, 1992.

[1] C. Lee, Y. Li, D. Shah, and D. Song. Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering. Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS), 2016.

[2] C. Borgs, J. Chayes, C. Lee, and D. Shah. Recommendations for Sparse Datasets via Similarity Based Collaborative Filtering. Preprint, 2017.

[3] S. Negahban, S. Oh, and D. Shah. Rank Centrality: Ranking from Pair-wise Comparisons, Operations Research, 2016. Preliminary version in Proceedings of NIPS, 2012.

[4] D. Karger, S. Oh, and D. Shah. Budget-Optimal Task Allocation for Reliable Crowd-Sourcing, Operations Research, 2014. Preliminary version in Proceedings of NIPS, 2011.

[5] C. Moore. Computer Science and Physics of Community Detection: Landscapes, Phase Transitions and Hardness, Bulletin of EATCS, 2017.

Figure 2. Recovery results for two images (building facade and peppers) with 70 percent of missing entries under different algorithms are presented. The last column corresponds to our algorithm, which is based on Collaborative Filtering. The performance is measured with respect to Relative Squared Error (RSE) — again, the lower, the better.

“ Social data presents us with an enormous opportunity for making data-driven decisions for better living, more efficient operations, more effective policy making, and overall uplifting of societies.”


Max Shulaker, an expert on nanosystems exploitingemerging nanotechnologies, joined the EECS faculty in the

fall of 2016. He is the Emmanuel E. Landsman (1958) Career Development Assistant Professor of Electrical Engineering and Computer Science and a principal investigator for both the Microsystems Technology Laboratories (MTL) and Research Laboratory of Electronics (RLE). At MIT, he is starting the Novel Electronic Systems (NOVELS) research group.

He received bachelor’s, master’s, and PhD degrees in electrical engineering from Stanford University. His PhD research on carbon nanotube-based transistors and circuits resulted in several firsts:

• the first digital systems built entirely using carbon nanotubefield-effect transistors, or FETs (including the first carbonnanotube microprocessor),

• the first monolithic three-dimensional integrated circuitscombining arbitrary vertical stacking of logic and memory,and

• the highest performance and highly-scaled carbon nanotubetransistors to date.

At MIT, Shulaker is launching an experimental research program aimed at realizing his vision for the next generation of electronic systems based on transformational nanosystems, leveraging the unique properties of emerging nanotechnologies and nanodevices to create new systems and architectures with enhanced functionality and improved performance.

Shulaker was interviewed in his Building 39 office, which overlooks the ongoing construction for MIT.nano, the new nanoscale fabrication and characterization facility scheduled to open in 2018. He spoke about his past and current research, his new experimental program, and his early experience at MIT.

Q: How did you become interested in nanosystems and nanotechnologies?

A: I got interested in this area in a class on digital systems that I took freshman or sophomore year at Stanford. The professor talked about trying to make a computer out of carbon nanotubes. It seemed like a crazy idea, but I asked the professor if I could help, and he said “yes.” That started close to a decade of working on carbon nanotubes.

The more things I did in the lab, the more excited I became about the technologies. That’s one reason I always encourage undergraduates to get involved in research — you never know when you will find your passion.

This work was exciting to me because it spanned all layers of the computing stack. I began focusing on the materials and carbon nanotube synthesis. Then we started looking at circuits. Then we started looking at systems. Then we starting looking at new applications. And now, my own PhD students are working on projects that span all those layers as well. They have to work on the new materials to build new circuits to enable new systems to demonstrate new applications.

When you add up all of the benefits across all these different layers, you aren’t talking about 10 or 20 percent benefits anymore, but instead gains exceeding several orders of magnitude. This work has the potential to make a huge difference in the world, and it is why I — and my students — are so excited to be working on it.

NEXT-GENERATION NANOSYSTEMS: A Q & A WITH MAX SHULAKER By Anne Stuart | EECS

“ I began focusing on the materials and carbon nanotube synthesis. Then we started looking at circuits. Then we started looking at systems. Then we starting looking at new applications. And now, my own PhD students are working on projects that span all those layers as well.”


Q. You’ve said that the broader field of emerging nanotechnologies is both exciting and depressing. Can you explain?

A: Sure. It’s exciting because of the promise we say that by using these new technologies, we can make chips that will be extremely fast and extremely energy-efficient. We can have sensors distributed all over the world. We can have chips in your body finding — and curing — diseases. And although these promises are exciting, they’re also a little depressing, because while we talk about how these technologies will change the world, too often in the lab we only make a single device, or a single transistor. So there is a huge disconnect between the motivation that we use to drive this field of research, and what we actually show working in the lab.

My group is trying to bridge the gap between what we say these emerging nanotechnologies can enable and what we actually demonstrate in the lab. To do this, the group works on — out of necessity — many different aspects: some people focus more on design and simulation, while others are experimentalists and actually build the systems that we design.

Q: Tell me about your groundbreaking PhD research. How did you become interested in that subject? Did you expect the number of “firsts” your research generated, or were you surprised? What’s been the result?

A: I did not know when I started undergrad that I was “meant” to do nanosystems. I got involved early in research, and tried out several different areas. It was only by doing and working in the lab that I found out what I didn’t like, what I did like, and eventually, what I loved.

We were certainly happy to have a number of “firsts,” but that was never the motivation to doing the work. I think the fact that we are working to define a new area of research — nanosystems — naturally makes the work new and, hopefully, interesting. While I found my own research during my PhD fascinating, I’m even more excited about the projects my students are working on. They are doing a fantastic job, and I’m eager to see all of the “firsts” they achieve.

Q: Have you actually launched your experimental program at MIT?

A: Yes. I feel tremendously lucky to have formed a group around an amazingly strong and talented group of core students. I guess it is a cliché to say that the best part of being a professor is working with students, but it really surprised me how true that has been. It’s the students in my research group, it’s the students in my classes. Thanks in large part to them, and thanks to the amazing amount of support I’ve gotten from MIT and other faculty here, as well as from our sponsors, our lab is up and running. My students are in the lab right now building the next generation of these nanosystems!

Q: Here’s something you’ve said about your work: “While investigating new devices or new architectures separately can be beneficial, combining the ‘right’ devices, with the ‘right’ architectures, in the ‘right’ way, results in performance gains that far exceed the sum of their individual benefits, while

simultaneously providing a rich set of enhanced functionality for applications that otherwise may not be feasible using traditional technologies.” Could you talk about what you mean by the “right devices,” the “right architectures,” and the “right way”?

A: That’s a very important question. To take a step back: if we want to understand how to improve computing, we have to know what are the obstacles we are facing today. And it turns out that part of the reason progress in computing is stalling is there aren’t just one or two obstacles facing computing, but many. For example, the “power wall” stems from the fact that it is becoming increasingly difficult to shrink devices smaller, and the “memory wall” refers to how a computer today can spend the vast majority of its time and energy just moving data between memory and logic — and there are many more “walls.”

Because there are many obstacles, it means that there cannot just be one solution. For instance, even if I create an amazing transistor, I would still face the memory wall, and visa-versa. So to realize really, really big gains — like orders-of-magnitude gains — in computing, just solving one problem isn’t enough. Instead, we need to use better devices to build better systems to enable new applications. So device-level research cannot exist in a vacuum. When we work on new devices, we have to figure out which devices, or which “right” device, is going to enable us to not only solve the “power wall,” but also enable us to build new system architectures — or the “right” system — to address the memory wall.

Q: Next, can you provide examples of enhanced functionality and improved performance?

A: We will have something published on this very shortly, and it is a fast-expanding thrust for our group. By leveraging the new fabrication techniques that some emerging nanotechnologies afford us, we can actually make 3D chips, skyscraper chips with multiple levels built one on top of the other. You can have sensing, data storage, and computation — all in one chip. That kind of chip, with fine-grained integration between these heterogeneous aspects of a system, can only be built using these new technologies. In fact, we are working in collaboration with Stanford University on a project which shows that this is the key to achieving the next 1,000X gain in energy efficiency.

Q: You’ve also been described as planning “to leverage the richness of new nanomaterials, new computing and memory technologies, and heterogeneous integration to enable new applications beyond the scope of traditional computing.” Please talk more about some of these new nanomaterials and technologies — and, especially, their potential.

A: I like to say that my group is not “married” to any specific emerging nanomaterial or nanotechnology. Rather, we are pretty substrate-agnostic, and instead really try to figure out which material is going to enable which devices to enable which systems, and so on. That being said, since a key application thrust for us is computing, we do work heavily on carbon nanotubes, which are rolled-up sheets of graphene to form nanocylinders with diameters of only about 1 nanometer. As you can imagine, there are a whole host of amazing things


you can do with a carbon nanotube. We’ve made state-of-the-art, very energy-efficient transistors using them and we are beginning to use them as physical nano-sized tubes, and so on.

We have been fortunate enough to also start collaborating with other faculty here at MIT who work with other types of nanomaterials, and we have been developing new systems and applications leveraging those materials also. Hopefully, you’ll be able to read about these new ideas soon!

Q: You’ve also been described as having the ultimate goal of driving nanosystems “from concept to reality, resulting in hardware demonstrations of what future electronic systems might look like.” How close are we to seeing nanosystems move from concept to reality? How will that happen?

A: It is actually a reality today. We can build and test these futuristic nanosystems, and perform certain tasks now that you simply cannot perform with conventional hardware today. We have also been extremely fortunate to develop a strong collaboration with Analog Devices, Inc. — which, by the way, has done a remarkable job of fostering truly innovative ideas and projects — and we are working hard to see just how far we can push these futuristic ideas into becoming reality.

Another important aspect is that what we work on is compatible with what exists in fabrication and design. We can build on top of any conventional silicon chip today, using the same tools and design infrastructure that already exists. This makes the barrier to introduction much lower. Who knows? Maybe one day, we will replace all of silicon. But to begin with, we don’t need to do that.

Q: Anything else you’d like to add?

A: Coming into MIT, I had a very clear notion of what I wanted my group to work on. But that’s been turned on its head. I’ve been so lucky to interact with other faculty here who are unbelievably talented. Together, we’ve come up with some really cool new ideas and projects I could not have dreamed of even describing before.

For instance, we have been so fortunate to start working today with Sangeeta Bhatia’s lab, developing new imaging and diagnostic modalities, which is something very removed from traditional “computing.” [Editor’s Note: Sangeeta Bhatia, the John J. and Dorothy Wilson Professor at MIT’s Institute for Medical Engineering and Science (IMES) and in EECS, is director of the MIT Laboratory for Multiscale Regenerative Technologies (LMRT).]

These fantastic collaborations allow us to still drive our core competency of computing, yet simultaneously explore how nanosystems can impact applications that lie beyond the scope of what we traditionally view as computing.

“ What we work on is compatible with what exists in fabrication and design. We can build on top of any conventional silicon chip today, using the same tools and design infrastructure that already exists. This makes the barrier to introduction much lower.”

Editor’s Note: Shulaker was the lead author of an article about development of a new 3-D computer chip, published in the journal Nature in July 2017. For more on that story, see news.mit.edu/2017/new-3-d-chip-combines-computing-and-data-storage-0705.

http://news.mit.edu/2017/new-3-d-chip-combines-computing-and-data-storage-0705

FACULTY FOCUS

Faculty Awards 39

Faculty Research Innovation Fellowships (FRIFs) 45

New EECS Associate Department Heads 46

EECS Professorships 47

New Career Development Chairs 51

New Faculty 51

Remembering EECS Faculty: Mildred S. Dresselhaus, Robert Fano 54

eecs.mit.edu 2017 CONNECTOR faculty 39

Mohammed AlizadehSloan Research FellowshipFacebook Faculty AwardVMWare Early Career Faculty AwardGoogle Research Award

Anant AgarwalPadmi Shri Award

Elfar AdalsteinssonFrank Quick Faculty Research Innovation Fellowship

Regina BarzilayHari BalakrishnanDelta Electronics Professor of Electrical Engineering and Computer Science

American Academy of Arts and Sciences

Duane BoningSangeeta BhatiaNational Academy of Inventors National Academy of ScienceUtrecht University Honorary Doctorate

Tim Berners-LeeA.M. Turing Award, Association for Computing Machinery

Clarence J. LeBel Professor of Electrical Engineering

Karl BerggrenBose FellowshipFrank Quick Faculty Research Innovation Fellowship

40 faculty CONNECTOR 2017 eecs.mit.edu

Munther DahlehInternational Federation of Automatic Control (IFAC) Award

Anantha ChandrakasanUC Berkeley EE Distinguished Alumni Award

Randall DavisInstitute for Operations Research and the Management Sciences (INFORMS) 2016 Innovative Applications of Analytics Award

Srini DevadasIEEE W. Wallace McDowell Award from the IEEE Computer Society

Fredo DurandACM FellowACM Siggraph Computer Graphics Achievement Award

Dirk EnglundAdolph Lomb Medal, Optical Society

G. David Forney, Jr.IEEE Medal of Honor

Erik DemaineACM FellowRare Craft Fellowship Award, American Craft CouncilBard College Honorary Degree

Tamara BroderickGoogle Research AwardSavage Award ISBA Lifetime Members Junior Researchers Award


ACM Fellow

Ruonan HanPolina GollandMICCAI Society FellowFrank Quick Faculty Research Innovation Fellowship

James FujimotoNational Academy of Engineering Fritz J. and Dolores H. Russ Prize

NSF CAREER Award

Jongyoon HanFrank Quick Faculty Research Innovation Fellowshipp

Tommi JaakkolaThomas Siebel Professor Association for the Advancement of Artificial Intelligence Fellow

William Freeman

Thomas HeldtW.M. Keck Career Development Pro-fessor in Biomedical EngineeringLouis D. Smullin (‘39) Award for Teaching Excellence

Daniel JacksonACM FellowArthur C. Smith Award 2017 ACM SIGSOFT Outstanding Researcher

Shafi GoldwasserUniversity of Haifa Honorary DoctorateBarnard College Medal of DistinctionRussian Academy of SciencesSuffrage Science Award, Imperial College London


Dina KatabiNational Academy of Engineering

Stefanie JegelkaGoogle Research Award

Manolis KellisEECS Faculty Research Innovation Fellowship

Barbara Liskov Luqiao LiuNCWIT Pioneer in Tech Award NSF CAREER Award

Nancy LynchNational Academy of Sciences

Aleksander MadrySloan Research FellowshipGoogle Research Award

Jae S. LimInducted into the Consumer Technology Hall of Fame

IEEE Vehicular Technology Society James Evans Avant Garde Award

Muriel Medard


Daniela Rus

Stefanie MuellerX-Consortium Career Development Assistant Professor Forbes 30 Under 30 in Science

American Academy of Arts and SciencesEngelberger Robotics Award from Robotics Industries Association

Ronald RivestElectronic Frontier Foundation Pioneer Award

Henry I. SmithIEEE Robert N. Noyce Medal

Max ShulakerEmmanuel E. Landsman (1958) Career Development Assistant Professor

Justin SolomonX-Window Consortium Career Development Assistant ProfessorArmy Young Investigator AwardForbes 30 Under 30 in Science

Tomás PalaciosIEEE Fellow

David SontagHermann L. F. von Helmholtz Career Development Assistant Professor in the Institute for Medical Engineering and Science (IMES)

Vivienne SzeYoung Investigator Research Program (YIP) award from the Air Force Office of Scientific Research (AFOSR)3M Non-Tenured Faculty Award


Peter SzolovitsInternational Academy of Health Sciences Informatics

John Tsitsiklis

Antonio Torralba

ACM SIGMETRICS Achievement Award

Frank Quick Faculty Research Innovation Fellowship

Caroline UhlerSloan Research FellowshipNSF CAREER Award

Cardinal WardeStevens Institute of Technology, Distinguished Alumni Award, Science and Technology

Ron WeissBose Fellowship

Russell TedrakeToyota Professor

Steven G. (1968) and Renee Finn Career Development Associate ProfessorNSF CAREER AwardSloan Research Fellowship

Virginia Williams

EECS AwardsEach year, EECS honors faculty, students, and staff for their outstanding achievements.

2016 Awards: eecs.mit.edu/2016-Spring-Awards

2017 Awards: eecs.mit.edu/2017-Spring-Awards


FACULTY RESEARCH INNOVATION FELLOWSHIPS (FRIFs)

Three professors in the Department of Electrical Engineering and Computer Science (EECS) have been awarded 2016–2017 Frank Quick Faculty Research Innovation Fellowships (FRIFs).

The FRIFs were created to recognize midcareer faculty for outstanding research contributions and international leadership in their fields. FRIFs provide faculty members with resources to pursue new research and development paths and to make potentially important discoveries through early-stage research.

Elfar Adalsteinsson is a professor in EECS and the Institute for Medical Engineering and Science (IMES). His group applies interdisciplinary skills to medical imaging at the intersection of engineering, computation, physics, science, and medicine. From 2010 to 2016, he served as associate director of the Madrid-MIT M+Visión Consortium. This partnership of leaders in science, medicine, engineering, business, and the public sector was dedicated to catalyzing change in Madrid’s health-care innovation ecosystem by accelerating translational research and encouraging entrepreneurship.

Karl Berggren is a professor of electrical engineering, a principal investigator in the Research Laboratory of Electronics (RLE), and a core member of the Microsystems Technology Laboratory (MTL). His research focuses on methods of nanofabrication, especially applied to superconductive sensors and circuits, photodetectors, electronics and computing, and energy systems. More specifically, current nanofabrication efforts emphasize developing improved charged-particle-based lithography to direct self-assembly by using block copolymers (materials systems that self-assemble to form integrated-circuit-like patterns on the 10-nm length scale). His efforts in the area of superconductivity are currently focused on understanding fundamental mechanisms of photodetection in superconducting nanowires, and on applying superconducting nanowires to classical electronic computing.

Antonio Torralba is a professor in the computer vision group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). His work focuses on novel approaches for image and video understanding. His goal is to build integrated vision systems that recognize objects, reason about contextual relationships between objects and places, and understand people and their actions. With his collaborators, he created the LabelMe annotation tool and a number of other curated databases that are widely used by the computer vision community.

Elfar Adalsteinsson Karl Berggren Antonio Torralba


LYNCH, OZDAGLAR NAMED EECS ASSOCIATE DEPARTMENT HEADSTwo Department of Electrical Engineering and Computer Science (EECS) faculty members were named associate department heads during the 2016-2017 academic year.

Nancy Lynch, the NEC Professor of Software Science and Engineering, became associate department head in September 2016. She succeeded Silvio Micali, the Ford Professor of Computer Science and Engineering, who had served as associate department head since January 2015.

Lynch is known for her fundamental contributions to the foundations of distributed computing. Her work applies a mathematical approach to explore the inherent limits on computability and complexity in distributed systems.

Her best-known research is the “FLP” impossibility result for distributed consensus in the presence of process failures. Other research includes the I/O automata system modeling frameworks. Lynch’s recent work focuses on wireless network algorithms and biological distributed algorithms.

The longtime head of the Theory of Distributed Systems research group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Lynch joined MIT in 1981. She received a BS from Brooklyn College in 1968 and a PhD from MIT in 1972, both in mathematics. Recently, Lynch served as head of CSAIL’s Theory of Computation (TOC) group for several years.

She is also the author of several books and textbooks, including the graduate textbook Distributed Algorithms, considered a standard reference in the field. Lynch has also co-authored several hundred articles about distributed algorithms and impossibility results, and about formal modeling and verification of distributed systems. She is the recipient of numerous awards, an Association for Computing Machinery (ACM) Fellow, a Fellow of the American Academy of Arts and Sciences, and a member of both the National Academy of Science and the National Academy of Engineering.

Asu Ozdaglar, the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering, became associate department head in January 2017. Ozdaglar succeeded David Perreault, professor of electrical engineering, who had served in the role since November 2013.

Ozdaglar is best known for her contributions in the areas of optimization theory, economic and social networked systems, and game theory. She has made several key contributions to optimization theory, ranging from convex analysis and duality to distributed and incremental algorithms for large-scale

systems and data processing. She is a co-author of Convex Analysis and Optimization.

Ozdaglar’s research focuses in large part on integrating analysis of social and economic interactions into the study of networks. Her work spans many dimensions of this area, including analysis of learning and communication, diffusion and information propagation, influence in social networks, and study of cascades and systemic risk in economic and financial systems. She continues to make key game-theory contributions, including learning dynamics and computation of Nash equilibria.

In October 2014, Ozdaglar became the director of the Laboratory for Information and Decision Systems (LIDS) and the associate director of the Institute for Data, Systems, and Society (IDSS). Ozdaglar was also a Technical Program Co-Chair of the 2015 Rising Stars program in EECS.

Ozdaglar has also organized numerous conferences and sessions on game theory, networks, and distributed optimization. She received the prestigious Donald P. Eckman Award from the American Automatic Control Council, and she was the inaugural recipient of the Steven and Renee Finn Faculty Research Innovation Fellowship at MIT.

Nancy Lynch Asu Ozdaglar

Editor’s Note: As this issue went to press, long-time EECS department head Anantha Chandrakasan was named Dean of the MIT School of Engineering, effective July 1. Ozdaglar will serve as interim department head during the search for Chandrakasan’s successor.


Regina Barzilay has been appointed the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT. The appointment recognizes Barzilay’s leadership in the area of human language technologies and her outstanding mentorship and educational contributions.

“Professor Barzilay is internationally known in the fields of natural language processing and computational linguistics, and is widely respected as a creative thought leader,” Anantha Chandrakasan, head of the Department of Electrical Engineering and Computer Science (EECS) and the Vannevar Bush Professor of Electrical Engineering and Computer Science wrote in a note announcing the appointment. “In addition to this research, she has made truly outstanding educational contributions.”

Barzilay’s research on natural languages focuses on the development of models of natural language, and uses those models to solve real-world language processing tasks. Her research in computational linguistics deals with multilingual learning, interpreting text for solving control problems, and finding document-level structure within text. Barzilay’s work enables the automated summarization of documents, machine interpretation of natural language instructions, and the deciphering of ancient languages. As the world has more and more text to be searched and interpreted, applications for this work increase year by year.

Jointly with Professor Tommi Jaakkola, Barzilay developed the popular Introduction to Machine Learning course (6.036), which enrolled 500 students in spring 2017. Barzilay has also recently revised the format of 6.864 (Advanced Natural Language Processing). That class’s content was modified to incorporate applications of deep neural networks to natural language processing, material covered almost exclusively in research papers. In addition, the class was reformatted to emphasize project-driven learning. This format helped multiple students — especially undergraduates — to start their own research in natural language processing. Barzilay was recognized for her educational contributions with the Jamieson Teaching Award in 2016.

Barzilay has also made valuable professional contributions in her field and in the department. She serves as the action editor for the Transactions of the Association for Computational Linguistics. She served as the program co-chair for the Conference on Empirical Methods in Natural Language Processing (EMNLP) in 2011, and is a chair of the 2017 Association of Computational Linguistics Conference. She

was also program co-chair of the 2015 Rising Stars workshop for women in computer science and electrical engineering workshop at MIT.

Barzilay is a recipient of various awards, including the National Science Foundation Career Award, the MIT Technology Review Innovators Under 35 Award, a Microsoft Faculty Fellowship, and several best paper awards in top natural language-processing conferences.

REGINA BARZILAY NAMED DELTA ELECTRONICS PROFESSOR


Duane Boning has been named the Clarence J. LeBel Professor of Electrical Engineering. The chair is named for Clarence Joseph LeBel ‘26, SM ‘27, who co-founded Audio Devices in 1937 and was a pioneer in recording discs, magnetic media for tapes, hearing aids, and stethoscopes.

“Boning’s teaching is recognized as outstanding at both the undergraduate and graduate levels, and he is a leader in the field of manufacturing and design,” noted Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science and head of the Department of Electrical Engineering and Computer Science (EECS). “This is fitting recognition of his outstanding contributions to research, teaching, mentoring, and service.”

Boning’s research focuses on manufacturing and design, with emphasis on statistical modeling, control, and variation reduction in semiconductor, MEMS, photonic, and nanomanufacturing processes. His early work developed computer integrated manufacturing approaches for flexible design of IC fabrication processes. He also drove the development and adoption of run-by-run, sensor-based, and real-time model-based control methods in the semiconductor industry. He is a leader in the characterization and modeling of spatial variation in IC and nanofabrication processes, including plasma etch and chemical-mechanical polishing (CMP), where test mask design and modeling tools developed in his group have been commercialized and adopted in industry. Boning served as editor in chief for the IEEE Transactions on Semiconductor Manufacturing from 2001 to 2011, and was named a fellow of the IEEE for contributions to modeling and control in semiconductor manufacturing in 2005.

In addition to creating the graduate-level course Control of Manufacturing Process (6.780J/2.830J), he has lectured in several core EECS subjects, including Signals and Systems (6.003) and Structure and Interpretation of Computer Programs (6.001). His teaching has been recognized with the MIT Ruth and Joel Spira Teaching Award. Boning won the Best Advisor Award from the MIT ACM/IEEE student organization in 2012 and the 2016 Capers and Marion McDonald Award for Excellence in Mentoring and Advising in the School of Engineering.

Boning served as associate department head in EECS from 2004 to 2011. He has previously and presently serves as associate director in the Microsystems Technology Laboratories, where he oversees the information technology and computer-aided design services organization in the laboratories. He is a long-standing and active participant in the

MIT Leaders for Global Operations (LGO) program and became the engineering faculty co-director for LGO in September 2016. Since 2011, he has served as the director for the MIT/Masdar Institute Cooperative Program, fostering many joint activities between MIT and Masdar Institute, an engineering university in Abu Dhabi, United Arab Emirates. From 2011 through 2013, he served as founding faculty lead in the MIT Skoltech Initiative, working to launch the Skolkovo Institute of Science and Technology (Skoltech) near Moscow, Russia.

Within MIT, Boning has served on several Institute committees, including as chair of the Committee on Undergraduate Admissions and Financial Aid (CUAFA) in 2007, and he served as chair of the Committee on the Undergraduate Program (CUP) in 2016-2017.

DUANE BONING APPOINTED LEBEL PROFESSOR IN EECS


Tommi Jaakkola, a professor of computer science and engineering, has been named as the inaugural holder of the Thomas Siebel Professorship in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS).

The appointment was announced in April 2017 by EECS Department Head Anantha Chandrakasan, Vannevar Bush Professor of EECS, and IDSS Director Munther A. Dahleh, William Coolidge Professor of EECS. “The appointment recognizes Professor Jaakkola’s leadership in the area of machine learning and his outstanding mentorship and educational contributions,” Chandrakasan and Dahleh wrote in a message to EECS faculty. “Professor Jaakkola is internationally well-known in the fields of machine learning and natural language processing, as well as in computational biology. He is widely respected as an original researcher and has made high-impact contributions.”

The new professorship was established through the generous contribution of veteran software entrepreneur Thomas Siebel, Chairman and CEO of C3IoT. Siebel is well-known at MIT for having established the Siebel Scholars program, which annually provides support for 16 MIT graduate students (five in EECS, five in Biological Engineering, five in the MIT Sloan School of Management, and one in Energy Science).

At the core of Jaakkola’s research are inferential and estimation questions in complex modeling tasks, ranging from developing the underlying theory and associated algorithms to translating such advances into applications. He has been a leading contributor to developing distributed probabilistic inference algorithms from this field’s inception to its current state as a well-established area of research.

From the modeling point of view, Jaakkola’s work covers a broad spectrum of areas, from the interface between generative and discriminative modeling, rethinking modeling from the point of view of randomization and combinatorial optimization, to recovery questions associated with continuous embedding of objects. In natural language processing (NLP), his contributions include solving hard combinatorial inference problems such as natural language parsing, developing deep convolutional representations of text, and reframing complex models to reveal interpretable rationales for prediction. Several of his papers have received best-paper awards at leading events.

In addition, Jaakkola “has made outstanding educational contributions,” Chandrakasan and Dahleh noted. He established and oversaw the growth of the graduate machine learning course, teaching it for many years until Professor Leslie Kaelbling took it over for further development.

Together with Professor Regina Barzilay, he developed the undergraduate machine learning course, which now enrolls more than 500 students per term. He modernized the advanced NLP course, again taught with Barzilay, from the point of view of neural approaches to NLP. In 2015, Jaakkola received the Jamieson Award for Excellence in Teaching in recognition of his educational contributions.

He has also made valuable professional contributions in his field and within EECS. He has held editorial positions on prestigious journals such as the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. He has also co-chaired or overseen areas of major conferences, including the Conference on Neural Information Processing Systems (NIPS), the Conference on Uncertainty in Artificial Intelligence (UAI), and the Conference on Artificial Intelligence and Statistics (AISTATS). He served for many years on the EECS Faculty Search Committee and has been a member of other committees as well. He has also contributed to the career paths of many students and postdocs that he has supervised and mentored at MIT. Former students and postdocs from his research group now hold positions in leading universities such as MIT, CMU, and UC Berkeley.

As an affiliate member of IDSS, Jaakkola has been instrumental in both the hiring and recruitment of statistics faculty as well as the creation of programs in statistics. He has served on the IDSS Statistics Faculty Search Committee from the start, and worked with the IDSS Statistics PhD Committee to develop a proposal for a dual PhD degree. He is also a participant in the Statistics and Data Science MicroMasters.

TOMMI JAAKKOLA NAMED INAUGURAL SIEBEL PROFESSOR


Russ Tedrake, a faculty member in the Department of Electrical Engineering and Computer Science (EECS), has been named as the inaugural chair holder of the Toyota Professorship.

The appointment was announced in May 2017 by Anantha Chandrakasan, EECS department head and Vannevar Bush Professor of Electrical Engineering and Computer Science, and Ian Waitz, Dean of the School of Engineering. “The appointment recognizes Professor Tedrake’s leadership in the area of robotics and his outstanding mentorship and educational contributions,” Chandrakasan and Waitz wrote in a message to faculty. “Professor Tedrake is internationally well-known in the field of robotics, and is widely respected for his theoretical, algorithmic, and experimental contributions to the field.”

Tedrake’s research focuses on developing optimization-based algorithms for planning, feedback control, and analysis of complex dynamic robots that can walk, run, and fly through unstructured environments. His work leverages the observation that the equations of motion of these robots, constrained by mechanics, have special structure. By finding new connections between convex optimization and the mathematical models of, for example, frictional contact mechanics, he has been able to make seemingly intractable problems in robot feedback control become tractable.

Tedrake’s algorithmic results have led to impressive demonstrations on real hardware. His algorithms enabled the first successful demonstrations of high-speed (post-stall) perching for fixed-wing unmanned aerial vehicles (UAVs); his small airplanes could land on a perch like a bird. More recently, his team has developed bird-sized UAVs that can dart through trees at more than 30 mph, guided by a provably robust feedback motion planning engine. He also led MIT’s entry in the DARPA Robotics Challenge, demonstrating optimization-based perception, planning, and feedback control for a complex humanoid that had to drive a car, open doors, turn valves, pick up and operate power tools, and walk across rough terrain and up stairs.

Tedrake has been a leader in organizing robotics activities across campus. He started the Robotics@MIT seminar series and the Robotics@MIT Student Conference, and serves as faculty advisor for many of the robotics student groups and projects at MIT.

“Professor Tedrake has made truly outstanding contributions to both graduate and undergraduate education both on and off

campus,” Chandrakasan and Waitz wrote. “His Underactuated Robotics course was one of the first two graduate courses to be put on edX, with a current enrollment exceeding 20,000 students, and his course notes and open-source software are widely known in the robotics community.” Tedrake has also been instrumental in updating the core controls and signal processing curriculum, and has been recognized with both the Jerome H. Saltzer Award and the Ruth and Joel Spira Award for his undergraduate teaching.

RUSS TEDRAKE APPOINTED TO INAUGURAL TOYOTA PROFESSORSHIP


Max ShulakerEmmanuel E. Landsman (1958) Career Development Assistant Professor

Stefanie MuellerX-Consortium Career Development Assistant Professor

Thomas HeldtW. M. Keck Career Development Professor in Biomedical Engineering

Justin SolomonX-Window Consortium Career Development Assistant Professor

Virginia WilliamsSteven G. (1968) and Renee Finn Career Development Associate Professor

David SontagHermann L. F. von Helmholtz Career Development Assistant Professor

NEW CAREER DEVELOPMENT CHAIRS

NEW FACULTYAdam Belay

Belay will join EECS as an assistant professor in July 2017. He received a PhD in computer science from Stanford University, where he was a member of the secure computer systems group and the multiscale architecture and systems team. Previously, he worked on storage virtualization at VMware Inc. and contributed substantial power-management code to the Linux Kernel project. Belay’s research area is operating systems and networking. Much of his work has focused on restructuring computer systems so that developers can more easily reach the full performance potential of hardware. He received a Stanford graduate fellowship, a VMware graduate fellowship, and a Jay Lepreau Best Paper award from the USENIX Symposium on Operating Systems Design and Implementation (OSDI).


Stefanie Mueller

Mueller joined EECS as an assistant professor in January 2017. She received a PhD in human-computer interaction (HCI) from the Hasso Plattner Institute in 2016, where she also received a master’s degree in IT-systems engineering. In her research, Mueller develops novel interactive hardware and software systems that advance personal fabrication technologies. Her work has been published at the most selective HCI venues — Association for Computing Machinery (ACM), the Conference for Human Factors in Computing Systems (CHI), and User Interface Software and Technology (UIST) — and received a best-paper award and two best-paper nominations. Mueller is an associate chair of the program committees at ACM, CHI, and UIST, and is a general co-chair for the ACM SIGGRAPH Symposium on Computational Fabrication at MIT in June 2017. She has been an invited speaker at MIT, Stanford, the University of California at Berkeley, Harvard, Carnegie Mellon University, Cornell University, Microsoft Research, Disney Research, Adobe Research, and others. In addition, her work has been covered by New Scientist, the BBC, The Atlantic, and The Guardian. Mueller heads the HCI engineering group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), which works at the intersection of human-computer interaction, computer graphics, computer vision, and robotics. She was included in the Forbes “30 Under 30 in Science” list for 2017, and was named an X-Consortium Career Development Assistant Professor in EECS.

Max Shulaker

Shulaker joined EECS as an assistant professor in July 2016. He received his bachelor’s, master’s, and PhD degrees in electrical engineering at Stanford, where he was a Fannie and John Hertz Fellow and a Stanford Graduate Fellow. Shulaker’s research focuses on the broad area of nanosystems. His Novel Electronic Systems Group aims to understand and optimize multidisciplinary interactions across the entire computing stack — from low-level synthesis of nanomaterials, to fabrication processes and circuit design for emerging nanotechnologies, up to new architectures — to enable the next generation of high performance and energy-efficient computing systems.

David Sontag

Sontag joined EECS in January 2017 as an assistant professor. He is also part of MIT’s Institute for Medical Engineering and Science (IMES) and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Before coming to MIT, he had been an assistant professor in computer science and data science at New York University’s Courant Institute of Mathematical Sciences since 2011. Previously, he was a postdoc at Microsoft Research New England. Sontag’s research interests are in machine learning and artificial intelligence with a recent focus on unsupervised learning, a problem of discovering hidden variables from data, and causal inference, which seeks to estimate the effect of interventions from observational data. At IMES, he will lead a research group that aims to transform health care through the use of machine learning. Sontag received CSAIL’s George M. Sprowls award for his PhD thesis at MIT in 2010, best-paper awards at several conference, and a National Science Foundation CAREER Award in 2014. He received a bachelor’s degree in computer science from UC Berkeley and master’s and PhD degrees in electrical engineering and computer science from MIT. He has been named the Hermann L. F. von Helmholtz Career Development Assistant Professor at IMES.


Ryan Williams

Williams joined MIT as an associate professor in EECS in January 2017. He received a bachelor’s degree in computer science and mathematics from Cornell, and a PhD in computer science from Carnegie Mellon. Following postdoctoral appointments at the Institute for Advanced Study (Princeton) and IBM Almaden, he was an assistant professor of computer science at Stanford for five years. Williams’ research interests are in the theoretical design and analysis of efficient algorithms and in computational complexity theory, focusing mainly on new connections (and consequences) forged between algorithm design and logical circuit complexity. Along with some best-paper awards, Williams has received a Sloan Research Fellowship, a National Science Foundation CAREER Award, and a Microsoft Research Faculty Fellowship, and he was an invited speaker at the 2014 International Congress of Mathematicians.

Virginia Vassilevska Williams

Williams joined EECS as an associate professor in January 2017. She received a bachelor’s degree in mathematics and engineering and applied science from Caltech and a PhD in computer science from Carnegie Mellon. She was a postdoctoral fellow at the Institute for Advanced Study at (Princeton), UC Berkeley, and Stanford. Prior to joining MIT, she spent more than three years as an assistant professor at Stanford. Her research interests are broadly in theoretical computer science, focusing on the design and analysis of algorithms and fine-grained complexity. Her work on matrix multiplication algorithms was covered by the media and was the most cited paper in algorithms and complexity in the last five years. She was named the Steven G. (1968) and Renee Finn Career Development Associate Professor in EECS and was also awarded a Sloan Research Fellowship for work done at Stanford.


Mildred S. Dresselhaus, a celebrated and beloved MIT professor whose research helped unlock the mysteries of carbon, the most fundamental of organic elements — earning her the nickname “queen of carbon science” — died at age 86 on Feb. 20, 2017, in Cambridge, Mass.

Dresselhaus, a solid-state physicist who was Institute Professor Emerita of Physics and Electrical Engineering and Computer Science (EECS), was also nationally known for her work to develop wider opportunities for women in science and engineering.

“Yesterday, we lost a giant — an exceptionally creative scientist and engineer who was also a delightful human being,” MIT President L. Rafael Reif wrote in an email to the MIT community. “Among her many ‘firsts,’ in 1968, Millie became the first woman at MIT to attain the rank of full, tenured professor. She was the first solo recipient of a Kavli Prize and the first woman to win the National Medal of Science in Engineering.”

Dresselhaus was also, “to my great good fortune, the first to reveal to me the wonderful spirit of MIT,” Reif added. “In fact, her down-to-earth demeanor was a major reason I decided to join this community. Like dozens of young faculty and hundreds of MIT students over the years, I was lucky to count Millie as my mentor.”

A winner of both the Presidential Medal of Freedom (from President Barack Obama, in 2014) and the National Medal of Science (from President George H.W. Bush, in 1990), Dresselhaus was a member of the MIT faculty for 50 years. Beyond campus, she held a variety of posts that placed her at the pinnacle of the nation’s scientific enterprise.

Dresselhaus’s research made fundamental discoveries in the electronic structure of semi-metals. She studied

various aspects of graphite and authored a comprehensive book on fullerenes, also known as “buckyballs.” She was particularly well known for her work on nanomaterials and other nanostructural systems based on layered materials, like graphene, and more recently beyond graphene, like transition metal dichalcogenides and phosphorene. Her work on using quantum structures to improve thermoelectric energy conversion reignited this research field.

“I like to be challenged,” she was quoted as saying. “I welcome the hard questions and having to come up with good explanations on the spot. That’s an experience I really enjoy.”

A strong advocate for women in STEM

As notable as her research accomplishments was Dresselhaus’s longstanding commitment to promoting gender equity in science and engineering, and her dedication to mentorship and teaching.

In 1971, Dresselhaus and a colleague organized the first Women’s Forum at MIT as a seminar exploring the roles of women in science and engineering. She received a Carnegie Foundation grant in 1973 to support her efforts to encourage women to enter those two traditionally male-dominated fields. For a number of years, she led an MIT seminar in engineering for first-year students; designed to build the confidence of female students, it always drew a large audience of both men and women.

Just two weeks before her death, General Electric released a 60-second video featuring Dresselhaus. The video imagined a world in which female scientists like Dresselhaus were viewed as celebrities, a message intended to both celebrate

Mildred S. DresselhausPhoto: Bryce Vickmark

INSTITUTE PROFESSOR EMERITA MILDRED DRESSELHAUS DIES AT 86“Queen of carbon science” and recipient of Presidential Medal of Freedom and National Medal of Science led US scientific community, promoted women in STEM.

By MIT News


her achievements and encourage more women to pursue careers in the “STEM” fields (science, technology, engineering, and mathematics).

Dresselhaus co-authored eight books and about 1,700 papers, and supervised more than 60 doctoral students.

“Millie’s dedication to research was unparalleled, and her enthusiasm was infectious,” said Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science and head of MIT’s Department of Electrical Engineering and Computer Science (EECS). “For the past half-century, students, faculty and researchers at MIT and around the world have been inspired by her caring advice. I was very fortunate to have had her as a mentor, and as an active member of the EECS faculty. She made such a huge impact on MIT, and her contributions will long be remembered.”

Diverted from teaching to physics

Born on Nov. 11, 1930, in Brooklyn and raised in the Bronx, Mildred Spiewak Dresselhaus attended Hunter College, receiving her bachelor’s degree in 1951 and then winning a Fulbright Fellowship to study at Cambridge University.

While she had planned to become a teacher, Rosalyn Yalow — who would go on to win the 1977 Nobel Prize in physiology or medicine — encouraged Dresselhaus to pursue physics instead. She ultimately earned an MA from Radcliffe College in 1953 and a PhD in 1958 from the University of Chicago, where she studied under Nobel laureate Enrico Fermi. From 1958 to 1960, Dresselhaus was a National Science Foundation Postdoctoral Fellow at Cornell University.

Dresselhaus began her 57-year association with MIT in the Solid State Division of Lincoln Laboratory in 1960. In 1967, she joined what was then called the Department of Electrical Engineering as the Abby Rockefeller Mauze Visiting Professor, a chair reserved for appointments of distinguished female scholars. She became a permanent member of the electrical engineering faculty in 1968, and added an appointment in the Department of Physics in 1983.

In 1985, Dresselhaus became the first female Institute Professor, an honor bestowed by the MIT faculty and administration for distinguished accomplishments in scholarship, education, service, and leadership. There are usually no more than 12 active Institute Professors on the MIT faculty.

Scientific leadership and awards

In addition to her teaching and research, Dresselhaus served in numerous scientific leadership roles, including as the director of the Office of Science at the U.S. Department of Energy; as president of the American Physical Society and of the American Association for the Advancement of Science; as chair of the governing board of the American Institute of Physics; as co-chair of the recent Decadal Study of Condensed Matter and Materials Physics; and as treasurer of the National Academy of Sciences.

Aside from her Medal of Freedom — the highest award bestowed by the U.S. government upon American civilians — and her Medal of Science, given to the nation’s top scientists, Dresselhaus’s extensive honors included the IEEE Medal of Honor for “leadership and contributions across many fields of science and engineering”; the Enrico Fermi Award from the U.S. Department of Energy for her leadership in condensed matter physics, in energy and science policy, in service to the scientific community, and in mentoring women in the sciences; and the prestigious Kavli Prize for pioneering contributions to the study of phonons, electron-phonon interactions, and thermal transport in nanostructures. She was also an elected member of the National Academy of Sciences and the National Academy of Engineering. In 2016, she received honorary degrees from Brandeis University, Oxford University, and North Carolina State University; the last was her 38th such honor.

Active on campus

Always an active and vibrant presence at MIT, Dresselhaus remained a notable influence on campus until her death. She continued to publish scientific papers on topics such as the development of 2D sheets of thin electronic materials, and played a role in shaping MIT.nano, a new 200,000-square-foot center for nanoscience and nanotechnology scheduled to open in 2018.

In 2015, Dresselhaus delivered the keynote address at “Rising Stars in EECS,” a three-day workshop for female graduate students and postdocs who are considering careers in academic research. Her remarks, on the importance of persistence, described her experience studying with Enrico Fermi. Three-quarters of the students in that program, she said, failed to pass rigorous exam requirements.

“It was what you did that counted,” Dresselhaus told the aspiring scientists, “and that followed me through life.”

Dresselhaus is survived by her husband, Gene, and by her four children and their families: Marianne and her husband, Geoffrey, of Palo Alto, California; Carl, of Arlington, Massachusetts; Paul and his wife, Maria, of Louisville, Colorado; and Eliot and his wife, Françoise, of France. She is also survived by her five grandchildren — Elizabeth, Clara, Shoshi, Leora, and Simon — and by her many students, whom she cared for very deeply.

Gifts in memory of Mildred Dresselhaus may be made to MIT.nano, the nanoscience/nanotechnology center scheduled to open in 2018. For more details, visit: annualfund.mit.edu/dresselhaus


Robert “Bob” Fano, a professor emeritus in the Department of Electrical Engineering and Computer Science (EECS) whose work helped usher in the personal computing age, died in Naples, Florida, on July 13, 2016. He was 98.

During his time on the faculty at MIT, Fano conducted research across multiple disciplines, including information theory, networks, electrical engineering and radar technologies. His work on “time-sharing” — systems that allow multiple people to use a computer at the same time — helped pave the way for the more widespread use of computers in society.

Much of his early work in information theory has directly impacted modern technologies. His research with Claude Shannon, for example, spurred data-compression techniques such as Huffman coding that are used in today’s high-definition TVs and computer networks.

In 1961, Fano and Fernando Corbató, professor emeritus in EECS, developed the Compatible Time-Sharing System (CTSS), one of the earliest time-sharing systems. The success of CTSS helped convince MIT to launch Project MAC, a pivotal early center for computing research for which Fano served as its founding director. Project MAC has since dramatically expanded to become MIT’s largest interdepartmental research lab, the Computer Science and Artificial Intelligence Laboratory (CSAIL).

“Bob did pioneering work in computer science at a time when many people viewed the field as a curiosity rather than a rigorous academic discipline,” CSAIL Director Daniela Rus said. “None of our work here would have been possible without his passion, insight, and drive.”

Fano was the Ford Professor of Engineering in EECS and a dedicated teacher who would often labor into the late hours of the morning, working on new lectures. He was also a member of multiple research labs at MIT, including the Laboratory for Computer Science, the Research Laboratory for Electronics (RLE), the MIT Radiation Laboratory, and the MIT Lincoln Laboratory. He helped create MIT’s first official curriculum for computer science, which is now the most popular major at the Institute.

In many respects, Fano was one of the world’s first open-source advocates. He frequently described computing as a public utility that, like water or electricity, should be accessible to all. His writings in the 1960s often discussed computing’s place in society, and predated today’s debates about the ethical implications of technology.

“One must consider the security of a system that may hold in its mass memory detailed information on individuals and organizations,” he wrote in a 1966 paper he co-authored with Corbató. “How will access to the utility be controlled? Who will regulate its use?”

A native of Italy, Fano studied at the School of Engineering of Torino before moving to the United States in 1939. He earned both his bachelor’s degree (1941) and his doctorate (1947) from MIT in electrical engineering, and was a member of the MIT faculty from 1947 until 1984.

During World War II, Fano worked on microwave components at the MIT Radiation Laboratory and on radar technologies at the Lincoln Lab. He also served as associate head of EECS from 1971 to 1974.

Over the years, Fano won many notable awards, including the IEEE’s Educational Medal for teaching and the Claude E. Shannon Award for his work in information theory and microwave filters. He was a member of the National Academy of Sciences and the National Academy of Engineering, and a fellow of the American Academy of Arts and Sciences and the Institute of Electrical and Electronic Engineers.

He is survived by his daughters Paola Nisonger SM ’79, Linda Ryan SM ’82, and Carol Fano, as well as five grandchildren.

Robert Fano Photo: Jason Dorfman | CSAIL

ROBERT FANO, COMPUTING PIONEER, DIES AT 98

Professor emeritus helped launch field of information theory and developed early time-sharing computers.

By Adam Conner-Simons and Rachel Gordon | Computer Science and Artificial Intelligence Laboratory (CSAIL)


Education News

Machine Learning for Just About Everyone 58

The Internet of (Play) Things 60

Talk Science to Me: The EECS Communication Lab 63

58 education CONNECTOR 2017 eecs.mit.edu

On the first day of Introduction to Machine Learning (6.036) in February 2017, the setting looked more like a sold-out

movie theater than an MIT classroom.

At least 700 students converged on the Institute’s largest lecture hall, filling every seat and overflowing into another room. Even after being pared to a more manageable crowd of about 550, 6.036 had higher enrollment than any other introductory course in the Department of Electrical Engineering and Computer Science (EECS) — even larger than Introduction to EECS (6.01).

The surge in interest in machine-learning courses should come as no surprise: It mirrors machine learning’s transformation from a niche technology to a mainstream area of expertise that’s very much in demand. In April 2017, an informal search of Boston-area jobs on Indeed.com, a popular employment website, turned up 805 positions requiring machine-learning experience, more than those seeking candidates with skills in PHP, Perl or robotics. At the same time, Amazon had posted nearly 2,900 jobs companywide for candidates with machine-learning expertise.

Machine learning is an algorithmic approach to data processing that builds models from samples of data sets to describe the data and make predictions accordingly. It’s a core component of technologies such as computer vision, natural language processing and robotics. The technology is used in a growing number of fields, including automation, autonomous vehicles,

education, finance, health care, marketing, politics, and science.

Machine-learning technologies such as neural networks have been in use for decades, but have become widespread only in recent years because powerful and affordable computing resources and large amounts of data are more available, says Tommi Jaakkola, the Thomas Siebel Professor in EECS and the Institute for Data, Systems, and Society (IDSS). He likens the rise of machine learning to the rise of electricity: “It creates capabilities that weren’t there before.”

EECS has had a graduate-level intro course, Machine Learning (6.867) for at least 16 years, says Jaakkola, who originally created that course. It’s been further developed by its current instructors, Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering in EECS, and Devavrat Shah, a professor of EECS. As the field took off, so did interest in 6.867. The class began to include a broader range of students, including some from outside EECS and others who were more interested in the technology’s applications than the theory behind it. “It was a very mixed population with different demands, so at some point it became untenable to maintain that,” Jaakkola says. “We thought we should really create an undergraduate entry-level machine-learning course.”

Thus was born 6.036, co-developed and co-taught by Jaakkola and Regina Barzilay, the Delta Electronics Professor of EECS. To address the needs of graduate students who are more

MACHINE LEARNING FOR JUST ABOUT EVERYONEExpanded offerings for one of MIT’s hottest topics reach a broad cross-section of the student population.

By Eric Smalley | Connector Contributor

Introduction to Machine Learning (6.036) meets in MIT’s largest lecture hall — and professors still had to whittle down the number of students who enrolled for Spring 2017.

Photo: Lillie Paquette/School of Engineering

eecs.mit.edu 2017 CONNECTOR education 59

interested in applied machine learning, especially those from outside EECS, the department also created a parallel graduate course, Applied Machine Learning (6.862). Graduate students in 6.862 attend the 6.036 lectures and do the 6.036 assignments, but also undertake a semester-long project supervised by Stefanie Jegelka, X-Consortium Career Development Assistant Professor of EECS.

The sheer number of students interested in the machine-learning courses reflects both the technology’s widespread adoption and the high level of expertise it demands. Successful practitioners need to understand how to phrase problems as machine-learning problems, know what methods exist, and be able to choose the appropriate method for each problem, Jegelka says.

The growing use of machine-learning technologies has naturally led to an increase in the number of student internships requiring machine-learning skills. The machine-learning courses emphasize hands-on, applied learning — including 6.867, although that course has a stronger theoretical component than the other two. The hands-on approach not only helps students learn the material, but also gives them the skills they need to land and succeed in internships.

Students in 6.867 work with a large data set, design and run algorithms, modify the algorithms, process the data, and see how the different modifications lead to different types of answers, Shah says. This experience helps students outside the classroom; if they find themselves unsure of which method to use for a particular application, they can start with algorithms from the machine-learning class. “One of the strengths of this course is helping students get ready to do something real,” Shah says.

Similarly, 6.036/6.862 teaches students how various machine-learning methods do and don’t work in practice and what issues are related to them, Jaakkola says: “They actually have to code up an algorithm and run it, and investigate results on real types of problems.”

The courses are also helping students outside the department with their research in their fields. Students in 6.862 come from throughout MIT, including from the Departments of Aeronautics and Astronautics, Architecture, Brain and Cognitive Sciences, Chemical Engineering, Civil Engineering, Economics, Mathematics, Mechanical Engineering, and Physics. “These students are defining a project that uses machine learning for their research, so they are working with data from their domain,” Jegelka says. “They learn and experience how to formulate the problem, and which methods may work for it, and sometimes also see where inventions from the machine learning side are needed to capture the problem fully.”

Projects by students in 6.862 involve a vast range of data-driven topics. Examples include:

• Predicting the energy usage of buildings based on building features.

• Making predictions about molecules, including thermodynamic properties, and the function of proteins based on their 3D structures.

• Addressing problems related to autonomous driving, such as learning driving maneuvers from simulation and recognizing the road from sensor data.

• Identifying cells and membranes from brain imaging.

• Analyzing health insurance uptake in developing countries.

• Detecting “fake news.”

• Analyzing transportation policies in Chinese cities.

Ultimately, machine-learning classes are enjoyable for instructors as well, Jegelka says: “It’s a lot of fun and very interesting to work with students on such a diverse set of problems and data.”

“ The surge in interest in machine-learning courses should come as no surprise: It mirrors machine learning’s transformation from a niche technology to a mainstream area of expertise that’s very much in demand.”

The course’s popularity reflects machine learning’s evolution from a niche area of expertise to one that’s in high demand among employers.


Invisible messages fly through the room. With laptops open and microcontrollers at the ready, students are learning how

to code programs that encrypt their messages, typed on the microcontrollers called Teensies, so that only their lab partners can read them.

“It’s like spy stuff,” says freshman Brandon Kramer on an April afternoon. Except for one thing: they’re not exactly dishing in state secrets. His lab partner, sophomore Abby Bertics, divulges that the main topic of their messages has been food.

Kramer and Bertics are taking the introductory course 6.S08 (Interconnected Embedded Systems). Only in its second year, the course is already extremely popular. Last year, 160 students pre-registered, and the instructors ran a lottery to accept 40, said Joel Voldman, professor of Electrical Engineering and Computer Science (EECS). This year 240 students signed-up, but the course could only enroll 180.

Voldman believes the course’s appeal stems in part from its hands-on approach. Week by week, students learn how to piece together the same interconnected components you’d find in a Fitbit: microcontrollers, gyroscopes, accelerometers, magnetometers, Wi-Fi chips, and so on.

“I wish I had had something like this when I was a freshman,” says Joe Steinmeyer, an EECS lecturer and co-creator of the course. “There’s so much stuff out there now. You can go on the Web and just buy these really amazing parts, like a GPS unit or Wi-Fi unit — and you can do a lot with them. But I think many students coming in don’t know where to begin.”

This course and a more advanced one (6.S062, Mobile and Sensor Computing), which also started last year, both help make students more familiar with using and integrating wirelessly connected devices — the so-called “Internet of Things.” The emphases, however, are different. The introductory 6.S08 focuses on building a device, which entails

THE INTERNET OF (PLAY) THINGS In two popular courses, students learn how to program mobile sensors — and savor the connections they make.

By Alison F. Takemura | EECS

Photos: Anne Stuart


putting together hardware and controlling it by programming in languages C++ and Python. The advanced 6.S062 allows students to build more sophisticated software that harnesses data from wireless sensors, such as mobile phones, for different applications. In both courses, students receive the opportunity to apply the skills they learn from lectures and lab exercises to a final, open-ended project. Some submissions are delights, and others, significant novel contributions.

6.S08: Embedded fun

“Before the class, I had no idea how to wire anything,” said Jenny Xu, a sophomore who took the course last year and is now a lab assistant (LA). She loves developing games in her own free time, so, in her final project, she and a partner put together the hardware and software for their own multi-player trivia game. All players could buzz in on their own consoles, the augmented Teensies used in the class, and scores would appear on a leaderboard. It was a rewarding challenge to make a game she could play with friends, Xu says.

Last year, sophomore Kenneth Collins and a partner created a device to answer a proverbial college-student question: Where can you find free food? The device interacted with a server that would go to your Gmail and look at free-food e-mails,” he says. Now an LA, he wants to go a little further with the project, possibly putting it on display in his dormitory, as something of a community service.

One of the instructors’ favorite projects from last year was a skateboard that could give its rider directions. Through use of GPS, the skateboard’s sides would light up to indicate which way to turn at intersections. The board, which the instructors still have, glows beautifully with blue LEDs.

Completing a final project is a winning feature of the course for Carissa Gadson, a sophomore and LA. “A lot of freshmen asked me, ‘Should I should take it? Is it worth it?’ I always say yes, because [at the end] you have something technical that you can explain really well.” Recruiters were surprised that she had worked with databases and servers, along with C++ and Python, she says. “I think that really helped with getting my internship last summer.”

Voldman wants the course to empower students for situations ranging from Undergraduate Research Opportunities Program projects to issues that might come up at work. “We’d like them to have the confidence that, if they approach a UROP or job and their employer says, ‘We have this equipment, and we need you to put an embedded system around it to control it or get the data off it,’ they feel like, ‘Yeah, I can do that,’” he says.

“The field is changing so fast,” Steinmeyer adds. “A lot of future engineering is going to be engineering across multiple systems.” It’s not going to be just one clean sandbox language like Python, for example, but Python, Javascript, and HTML, all mashed together, he says: “We’re really trying to give students experience integrating across multiple environments.”

At the time of this writing, current students haven’t gotten to the projects yet, but they’re still enthused. “There aren’t many classes where you’re just building cool things all the time,” Bertics says. “It’s my favorite class this semester.”

6.S062: The software side

Geared toward juniors and seniors, 6.S062 looks at how to use mobile sensors from a higher level than 6.S08: through software.

Software is what allows users to extract data from local and remote sensors — increasingly ubiquitous technologies. “So much of what’s happening in the world today involves sensing and phones and mobile devices,” says Sam Madden, professor of EECS and course co-founder. “There really are some specialized techniques that people should know if they’re working in those environments.” One technique is the Viterbi algorithm to solve a Hidden Markov model, which can be applied to a seemingly simple problem: how to figure out which road a car travelled along, using only a trace of GPS signal collected from the car’s driver.

“This problem can actually be very challenging,” Madden says. He flips open his laptop and pulls up a video of red dots moving along streets in Boston. Each dot is a GPS signal, most likely coming from a phone. “You squint at it, and you’re like, ‘Oh yeah, these match onto the roads pretty well.’ But then you see there’s weird stuff.” He zooms in and dots are sometimes on sidewalks or in the Charles River. Some areas have too little data to tell whether there’s actually a road there or whether the results are being confounded by a rogue pedestrian.

“ How do you build a network of devices that communicate with each other? Sensors may need to last for months at a time in a remote environment, how can you ensure their batteries don’t run down really fast?”

—EECS Professor Sam Madden, cofounder of 6.S062.


Sixty-six students from 14 MIT departments have declared that they will minor in computer science, taking advantage of a new offering that debuted this past fall.

Designed to enable students to earn credentials in computer science while they pursue other majors, the computer science minor ensures that graduates learn the fundamentals of programming, algorithms, and discrete mathematics. To complete the minor, students must take six subjects in Course 6, including four required courses in the fundamentals and two electives, at least one of which must be at an advanced level, typically within either artificial intelligence and/or theoretical computer science.

“The six courses in the minor program provide a thorough-going introduction to computer science,” says Chris Terman, the undergraduate officer for MIT’s Department of Electrical Engineering and Computer Science (EECS).

Terman noted that before the minor was developed, the only way to earn computer science credentials at MIT was to major in 6-2 (Electrical Engineering and Computer Science), 6-3 (Computer Science and Engineering), 6-7 (Computer Science and Molecular Biology) or 18C (Mathematics with Computer Science). The Minor in Computer Science is open to all undergraduates except those in courses 6-1 (Electrical Science and Engineering), 6-2, 6-3, 6-7, 7 (Biology), and 18C.

Students enrolled in the new minor thus far hail from a wide range of departments at MIT, including Aeronautics and Astronautics, Civil and Environmental Engineering, Mechanical Engineering, Mathematics, Economics, and Management.

As of spring 2017, there were 27 sophomores, 26 juniors, and 12 seniors in the degree program. However, more people may actually be pursuing the minor, because students may wait until their senior year to declare a minor, Terman notes.

Overall, the launch of the new minor has been a success, Terman says: “Use of online advising and progress-monitoring has helped keep the administrative burden low, so the minor program is serving a new cadre of students without adding substantially to the department’s advising and teaching load.”

For more information on the new computer science minor, visit eecs.mit.edu/csminor.

New Computer Science Minor Attracts Students from Across the Institute

When Madden changes the video’s focus to the Massachusetts Turnpike running beneath Boston’s Back Bay neighborhood, the GPS dots drop out — because GPS doesn’t work indoors or underground. And then in Copley Square, the dots get rowdy as GPS signals reflect off the Hancock Tower.

Road mapping this kind of real data is one of many problems students can tackle in the class, Madden says. “There’s multi hop communication. How do you build a network of devices that communicate with each other? Sensors may need to last for months at a time in a remote environment, how can you ensure their batteries don’t run down really fast?”

Tackling these problems gives students a foundation to build final projects of their own design — a satisfying experience, says Natasha Consul, a senior who took the course in 2016. “When you have that freedom and independence, that’s when you learn the most,” she says.

Three students — Geronimo Mirano, now a master’s of engineering (MEng) student, and Eric Lau and Harihar Subramanyam, who have both since graduated — solved a tricky problem for their final project. GPS works well outdoors, but because it doesn’t penetrate walls, the team created a solution for indoor travel, such as in a mall or a museum. Their software used noisy video data (from stationary surveillance cameras) and inertial data (from an individual’s phone) to help people find their bearings. Madden and course co-founder Hari Balakrishnan, the Fujitsu Professor of Electrical Engineering and Computer Science, call those results exciting. “We encouraged them to publish,” Madden says.

Mirano has been too busy with his master’s work to pursue a paper at the moment. But the class left a lasting impression. It showed him there were “really cool problems to be found in these technologies,” and that the same issues pop up in a variety of systems, he says. For example, Fitbit and Google maps both grapple with inertial data to figure out trajectories. The robots he programs in his research must similarly extract useful, low-bandwidth information from noisy, high-bandwidth sensors, he says, adding: “It’s all connected.”


In the spring of 2015, graduate students communicated a clear message to the Department of Electrical Engineering

and Computer Science (EECS): They wanted help communicating.

Specifically, they wanted to give better pitches for research and startup ideas and make presentations that wowed their colleagues and senior scientists. They also wanted to impress recruiters, who, mentors said, always saw plenty of candidates with technical skills; it was the applicants with strong communication skills who really stood out from the pack.

Students were particularly stressed during conferences, when they realized their talks weren’t what they could be, recalls Samantha Dale Strasser, a PhD candidate in EECS, who was among the graduate students who provided the 2015 feedback. “Coming from MIT, we really want to be not only at the forefront of science, but also the forefront of communicating that science,” she says.

In response, the department launched two initiatives: the EECS Communication Lab, a peer-coaching resource, and a new lab-supported class, Technical Communication (6.S977). By all accounts, both initiatives have succeeded, resulting not only in improved posters and pitches, but in a stronger department-wide awareness of the power of effective communication as well.

The Comm Lab, as it’s affectionately known, employs graduate students and postdoctoral associates from across EECS to serve as peer coaches. They’re trained in strengthening their own communication skills, including how to consider their audience and purpose, motivate their research, and create a narrative, rather than a litany. Then these skilled communicators, or communication advisors, are ready to provide advisees with one-to-one help. Advisees might be anyone in the department, including undergraduates, graduate students, and postdoctoral associates.

“The Comm Lab is a great resource,” says Priyanka Raina, a PhD candidate in EECS. She consulted the lab for a wide range of assignments: a conference paper, a presentation, her resumé, and a faculty package. “It helped me a great deal,” she says. “All the assignments that I worked on with the lab were accepted or saw positive results. I even got an interview with a top university.”

The EECS Comm Lab is the latest installment of the Communication Lab program, a School of Engineering (SoE) resource, affiliated with the Gordon-MIT Engineering Leadership Program. The Departments of Biological Engineering and Nuclear Science and Engineering also have their own communication labs. The model has expanded quickly because it serves students when they need it most, notes Jaime Goldstein, the program’s former director.

“Early scientists need to get funding, get a job, go to conferences, and meet collaborators,” she says. “We insert ourselves at just that right moment with just the right information. And peer coaches know how to ask the right questions because they’re insiders in the field. It’s a real recipe for success.”

Faculty members agree. In addition to that first Technical Communication class, the Comm Lab has hosted workshops and supported other courses. In January 2017, the Comm Lab provided a training session for graduate students presenting at the Microsystems Technology Laboratories’ (MTL) Microsystems Annual Research Conference. “Industry members and faculty commented that the quality of pitches showed marked improvement this year,” says Ujwal Radhakrishna, a postdoctoral associate in EECS who organized the conference.

Research abstracts and presentations in Introduction to Numerical Simulation (6.336) have also been notably clearer

TALK SCIENCE TO ME The EECS Communication Lab offers open-source, real-time, peer-to-peer help for students and postdocs.

By Alison F. Takemura | EECS Photo: Gretchen Ertl


than in the past. “The abstracts felt a lot better organized, with engaging motivations, detailed concise methods and results descriptions, and thoughtful considerations at the end,” says Luca Daniel, the EECS professor who instructed the Comm Lab-supported class. “The presentations were also more accessible to a wider audience. My class has students from 12 different departments, so that’s essential.”

Daniel wasn’t the only one enthusiastic about the Comm Lab results in his course. When he asked whether he should again use the resource in his course, his students responded with an emphatic “yes,” he says. Students also suggested adding midterm deadlines, in addition to deadlines for final abstracts and presentations, to encourage even earlier visits to the Comm Lab. “They love the fact that it is other students helping them,” Daniel says.

Diana Chien, the new director of the SoE-wide Communication Lab program, understands the appeal. “In technical communication, you really can’t separate the science or engineering from the communication, so our advisors are ready to tackle both at once,” she says. When EECS clients visit the Comm Lab to, for instance, work on conference presentations with communication advisors, they’re really connecting with peers — people who are “as ready to parse details about the design of a machine-learning algorithm as they are to ask strategic questions about audience and storytelling,” Chien says.

Chien and the communication advisors also created an online resource, the CommKit, to guide students through several common communication tasks, such as a cover letter or a National Science Foundation (NSF) application. If an impending deadline precludes students from meeting an advisor in person, help is still just a click away.

The Comm Lab’s popularity is mounting. Since September 2016, it has provided more than 250 appointments with 150-plus advisees. More than 270 students attended workshops on posters, pitches, thesis proposals, and the Research Qualifying Exam (RQE). Feedback from the Comm Lab’s first annual

survey showed that of the respondents who had visited the lab, all would recommend it to a friend. And while many students and postdocs haven’t yet used the lab, more than three quarters of non-users surveyed indicated they were still glad that EECS offers the service.

“The enthusiastic and sustained interest from students and faculty tells us the program’s doing exceptionally well,” says Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science and EECS department head. “I expect the Comm Lab will become a staple resource in the department.”

Skills taught in the Comm Lab have a clear professional impact, says Chris Foy, a PhD candidate in EECS who took the communication course and is now a peer coach. He ranks the Technical Communication class as one of his favorites at MIT, in part because it taught him how to focus on building a rationale or a narrative about his research. “Being able to do this is crucial as a scientist because there are so many problems that are, in theory, worth solving,” he says. “But if you can’t construct a story around why you chose this problem,” he adds pointedly, “then why are you solving it?”

Joel Jean, a PhD candidate in electrical engineering, credits his communication-advisor training with helping him clearly explain his vision for working on thin-film solar cells to help address climate change. That effort paid off: Jean won one of MIT’s most prestigious graduate awards, the Hugh Hampton Young Fellowship. “My return on investment from working with the EECS Comm Lab as an advisor has been extraordinarily high,” he says. “And I expect its value, both for me and for students in the department, to keep growing.”

Editor’s Note: Alison F. Takemura is the administrator for the EECS Communication Lab. To learn more: SoE Communications Lab: mitcommlab.mit.edu EECS Communications Lab: mitcommlab.mit.edu/eecs

“ In technical communication, you really can’t separate the science or engineering from the communication, so our advisors are ready to tackle both at once.”

—Diana Chien, Director, SoE Communication Lab

Trained student advisors coach their peers in the EECS Comm Lab. Photo: Alison F. Takemura

Alumni News

Michal Depa: An Innovation ‘Ecosystem’ for Better Health Care 66

Dario Gil: On the Cutting Edge of the Cutting Edge 68

Philip Guo: Making Programming Accessible for All 70

Cal Newport: Dual Careers 72

Martin F. Schlecht: Life Beyond MIT 74

Lisa Su: An Industry Leader Returns to MIT 76

Margaret Guo: Swimming Toward Success 78

66 alumni CONNECTOR 2017 eecs.mit.edu

Many consider the smartphone a tool. Some regard it an annoyance. For Michal Depa, SM ’11, the founder

and CTO of a start-up called Jana Care, the smartphone is a platform for delivering life-saving diagnostic tools to underdeveloped countries at a minimal cost.

Inspired by his medical-technology work in MIT’s Department of Electrical Engineering and Computer Science (EECS), Depa has developed the Aina Device (the name is based on the Sanskrit for “mirror”). The device attaches to a smartphone or a tablet and can measure a person’s glucose level, lipid profile, and other blood parameters from a drop of blood on a test strip. The Aina Device was conceived as a way to monitor diabetes — a crucial issue for countries such as India, which has 61 million diabetics, many of whom lack access to quality medical care.

It can also be used for other blood tests for which analyzers can cost up to thousands of dollars in the United States. The Aina Device, however, only costs about $20 to make. The device is now marketed as part of Jana Care’s “ecosystem” of medical technologies and software aimed at providing emerging markets with high-quality innovations.

“The top end of the health care system in India is on a par with the United States, as measured by health outcomes,” says Depa, who divides his time between Boston and Bangalore. “But that top tier is small; most of the country’s health care is not like that. Devices made in the U.S. can make it to the top of the Indian health care system, but getting them across the whole health care system is difficult, mainly due to cost.”

Depa’s goal for Jana Care is to produce technologies that span the breadth of health care systems, serving more than that top tier. And, he adds: “If you can develop something in an emerging market, then you can sell that

AN INNOVATION ‘ECOSYSTEM’ FOR BETTER HEALTH CAREJana Care, co-founded by EECS alumnus Michal Depa, focuses on high-quality, low-cost solutions for improving diagnostics in emerging markets.

By Stephanie Schorow | Connector Contributor

Michal Depa

eecs.mit.edu 2017 CONNECTOR alumni 67

product in the U.S. This is the thesis some people call ‘reverse innovation.’”

Depa has been intrigued with reverse innovation since college. Born in Poland and raised in Montreal, Depa studied electrical engineering and researched telecommunications at McGill University, and came to MIT as an undergraduate exchange student on a Killam Fellowship in 2007. He became interested in medical technology while earning a master’s degree in electrical engineering and computer science from MIT. “I found it was a good way to use technology to improve people’s lives,” he says. He worked on developing algorithms for analyzing cardiac images captured with MRI machines. As a volunteer with the Computer Science and Artificial Intelligence Laboratory (CSAIL) Sana Mobile group, he helped create an oral-cancer screening app for health workers to use in India.

Depa was intrigued with serving patients who lacked access to expensive equipment such as, for instance, the MRI machines. He was also impatient with the long lag time between coming up with an idea and bringing it to market. “The approach in academia is that you try to solve a problem in a way that no one has done before and publish it,” he says. Researchers thus shy away from simpler solutions that are similar to what others have done. But simplicity was what intrigued Depa.

In late 2011, he and Sidhant Jena, a Harvard Business School student, launched Jana Care (“jana” is the Sanskrit word for “people”) to deliver tools for affordable diabetes management care. The first product was the Aina Device, which has an innovative proprietary design but was built with mostly off-the-shelf components to perform several blood tests at a lower cost than existing analyzers. A smartphone or tablet provides the screen, while Wi-Fi connectivity means the results can be uploaded and stored. The Aina Device offers health-care professionals point-of-care tests for HbA1c, glucose, creatinine, hemoglobin, and the lipid profile (which measures cholesterol and triglyceride levels).

Rather than completing his PhD, Depa decided to focus on building Jana Care. “I wanted whatever I was doing to get out there sooner,” he says.

Now a 75-employee, for-profit company, Jana Care is funded by private investments and grants. It has advisors from several highly-regarded institutions, including Massachusetts General Hospital, and partnerships with insulin-maker Biocon and pump manufacturer Medtronic. With the help of these partnerships, Jana Care has shipped about 3,000 Aina Devices and directly reached 150 primary-care clinics. The company has also created the “Habits” app as an educational and coaching tool to help patients control diabetes and manage other related health conditions. Plans are underway to test and market devices that will help patients with other chronic conditions.

Depa may have left MIT before earning his PhD, but he emphasizes that he doesn’t consider himself as a risk-taker. He believes that no one with a degree from MIT should hesitate to launch or join a startup. “If anything, this will benefit your career,” he says. “MIT does live up to its reputation” — meaning the degree matters in the larger world — so when it comes to entrepreneurship, “you should go for it.”

Depa has developed a device that attaches to a smartphone or a tablet and can measure a person’s glucose level, lipid profile, and other blood parameters from a drop of blood on a test strip.


Here’s a thought experiment inspired by an interview with Dario Gil, SM ’00, PhD ’03, the vice president for science

and solutions of IBM Research:

Two researchers are spiritedly discussing a computing problem when one of them opens his laptop computer to check a formula — and the conversation grinds to a halt. The researcher now has access to information, but the en-gagement has been disrupted.

But what if the laptop simply joins the conversation? What if it verbally explains the information? Or outlines an analysis? Or even begins to debate the two researchers about the solution?

While most of us now think primarily of computers as processors of information, Gil sees them as potential collaborators.

“Imagine a future in which we are talking to each other and the computer system is also collaborating with us,” he says. “To me, it has always been very interesting to see the asym-metry of how much we expect of computing when we’re alone and how little we expect of it when we are together.”

Someday, however, users will compute together “not as a network in front of our computers over the Internet — we

know how to do that already — but when we are physically engaged with each other, without screens in front of us.”

If there is a cutting edge to the cutting edge, Gil walks it at IBM Research, where he oversees an expansive science agenda that includes the physical sciences, the mathe-matical sciences, and health care and the life sciences. He speaks with energy and passion about advances in ambient, ubiquitous computing as well as in artificial intelligence and cognitive systems.

While he directs a global organization of some 1,500 re-searchers across 11 laboratories, Gil also spends a few hours a day working with his quantum computing team. “It’s healthy for leaders to continue to be deeply involved in some particular area that you manage because it anchors you and feeds you with energy,” he says.

This love of hands-on research can be directly linked to Gil’s MIT days, when he worked in the nanotechnology laboratory of EECS Professor Emeritus Henry “Hank” Smith. There, Gil had his first lab experience: creating knowledge, rather than just learning it. “That was intoxicating,” he recalls. “My imagination was captured by the nano world — the world we could not see.”

ON THE CUTTING EDGE OF THE CUTTING EDGE

At IBM, EECS alumnus Dario Gil directs a global research team with an ambitious science agenda.

By Stephanie Schorow | Connector Contributor Photo: IBM


Indeed, Smith remembers Gil as an “energetic, friendly graduate student” who was “noticeably helpful to others and unusually creative in the way he approached problems in the lab.”

In the lab, Gil and other graduate students developed and demonstrated a new system for doing nanolithography, Smith says. That new system employs an array of 1,000 diffractive-opitical microlenses and writes patterns much faster and with greater precision than other forms of nanoli-thography. “We patented various elements of the technology and spun off a small company, LumArray, Inc.,” Smith says. That company is still operating in Somerville, Mass., provid-ing special lithography services.

The work is an example of how Gil, as Smith puts it, “takes in a comprehensive view of the role of technology in the larger world and how technology can both create problems and solve them.”

Among the key factors in the development of Gil’s perspec-tive was his international experience as a youth. Born in Spain as the youngest of four brothers, Gil grew up spending his summers learning languages in Ireland, France and Ita-ly. He spent his senior year of high school at Los Altos High School in California, which he considers a pivotal aspect of his life. After graduating as the valedictorian of Stevens Institute of Technology in Hoboken, N.J., he came to MIT in 1998 to study for a master’s degree and PhD.

“The part that I have always admired about MIT is its can- do culture and the quality with which it integrates theory and practice,” Gil says. Most telling to him is the ability of MIT to produce so many alumni that years and decades later continue to work in engineering and science, their passion undimmed.

In 2003, he graduated from MIT, joined IBM, and had his first child. His advice: “Try not to combine all that within a year.” He now has two girls.

Unlike many of his fellow alumni, Gil has worked for just one company since graduating. When he joined IBM, he con-tinued working in nanofabrication. He led the team that built the world’s first microprocessor with immersion lithography in 2004. He later moved into industry solutions, exploring smart grids and energy and then into artificial intelligence and cognitive solutions. As director of Symbiotic Cognitive Systems, he was responsible for the design and creation of three pioneering laboratories and experiential centers: the Cognitive Environments Laboratory, the IBM Research THINKLab, and the IBM Watson Experience Center.

“Over the last 14 years, I have had a chance at multiple careers,” he says. “Sometimes you do that with different companies. I get to do them inside IBM.”

Currently, he describes himself as “very, very excited right now about quantum computing.” Last year, IBM Research installed a 5-qubit quantum computer in the cloud. “Now we have 45,000 users from 140 countries who are learning about quantum computing,” he says. “I’m really passion-ate about our technical and science community around the world engaging with this topic.”

Gil’s vision for computing, in fact, seems boundless: Can we build machines that are persuasive? That can convince us? That we can debate with? And that can help solve the world’s problems?

Smith answers the last question this way: “I have met with Dario on a number of occasions for the sole purpose of exchanging ideas on the great problems facing the world and what role a company like IBM can play in helping to solve them: global warming, failed states, and the refugee problem, food supply, the role of the Internet in providing education to the Third World and in combating misinforma-tion and radicalism, and the role of social media for good and otherwise.”

Gil and his team are also involved with the intersection between genomic diagnosis and advances in artificial intelligence that could drive a new level of personalized medical treatment. IBM recently announced a partnership with Illumina and Quest Diagnostics in which DNA sequenc-ing can be imputed directly into Watson Genomics to create specific recommendations, such as a tailored treatment for a specific tumor. Or a person’s DNA would be sequenced to try to match it to a clinic trial to improve outcomes.

As someone at the forefront of artificial intelligence, Gil remains bemused by the tendency to frame debates about new technology as a clash between two extremes: robots take over the world on one hand and computers create uto-pia on the other where we can all lie on the beach all day.

“I understand why people like to frame it that way,” he says. “It’s catchy, right? It provokes a reaction. But I don’t think either frame is most illustrative of the path that lies ahead.” He advocates a nuanced perspective that technology will progress not because of the fancy new gadget we might build, but “the choices we make as a society.”

While most of us now think primarily of computers as processors of information, Gil sees them as potential collaborators.


If Philip Guo were a superhero, his name might be “The Influencer.” Guo, SB ‘05, MEng ‘06, now teaches and

conducts research at the University of California at San Diego, but his impact has been felt far beyond classrooms and labs. He has helped millions of people worldwide learn how to program, eased the fears and frustrations of thousands of doctoral students, and given thousands of people valuable insights into stereotypes and biases in the computing field.

Guo is an assistant professor in the UCSD Department of Cognitive Science, where he teaches human-computer in-teraction and conducts research on human factors, distance learning, and computing education. The path that led him there began with a childhood dream of studying computer science at MIT, and wound through an MEng thesis on tools for programmers, a doctoral thesis at Stanford on tools for data scientists, visiting researcher positions at Google, edX and Microsoft, and a postdoctoral position back at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

Programming and data science are important for all col-lege students, including those majoring in liberal arts, journalism, fine arts, and design, Guo says, adding: “That’s reflected in the job market as well.” However, many people shy away from the field because of its perceived difficulty, as well as stereotypes about programmers. “The popular im-age is of young people in hoodies crouched over a computer screen and being antisocial,” he says.

Guo has dedicated himself to overturning these popular perceptions and making programming accessible to as many people as possible. One recent research paper tackles that misconception about programming only involving those hoodie-clad millennials. Instead, Guo studied adults aged 60 to 85 to uncover the cognitive and social challenges they face in learning how to program. The result was a proposal for a set of tools and techniques tailored to the needs of older adults.

The theme of Guo’s research is developing scalable ways to help people learn computer programming and data science. The centerpiece of this work is Python Tutor, a tool that allows people to write code in a browser and see automat-ically generated diagrams that illustrate what their code does. The tool has its roots in the software Guo developed for his MEng thesis that analyzes C and C++ code to let pro-grammers see whether the code is running as expected. In addition to Python, the Python Tutor now works with Java, C, C++, Ruby, JavaScript, and TypeScript. “The C and C++ part of my visualizer tool actually uses a lot of the same ideas from my MEng thesis,” he says.

Python Tutor, which is free and open-source, is widely used in massive open online courses (MOOCs), traditional college courses and e-textbooks, Guo says. By his estimate, Python Tutor has, so far, been used by more than 3.5 million people in more than 180 countries to visualize more than 30 million lines of code.

Philip Guo

MAKING PROGRAMMING ACCESSIBLE FOR ALL Computer science alum Philip Guo aims to lower the barriers to learning programming and data science.

By Eric Smalley | Connector Contributor


In fact, Python Tutor has been the source of Guo’s biggest impact, says Rob Miller, a professor of computer science who was Guo’s postdoc advisor at MIT. The Web is full of tutorial sites and novice programming systems, but Python Tutor is unique because it offers a window inside the ma-chine, automatically drawing pictures similar to those that a good instructor would draw on a blackboard, Miller says. Learning the skills of visualization and mental execution are critical to understanding how programs behave, he adds. “Every good programming teacher draws these kinds of pictures. Philip’s is the first work I’ve seen that can create them automatically, for hundreds of simultaneous users, for every major language that people are trying to learn,” Miller says. “That’s a tremendous benefit to the world.”

Beyond his core research and teaching, Guo has served as an informal mentor to thousands of PhD students, in-cluding many in fields far removed from computer science, by way of a virally popular e-book about his own experience earning a doctorate. The PhD Grind is a personal narrative of that six-year journey, which he wrote shortly after completing his degree. Guo says that, unlike numerous other works offering “how-to” advice for PhD students, his book allows people to identify with him. “I think it’s a mirror neuron thing,” he says. “People build empathy and find a way to commiserate.”

Guo has also pointed a spotlight at biases in the computing field, particularly those that hinder female and minority students. He wrote a blog post, Silent Technical Privilege that detailed the advantage he gained from the stereotype of Asian males as skilled programmers, and contrasted his experience with those of fellow students who didn’t fit that stereotype. NPR and Slate picked up the blog post, and he has since contributed to research on barriers confronting female programmers, co-authoring a paper about the chal-lenges they face when they contribute to online forums.

Guo’s current research is aimed at bringing the same types of tools he’s developed for learning programming to the field of data science. Just as his programming visualization tool had its roots in his MEng thesis, this line of research builds on his PhD thesis, which helped researchers boost the productivity of their data analysis workflows. Guo’s goals are to help people learn to work with multiple programming languages; develop tutorials to help people learn about data quality, numeracy, statistics, machine learning, and experimental design; and determine whether these types of tutorials can help novice data scientists avoid common experimenter biases, statistical misconceptions, and erro-neous data interpretations. “The impact of this will be even bigger than programming because there are going to be many more people who do data analysis and data science than who are computer programmers,” he says.

Whatever challenges Guo tackles in the future, his MIT ex-perience has prepared him with more than just a thorough,

well-rounded computer science education. It also taught him the value of working with motivated and energizing faculty and students. “What MIT really brought to the table was providing a very intensive and passionate work envi-ronment,” Guo says. “That has really long-lasting effects because, even years later, I’m able to have this determina-tion and focus and work ethic that I and many of my peers developed during those years at MIT.”

See Python Tutor at pythontutor.com. Read the PhD Grind Blog at phdgrind.com and the Silent Technical Privileges blog at pgbovine.net/tech-privilege.htm. (Editor’s Note: Philip Guo is unrelated to Margaret Guo, profiled elsewhere in this publication.)

The theme of Guo’s research is developing scalable ways to help people learn computer programming and data science. The centerpiece of this work is Python Tutor, a tool that allows people to write code in a browser and see automatically generated diagrams that illustrate what their code does. The tool has its roots in the software Guo developed for his MEng thesis at MIT.


Calvin “Cal” Newport, SM ’06 and PhD ’09, doesn’t see the world the same way that many of his peers do in today’s

connected, Googling, and multi-tasking workplaces. Where many see productivity, he sees disorganization. Where others see communication, he sees distraction.

Not that Newport, Provost’s Distinguished Associate Professor of Computer Science at Georgetown University, is anti-technology. Nor that the author of several best-selling business

books opposes workplace innovation. Rather, Newport casts a suspicious eye on the very tools — e-mail, smartphones, Slack — that are supposed to make us more efficient.

Here’s how he provocatively puts it in his popular Study Hacks Blog: “As a distributed algorithm theorist … when I encounter a typical knowledge economy office, with its hive mind buzz of constant unstructured conversation, I don’t see a super-connected, fast-moving and agile organization — I instead see a poorly designed distributed system.”

What gives weight to Newport’s words are his accomplishments before, during, and after his years studying computer science at MIT, and his dual career as a computer scientist and book author. A casual observer might think that he is a 24/7 multi-tasker who rarely takes a break from the computer screen. Instead, Newport professes to live by the idea he espouses in his most recent book, Deep Work: Rules for Focused Success in a Distracted World (Grand Central Publishing/Hachette Book Group, 2016). He keeps — more or less — normal work hours and avoids distractions such as social media and even e-mail. To spend time with his two- and four-year-old boys, he doesn’t work in the morning or evening.

“To get what I need to get done just during normal work hours really does require that I’m very focused,” says Newport, who lives in the Washington, D.C., area. That means that when he works, he works with deep concentration in intense blocks

THE DUAL CAREERS OF CAL NEWPORT Alumnus balances two different worlds: teaching computer science and writing business bestsellers.



of time. “That’s in part why I don’t have any social media,” he says. “I don’t Web-surf. I’m hard to reach. That’s because I only have so much time if I’m going to produce the stuff that I need to produce. I really need to spend that time locked in.”

Newport, a boyish-looking 34-year-old, has been “locked in” since his high school days near Princeton, N.J., where he and a friend launched Princeton Web Solutions, a dot-com era Web development and sourcing company. He went on to attend Dartmouth College; before graduating summa cum laude with a degree in computer science in 2004, he had already written his first book.

“I arrived at college having read lots of business books. You need them when you’re running a business,” Newport recalls. At that point, he wanted to learn more about succeeding in school, dealing with student loans, and similar issues. “Back then, you couldn’t find a real, serious advice book for college students. Everything was written to be fun and approachable,” he recalls. “I wanted a book that said, ‘OK, here’s how you get good grades.’”

Not finding what he sought, he conducted interviews with national and international scholarship winners and used the material to write How to Win at College: Surprising Secrets for Success from the Country’s Top Students (Three Rivers Press, 2005). Other books followed, including How to Become a Straight-A Student (Three Rivers Press, 2006), which was based on interviews with 50 straight-A students. A few years later, he entered the business book market with So Good They Can’t Ignore You: Why Skills Trump Passion in the Quest for Work You

Love (Grand Central Publishing/Hachette Book Group, 2012).

Each book reflected a stage in Newport’s life. “I think I write the book I need, not the book that I think I need to tell people about,” he says. He wrote So Good “not because I had some great answers I wanted to share, but because I wanted an excuse to do the research to get an answer for myself.”

He balanced book-writing with his research at MIT. From 2004 to 2009, he was a research assistant and teaching assistant in the Theory of Distributed Systems Group at the Computer Science and Artificial Intelligence Lab (CSAIL). From 2009 to 2011, he was a postdoctoral associate in CSAIL’s Networks and Mobile Systems Group. Around that time, he started a blog he continues today. After graduating from MIT, he became an assistant professor of computer science at Georgetown University in 2011; he was named to the distinguished associate professorship in February 2017.

At MIT, Newport trained in an environment that required intense concentration; he saw a tangible connection between the ability to concentrate and quality of output. That led to development of the ideas that would become Deep Work, a Wall Street Journal business bestseller and an Amazon Best Book of 2016 Pick in Business and Leadership. “Deep work,” according to Newport, is “the ability to focus without distraction on a cognitively demanding task. It’s a skill that allows you to quickly master complicated information and produce better results in less time.”

Deep work is done without the checks that most people do throughout their day: a quick glance at the inbox; a quick glance at the phone. “We know from the research and experience that these quick checks actually significantly reduce your cognitive capacity,” he said. This is also why Newport wants to tell people they are “allowed” to stop using social media. “I don’t use it. I find it’s too addictive for me,” he says. “It’s going to take me away from the things I really care about.”

If you think you can’t live without clicking on your Twitter feed, Newport offers this insight: “Deep work is a trainable skill. Most people think about intense concentration like a habit, like flossing their teeth, something they know how to do; they really just need to make some more time to do it. The reality is it’s much more like a skill, like playing the guitar.” In other words: If you haven’t been practicing, you won’t be very good at it.

Deep work also has applications for MIT undergraduates, in Newport’s view. “You need to do less, and do what you do better. That’s actually the formula for both success in your academic life and also in terms of your own personal health, satisfaction, and happiness,” he says.

“When I was in college, for example, I didn’t double-major, I didn’t triple-major. I didn’t join 15 clubs. I did computer science and I wrote. And those have consistently been my two things. I try to do those things as well as I can.”

Visit the Study Hacks Blog at calnewport.com/blog

At MIT, Newport trained in an environment that required intense concentration; he saw a tangible connection between the ability to concentrate and quality of output.


As CEO of SynQor, Martin F. Schlecht oversees one of the world’s leading suppliers of power electronic

products. Yet the MIT alumnus might easily have spent his entire career in MIT’s Department of Electrical Engineering and Computer Science (EECS).

Schlecht earned five degrees (SBEE, SBME, SMEE, EE, and ScD) from the Institute and then spent 15 years on the EECS faculty. What prompted the full professor to leave and start a company? “I wanted to do something new,” Schlecht says. “Having spent all my time in academia — I was 43 years old — I decided I wanted to learn something about business.”

Schlecht also saw an opportunity: a market need for more efficient DC-to-DC converters to meet the rising demand for logic circuits that operated on lower and lower voltages, a key component in telecom and datacom equipment, as well as elsewhere.

Schlecht envisioned a way to significantly improve the efficiency of such converters using synchronous rectification — a process that converts AC to DC in synchrony with changes in the polarity of the power circuit waveforms — in a particular manner. (While DC-to-DC converters begin with DC input, they use power switches to provide AC waveforms to the isolation transformer. For that reason, the secondary-side AC waveforms need to be rectified or converted back to DC to power such components as logic circuits.)

In the 1990s, most DC-to-DC converters were only about 80 percent efficient; they lost the rest of the energy to heat. By solving some of the technical challenges involved in building DC-to-DC converters using synchronous rectifiers, SynQor was able to produce converters that were 90 percent efficient and therefore didn’t need heat sinks or the special construction techniques that provided thermal connection from the power circuit components to those heat sinks. As a result, the company’s converters were smaller, lighter, and easier to fabricate while also providing a higher level of quality and reliability than what was then standard in the industry, Schlecht explains.

“Synchronous rectification was a known idea in a general sense, but it wasn’t adopted in the industry because it was very complex to implement, particularly in isolated converters,” Schlecht says. “What I was able to see — through my connections at MIT — was that there was a fast change in the need for higher efficiency as the voltages needed to power logic circuitry quickly dropped from the 5-volt standard to values below 1 volt.” At that time, he adds, he began to focus on developing power circuit topologies and architectures that were best suited to address the complexities of implementing synchronous rectifiers.

“During Marty’s work with me in his doctoral program, our research group was working at the cutting edge of high-frequency power electronics technology,” recalls John Kassakian, professor of electrical engineering in EECS.

LIFE BEYOND MIT A five-time MIT alumnus reflects on his transition from academia to industry — and what it’s like to leave a tenured EECS professorship to become a high-tech entrepreneur.

By Kathryn O’Neill | Connector Contributor

Martin F. Schlecht, former EECS professor, at SynQor headquarters


“It was Marty’s creativity and engineering skill that was largely responsible for our achieving what, at the time, was a record power-conversion density. I take pride in his having leveraged that work into a very successful, U.S.-based, power supply company.”

Schlecht launched SynQor in 1998 to provide his novel DC-DC converter technology, and he has since guided the company’s continual growth and diversification. Today, SynQor supplies thousands of products — not only DC-DC converters but also AC-DC power supplies, inverters, uninterruptible power supplies, and filters — to industries ranging from telecom to aerospace and from health care to the military.

Fortunately for Schlecht, MIT made it easy for him to take the first step toward entrepreneurship: he received a two-year leave of absence to start his company. “That was a safety net,” Schlecht says. “The real challenge was when the two years were up. Leaving MIT then was a tough decision.”

However, the telecom industry was booming in the late ‘90s, and SynQor had a technological edge. So at first the risk seemed quite manageable, Schlecht says: “Everyone thought the sky was the limit.”

The fledgling company was put to the test just about six months after Schlecht left MIT, when the telecom bubble burst. The industry’s collapse was “some 10 times bigger than the

better-known dot-com crash” of the early 2000s, according to The Economist. “The next five to six years were tough for everybody in our industry,” Schlecht recalls. Big companies struggled, and many startups went out of business.

Schlecht met these early challenges with the same problem-solving approach he’d grown familiar with at MIT: “I was able to bring my MIT education and cultural philosophy to bear to analyze the situation, and eventually help us grow.”

Recognizing that technological advantages don’t last forever — “Your competitors will eventually discover what you’re doing, and all existing players will have comparable products” — Schlecht credits SynQor’s survival in part to its business strategy and problem-solving culture.

These factors led SynQor to what Schlecht views as the company’s key competitive advantage today: lean, responsive

manufacturing. “After the bubble burst, we placed a lot of emphasis on offering not just technology but high quality, reliability, and manufacturing responsiveness,” he says. “I am very proud of our technology. But in retrospect, I’m more proud of what we’ve accomplished with our manufacturing capability.”

All the company’s products (more than 1 million converters per year) are manufactured at SynQor’s headquarters in Boxborough, Mass., about 30 miles northwest of MIT. That enables the business to respond nimbly to demand and to provide a significant measure of quality control. “By manufacturing here instead of letting someone do it halfway around the world, we are able to see issues that cause problems,” Schlecht says. “We are able to make continual improvements, and we have.”

Manufacturing in the United States also gives SynQor a competitive edge, he says: “It’s a wonderful example of how an American company can compete with an offshore manufacturer. It’s not by just trying to reduce costs. It’s by also providing features that make it worthwhile for the customer to pay a little extra money.”

What advice does Schlecht have for others who would like to start a company?

“Be able to recognize a problem and be able to solve a problem. Those are important skills to learn,” he says. “Be flexible and willing to change your mind in light of new facts. Know your priorities and focus on them.” But most important of all, he adds: “Be competitive. You must be driven to win.”

For Schlecht, founding a company wasn’t just about the startup phase. It was about growing a sustainable business and making the field of power electronics more efficient. “To younger people looking for a way to save the world, I would say that sometimes the answer is very simple: just develop a technology that’s more efficient than what’s out there and move that technology into the marketplace sooner than it would otherwise have gotten there. The energy you save the world by this effort can be substantial,” Schlecht says.

Almost 20 years have passed since the SynQor’s launch, and Schlecht’s time at MIT is long behind him. However, he has had the chance to watch his daughter go through the Institute — Lisa Schlecht is a 2010 mechanical engineering graduate who also received two master’s degrees from MIT, in mechanical engineering and in technology and policy. (Schlecht’s son, Derek, received a bachelor’s degree in mechanical engineering from Syracuse University in 2013 and is pursuing a master’s degree at North Carolina State University.) At SynQor, Schlecht has begun to focus on ensuring that the business will continue to thrive well past his own tenure at the helm.

Looking back now, Schlecht said he considers it a privilege to have had two such interesting careers. “I really enjoyed my time at MIT. I enjoyed the things I learned. I enjoyed all the people I met, whether students, faculty, or staff,” he says. “I’m very glad to have had completely different professional life experiences. It’s been fun on both sides.”

“ To younger people looking for a way to save the world, I would say that sometimes the answer is very simple: just develop a technology that’s more efficient than what’s out there and move that technology into the marketplace sooner than it would otherwise have gotten there.”

—Martin F. Schlecht, CEO, SynQor


Three-time EECS alumna Lisa Su, now the president and CEO of Advanced Micro Devices, urged MIT’s new doctoral

graduates to “dream big” and “work hard every day to solve the world’s toughest problems” in her commencement address at the Institute’s 2017 Investiture of Doctoral Hoods.

MIT professors, clad in the multihued robes representing the universities from which they received their doctorates (including MIT), draped doctoral hoods over students from 26 departments, programs, and centers at the Institute. EECS awarded 95 doctoral degrees during the most recent academic year, and most of those recipients attended the ceremony.

“I encourage each of you to dream big and believe you can change the world, have the courage to take risks and enthusiastically learn from mistakes, and work hard every day to solve the world’s toughest problems,” said Su, who received an SB in 1990, an SM in 1991, and a PhD in 1994. “I think if you do that, I’m pretty sure you will make everybody very proud, and you will be incredibly lucky throughout your career.”

In outlining her own experiences in technology and business, which have taken her from the Institute’s laboratories to the executive suite, Su observed that MIT has been a central influence on her own life and career. “The MIT PhD degree truly shaped who I am in so many ways, both personally and professionally,” she said.

Su came to the U.S. from Taiwan at age 2 and grew up in New York City. As an undergraduate at MIT, she developed a deep interest in semiconductors; as a graduate student, she received a master’s degree in management and a doctorate focused on research in silicon-on-insulator technology. Su quipped that when she entered MIT’s doctoral program, at the urging of her parents, she was “too young at the time to know any better.”

However, she wound up thriving in a challenging academic environment. “MIT is pure, and it’s really hard,” Su said. “MIT taught me how to think and solve really hard problems.”

Recalling the many ways that her technical education encouraged her to pursue a career in management, Su re-counted, “I thought I could make better business decisions because I understood the technology.”

Su began her career at Texas Instruments. She spent 13 years working at IBM, rising to the level of vice president of the Semiconductor Research and Development Center. She then worked in multiple executive roles at Freescale Semi-conductor, Inc. She joined Advanced Micro Devices in 2012 as a senior vice president and general manager for global business units, and served as chief operating officer before becoming the CEO.

INDUSTRY LEADER LISA SU RETURNS TO MIT At 2017 PhD hooding ceremony, the Advanced Micro Devices CEO says MIT “taught me how to think.”

By Peter Dizikes | MIT News

Photo: Dominick Reuter


“ I encourage each of you to dream big and believe you can change the world, have the courage to take risks and enthusiastically learn from mistakes, and work hard every day to solve the world’s toughest problems.”

—Lisa Su, CEO, Advanced Micro Devices

Su was named one of the Top 50 World’s Greatest Leaders by Fortune in 2017, and has been named a Top Semiconductor CEO by Institutional Investor in both 2016 and 2017. She was also cited as one of MIT Technology Review’s Top 100 Young Innovators in 2002. She serves on the board of directors for Analog Devices, the Global Semiconductor Alliance, and the U.S. Semiconductor Industry Association.

MIT Chancellor and Ford Professor of Engineering Cynthia Barnhart SM ’86 PhD ’88, who annually presides over the hooding ceremony, introduced Su. While giving welcoming remarks, Barnhart said she was “thrilled” to have Su addressing the graduates, and offered her own congratulations to the newly minted doctoral graduates.

“Earning a doctoral degree from MIT is no small feat,” Barnhart told the assembled graduates. “You have every reason to be proud, to be relieved, and to be filled with hope for what the future holds.”

This marks the third year that MIT’s doctoral hooding ceremony has featured a keynote speaker, who is chosen with input from MIT faculty and doctoral students.

Academic regalia dates to at least the 15th century, but American universities only adopted formal codes for graduation gowns and hoods in 1893. MIT doctoral degree robes have had their current design since 1995. MIT features a silver-gray robe with a cardinal red velvet front panel, as well cardinal red velvet bars on the sleeves. Additional color markings denote whether graduates have received a Doctor of Philosophy (PhD) or a Doctor of Science (ScD) degree.

The actual doctoral hoods are part of the doctoral robe ensemble. After the remarks by Barnhart and Su, all doctoral graduates had their names announced as they walked across the stage, then individually had the hoods draped on their ensembles by their department or program head.

JUST FOR POSTDOCS EECS is MIT’s largest department — so it should come as no surprise that it’s home to a massive postdoc community as well.

Dozens of postdoctoral associates work in the four EECS labs: Computer Science and Artificial Intelligence Laboratory (CSAIL), the Laboratory for Information and Decision Systems (LIDS), the Microsystems Technology Laboratories (MTL), and the Research Laboratory of Electronics (RLE). EECS’s Postdoc6 initiative helps unite this widely dispersed community for peer networking and skills training.

“Postdocs come to MIT in what is perhaps the most stressful period in their careers,” notes Nir Shavit, professor of electrical engineering and computer science and Postdoc6 faculty coordinator. “They have a relatively short period of time to show that they can engage in novel research, typically different from what they did in their PhDs, and at the same time apply for jobs.”

One popular offering is the EECS Leadership Workshop for Postdocs, a two-day offsite event offered several times a year for groups of 16 postdocs. The workshops, held at MIT’s Endicott House conference center in Dedham, Mass., offer presentations and interactive sessions tailored to postdocs interested in both academic and nonacademic careers.

Workshop attendees actively participate in sessions on leadership, collaboration, group dynamics, effective communication, and organizational skills such as setting goals and priorities. Facilitators use improvisational-theater techniques as part of that training, creating a microcosm of what happens in the lab. They also establish follow-up peer groups to provide postdocs with supportive networks that last long after each workshop ends.

“Key to these workshops is the ability to take the postdocs out of their busy everyday lives and allow them an interruption-free environment in which they can reflect on their needs going forward as future scientists and leaders,” Shavit says.

Postdoc feedback has been overwhelmingly positive. Typical was the comment from one recent attendee, who described the intensive workshop as a “very useful and productive experience,” adding: “The material can be applied immediately.”


The first question someone might ask upon learning about the accomplishments of Margaret Guo ’16 is

simply: “How?”

As in: how did she graduate from MIT with dual degrees in electrical engineering/computer science and biological engineering while maintaining a perfect grade point average (GPA)—and, among other activities, heading the MIT Society of Women Engineers during her senior year? And, especially, how did she manage all that while also becoming a national collegiate swimming champion? (In October 2016, thanks to that last achievement, Guo won the NCAA’s “Woman of the Year” award — the first MIT student to do so in the award’s 26-year history.)

So how did she do it all? The answer is simple, yet surprising: Passion.

“I think one of the things MIT has taught me is how to prioritize things that I am passionate about,” says Guo, a native of San Diego, who is now studying for a medical degree and a PhD at Stanford University. “For me, that meant spending my weekends as part of the Society of Women Engineers, mentoring young girls, and showing them the wonders of science.”

It also meant spending 20 hours a week practicing in the MIT pool. But Guo says that time spent swimming forced her to be more productive elsewhere. “Over the semesters, I learned to focus on the things I care about and do those things really well,” she says.

Her focus brought her star-athlete status: She earned five All-America honors from the College Swimming Coaches Association of America, and an additional six all-conference accolades. In 2016, Guo and her relay teammates set New

England Women’s and Men’s Athletic Conference records in three events, and Guo qualified individually for the Division III Women’s Swimming and Diving Championships.

“I don’t particularly think that my MIT education and swimming were in direct conflict,” says Guo, who has been swimming since she was a toddler. “I loved being surrounded by people who shared a common set of values and a mutual determination to achieve a team goal.”

Indeed, she credits her MIT classmates for helping her become “a better version of me.” They helped wake her up for 6 a.m. practices, toiled with her on problem-set questions, and supported her during the highs and the lows. “The inherently collaborative nature of the school fits well into its innovative vibe,” she says. “We weren’t competing against each other for the grade; we were working together to gain a deeper understanding of the world in order to make it a better place.”

Guo has now taken her passion back to the West Coast to pursue an MD/PhD. “Being a student athlete, I’ve been always interested in human physiology and always wanted to explore the physical and functional properties that make us so uniquely human,” she says. “It can be argued that the human body is a basically the world’s most complex system, with intertwining feedback loops, a lot of different inputs, and a lot of different parallel processes.”

Taking the viewpoint that diseases might be considered “system failures,” Guo hopes to work on creating directed and effective therapeutics for systems-based diseases, such as cancer or autoimmune disease. Ultimately, she says, she wants to help people lead healthier, happier lives.

SWIMMING TOWARD SUCCESS Margaret Guo, NCAA’s 2016 “Woman of the Year,” describes how she balances her passions for learning, leadership, and competitive athletics.



DONOR RECOGNITION

80 donor recognition CONNECTOR 2017 eecs.mit.edu

The Department of Electrical Engineering and Computer Science is truly grateful for the contributions of all alumni and friends. Listed below are those who made new gifts or commitments of $100 or more during Fiscal Year 2016 (July 1, 2015-June 30, 2016). Donors for FY 2017 will be listed in the next issue of the connector. *

* This material was compiled from Department and Institute records. To report an error in this list, or to be removed from future listings, please contact [email protected].

Robert L. Adams SM ’69

Rajni J. Aggarwal ’89, SM ’90, PhD ’96

James D. Ahlgren ’55

Roger K. Alexander SM ’91

Nasro Min Allah

Tao D. Alter SM ’92, PhD ’95

Abeer A. Alwan SM ’86, EE ’87, ENG ’87, PhD ’92

Boon S. Ang SM ’93, CSE ’98, ENG ’98, PhD ’99

Colin M. Angle ’89, SM ’91

Erika N. Angle ’04

Craig A. Armiento EE ’77, SM ’77, PhD ’83

Michael A. Ashburn SM ’96

Ash Ashutosh

Haejin Baek ’86

Arthur B. Baggeroer EE ’65, SM ’65, ScD ’68

Eugene J. Baik ’05, MEng ’06

Eileen J. Baird SM ’87

Esme O. Baker S EE ’00

David R. Barbour SM ’61

Richard A. Barnes ’68

Robert V. Baron ’71, EE ’77, SM ’77

Paul D. Bassett EE ’85, SM ’85

Arthur J. Benjamin SM ’77

John U. Beusch SM ’62, PhD ’65

Manish Bhardwaj SM ’01, PhD ’09

Archit N. Bhise ’13

H. E. Blanton SM ’49, EE ’55

Lenore C. Blum PhD ’68

Manuel Blum ’59, SM ’61, PhD ’64

Kevin L. Boettcher SM ’81, EE ’82, PhD ’86

Nelson E. Bolen SM ’60, EE ’62

Michael T. Bolin ’03, MEng ’05

Tom P. Broekaert SM ’89, PhD ’92

Rodney A. Brooks

Derek L. Bruening ’98, MEng ’99, PhD ’05

Randal E. Bryant SM ’77, EE ’78, PhD ’81

Robert R. Buckley EE ’78, SM ’78, PhD ’81

John F. Buford ’79, SM ’81

Charles G. Bures ’69

Geoffrey F. Burns SM ’89, PhD ’92

Charles H. Campling SM ’48

Katelyn Carroll

Michael P. Cassidy ’85, SM ’86

Valentino E. Castellani SM ’66

David L. Chaiken SM ’90, PhD ’94

Stanley G. Chamberlain SM ’62, EE ’63

Cy Chan SM ’07, PhD ’12

Ronald D. Chaney ’85, SM ’86, PhD ’93

Daniel K. Chang SM ’92

Hwa-Ping Chang PhD ’95

Steven B. Chanin ’89, SM ’91

Arthur C. Chen ’61, SM ’62, PhD ’66

Brian Chen SM ’96, PhD ’00

Harry H. Chen SM ’76

Kan Chen SM ’51, ScD ’54

Shu-Wie F. Chen ’86

Yin F. Chen ’14

Donald Chu ’75

Nelson C. Chu SM ’90

Shun-Lien Chuang SM ’80, EE ’81, PhD ’83

Man Ciin

Douglas R. Cobb SM ’65

Lewis D. Collins SM ’65, ScD ’68

Satyan R. Coorg SM ’94, PhD ’98

Geoffrey J. Coram PhD ’00

Fernando J. Corbato PhD ’56

Thomas H. Cormen SM ’86, PhD ’93

Jack D. Cowan SM ’60

Elliot M. Cramer ’55

David R. Cuddy EE ’74, SM ’74

Susan R. Curtis SM ’82, PhD ’85

Barbara M. Cutler ’97, MEng ’99, PhD ’03

Jerome Daniels

Bahman Daryanian ’77, SM ’80, SM ’86, PhD ’89

George A. Davidson SM ’56

Ronald De Vergiles

Jeff A. Dean

Douglas J. Deangelis SM ’06

Carrick J. Detweiler SM ’06, PhD ’10

Alpha Doo S ’77 EE

eecs.mit.edu 2017 CONNECTOR donor recognition 81

David H. Doo ’77

Jessie L. Dotson ’89

Jonathan B. Downey ’06

Jon Doyle SM ’77, PhD ’80

Paul R. Drouilhet ’54, SM ’55, EE ’57

Adam M. Eames ’04, MEng ’05

Bruce A. Eisenstein ’63

Arthur Evans NON ’54

Robert R. Everett SM ’43

Kenneth W. Exworthy SM ’59

Robert M. Fano ’41, ScD ’47

Michael D. Feezor ’63

Yuchen Feng ’13

Matthew L. Fichtenbaum ’66, SM ’67, EE ’68

Steven G. Finn ’68, SM ’69, EE ’70, ScD ’75

James G. Fiorenza PhD ’02

Giovanni Flammia PhD ’98

Mark A. Foltz SM ’98, PhD ’03

G. David Forney Jr. SM ’63, ScD ’65

Paul J. Fox EE ’73, SM ’73

Janet A. Fraser SM ’84

Thomas H. Freeman ’76, ’77

Robert W. Freund EE ’74, SM ’74

Anna V. Gallagher ’02

Thomas H. Gauss SM ’73

Steven P. Geiger SM ’74

Michael A. Gennert ’80, SM ’80, ScD ’87

Gwendolyn L. Gerhardt

Kent L. Gerhardt

Michael D. Gerstenberger EE ’85, SM ’85

Jeremy S. Gerstle ’99, MEng ’01

Edward C. Giaimo ’74, SM ’75

Anastasios N. Gianotas ’78

Carla M. Gianotas S ’78 EE

Arthur A. Gleckler ’88, SM ’92

Kenneth W. Goff SM ’52, ScD ’54

Nicholas Gothard SM ’62

Edmund P. Gould SM ’62

Paul A. Green ’73

Julie E. Greenberg SM ’89, PhD ’94

Randall V. Gressang SM ’66, EE ’67

Stephen E. Grodzinsky ’65, SM ’67

Winthrop A. Gross EE ’73, SM ’73

Sheldon Gruber ScD ’58

John V. Guttag

Olga P. Guttag

Wayne H. Hagman SM ’81

Walter C. Hamscher SM ’83, PhD ’88

Alain J. Hanover ’70

Ralph R. Harik ’01, MEng ’03

John G. Harris ’83, SM ’86

Eman S. Hashem SM ’89

Wendi B. Heinzelman SM ’97, PhD ’00

Jerrold A. Heller SM ’64, PhD ’67

Michael Henriques S ’91 EE

Steven J. Henry ’72, SM ’73

Herbert L. Hess SM ’82

Charles Robert Hewes SM ’67, PhD ’71

Alejandro P. Heyworth ’95

John S. Hill SM ’60

Robert O. Hirsch ’48, ’50, SM ’51

Michael G. Hluchyj EE ’79, SM ’79, PhD ’82

Theresa H. Hluchyj S EE ’82

Karen W. Ho ’94

Sou K. Ho

Wai K. Ho

Roger A. Holmes SM ’58

Jerry L. Holsinger PhD ’65

Gim P. Hom ’71, SM ’72, EE ’73, SM ’73

Liang Hong ’06

Merit Y. Hong ’84, SM ’87, PhD ’91

Charles W. Hoover ’47

Heidi Hopper

Tareq I. Hoque ’88, SM ’88, SM ’92

Mary A. Hou ’91, SM ’92

Henry H. Houh ’89, ’90, SM ’91, PhD ’98

Lisa Houh S ’89 EE

Kay L. Hsu ’90, SM ’91

Caroline B. Huang SM ’85, PhD ’91

Hugh Hudler S ’97 EE

Caleb W. Hug SM ’06, PhD ’09

David L. Isaman SM ’70, PhD ’79

William P. Jaeger SM ’80

Hans P. Jenssen ’65, EE ’68, PhD ’71

Cynthia K. Johanson ’01

Susan B. Jones

Charlene C. Kabcenell ’79

Dirk A. Kabcenell ’75

Zam K. Kam

Steven Kamerman ’73


Edward J. Kapp SM ’59

Zahi N. Karam SM ’06, PhD ’11

John S. Keen ScD ’94

Stephen T. Kent SM ’76, EE ’78, ENG ’78, PhD ’81

Ramin Khorram ’83, SM ’84

Elliotte J. Kim ’12

Richard Y. Kim ’83, SM ’88

Kenneth P. Kimi SM ’81

Barbara J. Klanderman PhD ’02

Gregory A. Klanderman SM ’95

David L. Kleinman SM ’63, PhD ’67

Thomas F. Klimek SM ’59

Wolf Kohn SM ’74, PhD ’78

Thomas F. Kollar SM ’07, PD ’11, PhD ’11

Ronald B. Koo ’89, SM ’90

Alisa Kretzmer S EE ’46

Ernest R. Kretzmer SM ’46, ScD ’49

Shawn Kuo SM ’04

Yu-Ting Kuo SM ’94

Shang-Chien Kwei ’05

Prashant Lal ’99

Yafim Landa ’11, MEng ’13

Emanuel E. Landsman ’58, SM ’59, ScD ’66

Andrea S. Lapaugh EE ’77, SM ’77, PhD ’80

Christopher T. Lee SM ’62, EE ’66

Jay K. Lee SM ’81, EE ’82, PhD ’85

Lily Lee SM ’95, PD ’02, PhD ’02

Michael Lee SM ’95

Yang-Pal Lee ’72

Yoong Keok Lee PhD ’15

Young S. Lee EE ’69, SM ’69

Alan P. Lehotsky ’73

Frederick J. Leonberger SM ’71, EE ’72, PhD ’75

Alan Levin ’72

Alexander H. Levis ’63, SM ’65, ME ’67, ScD ’68

Donald M. Levy SM ’58

Frank S. Levy ’63

Kevin A. Lew SM ’95

Anthony J. Ley SM ’63

Ying Li SM ’89, EE ’93, ENG ’93, PhD ’94

Jae S. Lim ’74, SM ’75, EE ’78, ScD ’78

Catherine Lin

Li-Jen T. Lin

Tzu Mu Lin

Barbara H. Liskov S ’60 EE

Nathan A. Liskov ’60

Frank J. Liu EE ’66

Kurt A. Locher ’88, SM ’89

Gary W. Look SM ’03, PhD ’08

Francis C. Lowell SM ’64, EE ’65

Allen W. Luniewski EE ’77, SM ’77, PhD ’80

William F. Maher SM ’80

Charles I. Malme SM ’58, EE ’59

Henrique S. Malvar PhD ’86

Alexandros S. Manos SM ’96

Jonathan A. Marcus ’06

Steven I. Marcus SM ’72, PhD ’75

Barry Margolin ’83

Elisabeth A. Marley SM ’96, PhD ’00

Glendon P. Marston ScD ’71

Emin Martinian SM ’00, PhD ’04

Michael Y. McCanna ’11

John L. McKelvie SM ’49

Ignacio S. McQuirk SM ’91, ScD ’96

Alan L. McWhorter ScD ’55

Scott E. Meninger SM ’99, PhD ’05

Dale E. Miller ’63

Stephen W. Miller ’63

Sramana Mitra SM ’95

Jama A. Mohamed PhD ’00

Lajos Molnar ’97, MEng ’98

Guy E. Mongold SM ’59

Warren A. Montgomery EE ’76, SM ’76, PhD ’79

Paul Moroney ’74, EE ’77, SM ’77, PhD ’79

Joel Moses PhD ’67

Marianne Mosher ’76

Jose M. Moura EE ’73, SM ’73, ScD ’75

Sean D. Murphy ’91

Sadiki P. Mwanyoha ’98, MEng ’98

Keith S. Nabors SM ’90, PhD ’93

Santhosh Narayan ’15

Robert F. Nease SM ’53, ScD ’57

Phillip T. Nee SM ’94, PhD ’99

John W. Neese SM ’79

Peter G. Neumann

Carl E. Nielsen SM ’58

Kenneth W. Nill ’61, SM ’63, PhD ’66

Paola F. Nisonger SM ’79

Robert L. Nisonger SM ’78

Robert W. Nutting SM ’85

eecs.mit.edu 2017 CONNECTOR donor recognition 83

James J. Olsen ’80, SM ’85, PhD ’93

Randy B. Osborne EE ’86, ENG ’86, SM ’86, PhD ’90

Rajesh K. Pankaj SM ’88, PhD ’92

Albert R. Paradis EE ’81, SM ’81, PhD ’86

Lynne E. Parker PhD ’94

Thornton S. Paxton SM ’65

Hugh M. Pearce SM ’66, EE ’67

Wendy Peikes ’76

Paul L. Penfield ScD ’60

Sharon E. Perl SM ’88, PhD ’92

David J. Perreault SM ’91, PhD ’97

Mary Linton B. Peters ’92

Stephen L. Peters ’91, SM ’92, PhD ’06

Marvin E. Petersen SM ’57

Robert J. Petrokubi SM ’68

Michael S. Phillip S EE ’91

Cynthia A. Phillips SM ’85, PhD ’90

Lisa A. Pickelsimer SM ’92

Marilyn Pierce

Elliot N. Pinson SM ’57

John C. Pinson SM ’54, ScD ’57

Damian O. Plummer ’02

Michael O. Polley ’89, SM ’90, PhD ’96

George H. Polychronopoulos SM ’88, PhD ’92

Aditya Prabhakar ’00, MEng ’01

James C. Preisig EE ’88, ENG ’88, SM ’88, PhD ’92

Robert A. Price SM ’53

Frank Quick ’69, SM ’70

Sanjay K. Rao ’02, MEng ’03

Richard H. Rearwin SM ’54

John A. Redding SM ’76

Clark J. Reese SM ’69, EE ’70

Howard C. Reeve SM ’83

Donnie K. Reinhard SM ’68, EE ’71, PhD ’73

Ellen E. Reintjes ’73, MCP ’74

John F. Reintjes ’66

Eric Richert

Evan Richert

Joan Richert

John Richert

Frederick L. Ricker SM ’77

Jennifer Ricker S EE ’77

Dominic A. Rizzo ’04

Roger A. Roach NON ’67

Joseph J. Rocchio ’57, SM ’58

Clifford A. Rose EE ’67, SM ’67

Larry S. Rosenstein ’79, SM ’82

Murray A. Ruben EE ’64, SM ’64

Melanie B. Rudoy SM ’06, PhD ’09

Martha Ruest S ’77 EE

William D. Rummler SM ’60, EE ’61, ScD ’63

Daniel M. Sable ’80

Freddie Sanchez ’00

Nils R. Sandell SM ’71, EE ’73, PhD ’74

Frank M. Sauk ’74, SM ’77

John E. Savage ’61, SM ’62, PhD ’65

Christopher J. Schaepe ’85, SM ’87

Roger R. Schell PhD ’71

Joel E. Schindall ’63, SM ’64, PhD ’67

Martin F. Schlecht ’77, EE ’80, SM ’80, ScD ’82

Paul S. Schluter EE ’76, SM ’76, PhD ’81

Jean-Pierre Schott EE ’82, ENG ’82, SM ’82, PhD ’89

Sarah E. Schott ’83

Brian L. Schulz SM ’84

Richard J. Schwartz SM ’59, ScD ’62

Campbell L. Searle SM ’51

David A. Segal ’89

Charles L. Seitz ’65, SM ’67, PhD ’71

Philip E. Serafim SM ’60, ScD ’64

Danny Seth SM ’01

Carol L. Seward ’47

L Dennis D. Shapiro ’55, SM ’57

Amnon Shashua PhD ’93

Paul J. Shaver SM ’62, ScD ’65

Henry R. Shomber SM ’80

Minoo N. Shroff ’63

Howard J. Siegel ’71

James H. Simons ’58

Marilyn Simons S ’58 MA

Edward M. Singel EE ’75, SM ’75

Jagadishwar R. Sirigiri SM ’00, PD ’02, PhD ’03

Jay R. Sklar SM ’62, PhD ’64

Emilie I. Slaughter ’87, SM ’88

Frank G. Slaughter ’84

Sandy Sloan

Donald L. Snyder SM ’63, PhD ’66

Gary H. Sockut SM ’74

Carlton E. Speck ’63, SM ’65, ScD ’70

David A. Spencer SM ’71, EE ’72

Richard H. Spencer EE ’57


Steven V. Sperry SM ’78

John M. Spinelli SM ’85, PhD ’89

Peter W. Staecker ’64, EE ’68

Kenneth R. Stafford SM ’66

Daniel D. Stancil SM ’78, EE ’79, PhD ’81

David L. Standley SM ’86, PhD ’91

Billy J. Stanton SM ’83

Andrew F. Stark ’97, MEng ’98

Clifford S. Stein SM ’89, PhD ’92

Gideon Stein SM ’93, PhD ’98

Russell L. Steinweg ’79

Eric H. Stern ’73

Melvin L. Stone ’51

Christopher E. Strangio EE ’76, SM ’76

Eric J. Stuckey SM ’98

Joan M. Sulecki SM ’83

David L. Sulman SM ’69

John D. Summers SM ’84

John Z. Sun SM ’09, PhD ’13

Katherine Swartz ’72

Donald L. Tatzin ’73, MCP ’74, ’75

Maziar Tavakoli Dastjerdi SM ’01, PhD ’06

Joan D. Teller

Samuel H. Teller

Michael L. Telson ’67, SM ’69, EE ’70, PhD ’73, SM ’74

Ahmed H. Tewfik SM ’84, EE ’85, ScD ’87

Barry L. Thompson SM ’88, PhD ’93

James M. Thompson ’77

Richard D. Thornton SM ’54, ScD ’57

Edward G. Tiedemann PhD ’87

James M. Tien SM ’67, EE ’70, PhD ’72

David A. Torrey SM ’85, EE ’86, PhD ’88

Sara Torrey S EE ’88

Hai V. Tran SM ’85

Charles D. Trawick SM ’80

Oleh J. Tretiak SM ’60, ScD ’63

Olivia Tsai ’03

Frederica C. Turner ’95

John C. Ufford SM ’75

Filip J. Van Aelten SM ’89, PhD ’92

Thomas H. Van Vleck ’65

Juan D. Velasquez SM ’96, PhD ’07

Matthew D. Verminski SM ’98

Olga Y. Veselova SM ’03

Holly A. Waisanen PhD ’07

Joseph E. Wall EE ’76, SM ’76, PhD ’78

Alexander C. Wang SM ’97, PhD ’04

Caroline W. Wang ’86

Da Wang SM ’10, PhD ’14

David Wang ’00, MEng ’00

Grace I. Wang SM ’07, PD ’11, PhD ’11

Kang-Lung Wang SM ’66, PhD ’70

Lawrence C. Wang ’99, ’00, MEng ’03

Shen-Wei Wang PhD ’68

Susan S. Wang ’83

Charles M. Watson SM ’70

Jennifer Welch SM ’84, PhD ’88

Gary L. Westerlund NON ’77

Donald F. Western SM ’66

Harold M. Wilensky ’70

John A. Wilkens PhD ’77

Lucile S. Wilkens PhD ’77

Daniel M. Willenson ’04, MEng ’12

Timothy A. Wilson ’85, SM ’87, ScD ’94

William J. Wilson SM ’63, EE ’64, PhD ’70

Raydiance R. Wise SM ’07

John W. Wissinger PhD ’94

Harvey M. Wolfson EE ’74, SM ’74

Joseph F. Wrinn ’75

Jun Wu

William W. Wu SM ’67

Joseph Wylen SM ’50

Katsumi Yamane SM ’71

Ying-Ching E. Yang SM ’85, EE ’86, ENG ’86, PhD ’89

Roy D. Yates SM ’86, PhD ’90

Vera S. Yaul

Wayne Y. Yaul

Anthony Yen SM ’87, EE ’88, ENG ’88, PhD ’92, MBA ’06

Robert D. Yingling SM ’68

Robert A. Young PhD ’68

Ryan E. Young ’08

Hai-Feng Yun

Weijie Yun

H. R. Zapp ’63, SM ’65

Ronald E. Zelazo ’66, SM ’67, EE ’69, PhD ’71

Francis H. Zenie ’56

Dale A. Zeskind EE ’76, SM ’76

Limin Zhang

Yan Zhang

EECS VISITING COMMITTEE

In April 2017, the MIT EECS Visiting Committee made its biennial visit to the department. The Committee operates as an advisory group to the MIT Corporation and the senior administration, offering appraisal, advice, and insights about the department. Chaired by John A. Thain, the committee is made up of leaders in industry and academia, many of whom are MIT alumni. Members heard presentations by School of Engineering and EECS leaders as well as EECS faculty, lab directors, and postdocs, and met with groups of faculty and students. They also toured the Department Teaching Laboratories and the Engineering Design Studio, learning about work in progress, and attended a reception with poster presentations by select SuperUROP students.

Front row, left to right: Asu Ozdaglar, Charlene C. Kabcenell, Raymond Stata, Andrew J. Viterbi, Anantha Chandrakasan, Diane B. Greene, Lisa T. Su, Jeannette M. Wing, Nancy Lynch | Back row, left to right: Ash Ashutosh, James A. Goldstein, Katherine Yelick, Giovanni De Micheli, John A. Thain, Vanu G. Bose, Raymie Stata, Eran Broshy, Colin M. Angle, Susie J. Wee

SuperUROP presentation during reception Demo during Engineering Design Studio tour

Photos: Mary Ellen Sinkus / Anne Stuart

MIT EEC

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Ignacio Estay Forno, a junior in EE, is researching a method to interface with nanoscale single photon detector arrays.

Stay connected to EECS at eecs.mit.edu

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