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Benjamin Coifman, Activities January 2017 to April 2018

(complete dossier available upon request) EDUCATION

Ph.D. University of California, Berkeley, Civil and Environmental Engineering (Transportation Engineering), Dec. 1998

M.Eng. University of California, Berkeley, Electrical Engineering and Computer Science, December 1998 M.S. University of California, Berkeley, Civil and Environmental Engineering, December 1995 B.E.E. University of Minnesota, Minneapolis, Electrical Engineering, graduated Summa Cum Laude,

June 1992 APPOINTMENTS

Associate Professor, The Ohio State University, Department of Civil, Environmental and Geodetic Engineering, 2005 to present (Assistant Professor 1999-2005).

Associate Professor, The Ohio State University, Department of Electrical and Computer Engineering, 2005 to present. (Assistant Professor 1999-2005)

Visiting Professor, Newcastle University, School of Engineering, Transport Operations Research Group and Future Mobility Group, May 2017 to Aug 2018.

Postdoctoral Researcher, University of California, Berkeley, Partners for Advanced Transit and Highways, December 1998 - September 1999

TEACHING

1. UNDERGRADUATE, GRADUATE, AND PROFESSIONAL COURSES TAUGHT Term/year Course number, title Formal evaluations credit hours Enrollment % Taught Students Faculty Peers Sp 2017 CE 2050, Probabilistic Applications and Data Interpretation in Civil and Environmental

Engineering, U, 3 cr 85 students 100% Yes Yes

A CEG in-class peer review was conducted by Prof. of Practice Daniel Pradel and the report was shared with the instructor.

Au 2017-Sp 2018 No teaching- FPL and research release Independent study courses: Term/year Course number, title credit hours Enrollment % Taught

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Sp 2017 CE 4998, U 2 cr 1 student 100% Research in Civil Engineering (Lauren Biales-Wise: 2 cr)

Sp 2017 CE 4999, U 2 cr 1 student 100% Research in Civil Engineering (Sili Kong: 2 cr)

Sp 2017 CE 8999, G 3 cr 1 student 100% Individual Studies in Civil Engineering (Balaji PonnuDevanarayanan: 3 cr)

Sp 2017 ECE 8999, G 15+15 cr 2 students 100% Research in Electrical Engineering (Mo Wu: 15 cr, Lizhe Li: 15 cr)

Su 2017 CE 4999, U 2 cr 1 student 100% Research in Civil Engineering (Sili Kong: 2 cr)

Su 2017 ECE 8999, G 0.5+4 cr 2 students 100% Research in Electrical Engineering (Mo Wu: 0.5 cr, Lizhe Li: 4 cr)

Au 2017 CE 4999, U 2 cr 1 student 100% Research in Civil Engineering (Sili Kong: 2 cr)

Au 2017 CE 8999, G 3 cr 1 student 100% Individual Studies in Civil Engineering (Balaji PonnuDevanarayanan: 3 cr)

Au 2017 ECE 8891, G 2 cr 1 student 100% Research in Electrical Engineering (Mo Wu: 2 cr)

Au 2017 ECE 8999, G 1+15 cr 2 students 100% Research in Electrical Engineering (Mo Wu: 1 cr, Lizhe Li: 15 cr)

Sp 2018 CE 8999, G 3 cr 1 student 100% Individual Studies in Civil Engineering (Balaji PonnuDevanarayanan: 3 cr)

Sp 2018 ECE 8999, G 3+9 cr 2 students 100% Research in Electrical Engineering (Mo Wu: 3 cr, Lizhe Li: 9 cr)

2. INVOLVEMENT IN GRADUATE/PROFESSIONAL EXAMS, THESES, AND DISSERTATIONS; AND UNDERGRADUATE RESEARCH

a) Graduate student programs i) Doctoral Students - active this period

4) Douglas Thornton, completed January 6, 2017, High Fidelity Localization and Map Building from an Instrumented Probe Vehicle. Last reported position: Cyber Embedded Engineer, Battelle Memorial Institute.

5) Mo Wu, anticipated completion in Su 2018. 6) Balaji PonnuDevanarayanan, anticipated completion in Su 2018. 7) Lizhe Li, anticipated completion in Sp 2019.

iii) Doctoral Students - candidacy examination committee chair 6) Mo Wu, completed May 3, 2017. 7) Lizhe Li, completed Aug 19, 2017.

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vi) Masters Students Plan B - advisor 9) Xinyun Shan, completed May, 2017. 10) Zhouqiao Zhao, completed May, 2017.

b) Noteworthy accomplishments of graduate student advisees i) Awards:

Balaji Ponnu, Keith Bedford Travel Award, Dept. Civil, Environmental and Geodetic Engineering, 2017.

c) Undergraduate research mentoring i) Senior Honor Theses

6) Sili Kong, New Insights into Car-following Behavior on Freeways Based on High Resolution Vehicle Data, completed November 22, 2017.

ii) Undergraduate Research Assistants: name, (courses taken with me and term, if any), period of supervision (inclusive of 2.c.i)

56) Lauren Biales Wise, (ce2050, Au 2014), Au 2015-Sp 2017 57) Rachel DuBois, (ce2050, Au 2014), Au 2015-Sp 2017 58) Sili Kong, (ce2050, Sp 2016), Sp 2016-Au 2017 59) Greg Gaus, (ce2050, Au 2014), Su 2016-Su 2017 60) Matthew Lowe, (ce2050, Sp 2017), Au 2017-present 61) Derek Briggs, Au 2017-present 62) Danny Walton, Au 2017-present 63) Hongliang Si, Au 2017-present 64) Dominic Mikrut, Au 2017-present 65) Chenxi Dai, Au 2017 66) Yuyi Chang, Sp 2017-present 67) Matthew Friedman, Sp 2017-present

5. CURRICULUM DEVELOPMENT- PAST FIVE YEARS

CE 2050, Probabilistic Applications and Data Interpretation in Civil and Environmental Engineering Through Sp 2017 CE 2050 was required for all undergraduate civil and environmental engineering students, and so there were typically 3-4 sections offered per year. I was first assigned a section in Au 2013. This would be my first time teaching lower division CE course and I was handed the "standard lecture material" developed by a faculty member who had retired a couple years prior. The standard lecture material continued to be used in most offerings past Au 2013. There was no textbook, the lecture slides were thin on technical information, and the material only went as far as probability distributions, with no coverage of statistics. Recognizing that I needed a textbook, I sought out: (1) an inexpensive book to ensure the students could purchase it, (2) something that would go deeper than the existing material, (3)

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would focus on applications rather than theory since the other sections were application oriented, and (4) based on my experience teaching sophomore level ECE courses, a text that would have a large number of worked problems. I settled on a Schaum's Outline of Probability and Statistics, a $25 MSRP book with "897 fully solved problems" and roughly the same amount of problems where only the final answer is given. Recognizing that it takes repeated practice to learn the material I typically assigned over 20 homework problems every other week, but the feedback in class was that the students did not like the large homework load. I suffered a short-term disability about 1/3 of the way into the course and was replaced with a different instructor for the remainder of the term. No SEI evaluations or peer reviews were conducted for my truncated portion of the class. In Au 2014 I was once more assigned the course. Since this offering was still effectively my first time through the material I only made minor changes. Specifically, based on the in-class feedback from the prior year I decided to reduce the homework assignments to three problems per week with the remaining problems moved to a set of non-graded recommended problems. In this offering I was able to go beyond probability and cover the following statistical topics: sampling theory, confidence intervals, hypothesis testing, and a brief introduction to linear regression. The biggest lesson I learned from this offering was that many students did not attempt the recommended problems. SEI evaluations were collected, but no peer reviews were conducted. I next taught the course in Sp 2016. In preparation for this offering I sought to leverage the lessons I had learned: 1. Students do not like to buy textbooks, even when the book is inexpensive, with used copies available

on-line for less than $10. So one of the first assignments was to upload a photo of their own copy of the book to Carmen within the first two weeks of the course.

2. Students do not like to do homework problems, yet it is necessary to work through many problems before completely grasping a new concept. So this time around while keeping the eight topical homework assignments I gave them 30-40 problems per assignment. With so many problems the students were bound to be displeased without accommodation. So rather than coming up with completely new problems as I had the previous offering, I used the supplementary problems in the textbook. This way the students still have to find the solution on their own, but they can immediately verify that they got the correct answer before submitting the homework. Further accommodating the students, I typically solved a representative selection of 4-5 of the assigned homework problems in lecture before the due date, thus, providing further incentive to attend lecture.

3. The students who most need to come to office hours are often the least likely to come. So this time around each assignment has a discussion session for one class meeting (effectively an in class office hour) before the due date. In these discussion sessions, I had the students ask for help on specific problems and I worked through many more of the assigned homework problems.

4. In talking with a psychology professor about the learning process, I learned that the simple act of being quizzed helps foster deeper learning of the subject matter. So in this offering I added a mandatory quiz on Carmen after the submission of each homework assignment. These quizzes are similar in complexity to the assigned problems, but unlike the homework, the students do not have the final answer. The students must pass every quiz perfectly to pass the class, but they can take the quiz an unlimited number of times and could ask me for help on any quiz problem that was too challenging. I found the most important feature of the quizzes is that it gives the students immediate feedback as to how deeply they understand the material, preventing a potentially ugly surprise on an exam.

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5. Students are not in a position to see the big picture, they do not have the perspective to understand the reasoning behind the workload. So in this offering I took care to explain that the homework are called "exercises" for a reason, analogous to high school sports, the homework assignments are "practice," the exams are the "game," and there are several hours of practice for every minute spent in the game. I also made clear to the students that I structured the class so that the homework will be the most challenging part, but if one does the homework, that the exams should be fairly easy. In the process, I make clear my teaching philosophy, the benefits of deep learning, and the fact that deep learning takes repeated exposure.

6. Historically I have allowed a page of hand written notes on the exams, assuming the preparation of this page would also provide reflective integration across the material. In the Sp 2016 offering I started to suspect that some of the students are not sufficiently skilled at preparing equation sheets to take into exams. So instead of allowing an equation sheet I integrated a "fill-in-the-blank" equation sheet on the exam where I gave the equation and they had to name what it was (the list of equations was shared on a review sheet ahead of the exam). This feature alone proved very successful.

These ingredients worked. The midterm exams were challenging, but because of all of the feedback ahead of the exams all of the students scored over 75% (and all but 4 over 85%) on the first midterm and all but 2 students scored over 75% on the second midterm. The improved test performance showed that the students are learning the material more deeply. The choice of the book provided an accessible, non-lecture resource for these students. The homework assignments required the students to learn the material more deeply. While the assignments were far more challenging than my previous offering none of the students complained about the difficulty or demands. The quizzes then provided immediate feedback on the depth of understanding. So the students arrived at the exams very well prepared and their performance on the exams reflected this fact. SEI evaluations were collected, but the scheduled peer review was not conducted. By this time several issues were starting to emerge, chief among them was the disparity between the 3-4 annual offerings of CE 2050. None of the other sections of 2050 went beyond probability in their coverage. As a result, the course has a reputation among students of being an easy class. It was also apparent that many students do not see why the topic is relevant to C/EnE (civil and/or environmental engineering). Another issue is that while the course is intended for sophomore level students, there are many students who transfer to C/EnE after completing one or two years in another major. Given their academic maturity, many of the 3rd-4th year transfer students are dissatisfied with the slower pace of the sophomore level course. The situation is compounded by the fact that many of these transfer students had already taken a statistics course in their previous major. So for 2016-2017 I was tasked by the department char with developing a standard offering of CE 2050 that I would then pilot in Sp 2017. I started this work in Su 2016. One of my goals was to bring the focus of the in-class examples and homework assignments to civil and environmental engineering problems so that the students would have a better context to understand the importance of the material in CE 2050. I met several times with the stakeholders including other CE 2050 instructors and the undergraduate studies committee. In Au 2016 Karen Dannemiller (then a new assistant professor) joined me in the effort to standardize 2050. Karen brought an even more ambitious goal to which I concurred: to make CE 2050 a showplace for all of the areas in C/EnE by using the examples and problem sets to present the breadth of the department and expose the students the opportunities that await them in subsequent courses. As I was preparing for Sp 2017 Karen and I identified two promising statistics textbooks, both with the presentation focused in a C/EnE context. There was the contemporary Ang and Tang (A+T) that had already been used in some offerings of CE 2050, and Benjamin and Cornell (B+C), a textbook from the

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1970's that is back in print at a low cost. While both books were promising for CE 2050, neither book looked like it would serve as a long-term reference to the students after completing 2050. Given the fact that B+C was about 1/10th the cost of A+T, I decided to pilot B+C as the primary textbook while also keeping the Schaum's Outline for reference. It took considerable time to track down a solution manual for B+C, and it did not arrive until after Sp 2017 started. While on the surface B+C looked promising, after reworking my lectures and homework around the new text, about a third of the way through the term I came to realize that B+C was not meeting the needs of the course. In the ensuing damage control, I wound up shifting back to Schaum's as the primary text and brought in several problems from A+T for the homework assignments with the intent to move to A+T in future offerings. Throughout the course material I updated most of the examples in lecture, the quiz questions, and the exam problems to C/EnE applications. While teaching the Sp 2017 offering I spent considerable effort revising the lecture notes and providing background so that any instructor could step in and use the notes. In addition to the structural changes discussed above, I made improvements on the communications side. Prior to Sp 2017, several students had commented in the written SEI's about my slow response to email questions. I have yet to have a term where I am not undertaking major revisions to this class, so with the extra demands on my time I often fall behind on all of my email. Although in past offerings I had repeatedly told the students to put the course number in the subject of their email (to help me when triaging email), many did not. I had also repeatedly told the students to approach me before or after lectures if they have conflicts with my office hours and want to schedule to meet at some other time. While these guidelines worked for most of the students, it did not reach all of the students. So, in the Sp 2017 offering I moved all of my course related electronic inquiries from email to Carmen messages. In this fashion, I no longer had to triage email to find the course inquiries. Almost every day I checked and responded to Carmen messages several times a day (occasionally an inquiry on Friday night might go 36 hrs before getting a response). The overarching problem of the students with a prior statistics course persisted in to this course offering (29 of 85 students reported having a prior stats course, meanwhile, only 16 of the students were at the targeted sophomore level). Ultimately the undergraduate studies committee decided to allow equivalent credit for any statistics course required by other departments in the college. For the Sp 2017 offering SEI evaluations were collected and a peer review was conducted. In Au 2017 I went on sabbatical while Karen continued the revisions to 2050. Now that students with a prior statistics course are no longer required to take CE 2050, the number of offerings is just once per semester. Teaching CE 2050 Au 2017, Karen kept much of the topical structure (covering the same statistical topics as I had since Au 2014) and support structures (quizzes and in class review sessions from Sp 2016), she built on my lecture notes, retained Schaum's Outline as a reference text, and continued increasing the focus on C/EnE problems. She also made several positive changes in new directions: identifying a very good statistics book to use as the primary text, brought clickers in to the classroom, and moved most of the homework and exams to on-line tools. We continue to discuss ways to bring about her vision of a grand showplace of C/EnE to give the students a taste of the various specialties that lie ahead.

CE 5750/ECE 5400, Instrumentation, Signals, and Control in Transportation Applications

This class is cross-listed between CE and ECE, it is open to undergraduate and graduate students from both departments. The preceding course under the quarter system primarily served graduate students, and assignments consisted of: five group homework sets that used Matlab programming to address "Big Data" problems, five individual homework sets that focused on analytical traffic flow theory, and one group project that sought to leverage the other course components. With the switch to semesters in Au 2012 the

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demand also shifted, undergraduates now typically make up 60%-70% of the enrollment. The typical CE undergraduate has little programming experience since Matlab is optional in the numerical methods course (CE 2060) and I quickly learned that many of these students were not able to complete the programming assignments on their own. While the disparity between the typical CE undergraduate student and the typical ECE graduate student will forever add an extra challenge to teaching this course, it is rewarding to the students to use their respective strengths to surmount a topic that falls between the two disciplines. Between 2013 and now I have been adjusting the course to keep the assignments up to date and revising the pace to accommodate the changing demographics. First, in terms of topical revisions, starting in Au 2013 for the group homework assignments the students are given a data set consisting of (i) one day of individual vehicle actuation data from roughly 60 loop detector stations along I-71 in Columbus, and (ii) from the same day and corridor about two hours of instrumented probe vehicle data that contains the trajectories of the probe and the vehicles around it. Over the five homework assignments the students go from noisy raw data to manipulating their processed data to study the interrelationships between vehicles and explore how the traffic state evolves over time and space. What the students do not realize is that the data sets they use are actually ahead of the cutting edge in the research community. I have spent many years extracting large, high quality, empirical data sets, and the two data sets used in the class are prime examples. The instrumented probe vehicle data set was only released to the research community in late 2016 and we do not anticipate releasing the corresponding loop detector data to the research community until later this year. For the changing demographics, I have made several major revisions to accommodate the shift to more undergraduates with less programming experience, 1. I found that the students who most needed programming help were also the ones least likely to come

to office hours or ask questions in class. If the students will not come to office hours, I decided that I would bring the office hours to them by reallocating one lecture period per week to become an in-class computer lab over the duration of the group homework problem sets. During these labs, I float from group to group helping them surmount any problems they have encountered.

2. Even with the in-class labs many of the students were not able to formulate the necessary processing sequence for their algorithms or envision the necessary data structures for the tasks at hand. So while it would be desirable to have the students formulate their own algorithms from scratch, given the amount of ground I want to cover with the assignments I have found it necessary to provide the students with skeleton code that defines the data structures and the sequence of events in the program, to which they then add the code to do the specific tasks. In this manner, the students are no longer struggling with the low-level structure of their programs, while they still have to understand the code and have an overarching vision to complete each of the programming assignments.

3. I removed all of the individual homework sets so that the students could focus their attention on the programming tasks. Then, over the period that the students are working on the group project I go back and present the traffic flow theory in the context of the results from the five programming assignments. In this format, I deliberately incorporate tasks in the programming assignments to set up examples that I will later use when I subsequently lecture on traffic flow theory. Having already worked hands-on with the data, the relationships make intuitive sense to most students and they quickly absorb the theory presented after the empirical exploration.

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CE 5760, Network Metrics and Control in Transportation Systems This class is the highest-level class I teach in a two-year cycle. It is aimed primarily at CE graduate students, but it is also open to fourth year CE undergraduate students. The course covers signalized intersection control on roadway networks, network metrics, and vehicle routing problems. The class typically has under 10 students per offering, which allows for a more conversational style of presentation. Prior to the Au 2013 offering I had a significant problem that the students simply did not buy the required textbook and even when assigned readings came from on-line sources they were not doing them. With the Au 2013 offering I shifted to using reading material that was low or no cost to the students and with each reading assignment I also required them to write a short essay to summarize the reading, thus, ensuring that the students actually did the reading. In this offering I also sought to bring more analysis into the course to make the theory more tangible. I pursued two separate paths in this regard, first, I envisioned collecting empirical data on arterial network flows and analyzing it in the class. Second, with each reading assignment I sought to contrast very applied work against very theoretical perspectives on the same topic. Unfortunately, about 1/3 of the way into the semester I suffered short-term disability that left me bedridden for 3 months. Since there were several graduating students in the class, I chose to continue leading the class remotely so that the students would not have any delays to their graduation. From that point on most assignments were posted on Carmen and I held weekly or bi-weekly class sessions by telephone. In Au 2015 offering I undertook another revision of the course to fine tune the previous revisions. Rather than collect field data and be subject to the associated uncertainties, instead, I decided to develop several weeks of new material focused on traffic simulation. I remain stunned by the extent of distracting technologies (smartphones, etc.) have become commonplace in lecture rooms these days. To counter these forces, in this offering I took the "write a passage" requirement one step further and required the students to write 1-2 paragraphs summarizing each day's lecture and submit it to Carmen within 24 hrs. This requirement not only brought greater class participation from the students, but it also provided valuable feedback to me about what did and did not work in lecture, allowing me to clarify any key points that were misunderstood in the previous lecture. Furthermore, recognizing that the reading based essays were a drag on the students, I replaced them with a less-demanding but equally effective mechanism to ensure the students would read assigned material: the threat of pop quizzes designed to be easy for someone who has done the reading and impossible for someone who has not done the reading.

ECE 2100, Electrical and Computer Engineering II- recitation

With the move to semesters this course combined material from several sophomore level lecture courses and one lab section under the older quarter system, all of which were required courses for students in the ECE major. The class also absorbed one ECE service course required for computer science (CSE) majors. The new ECE 2100 was the second half of the sophomore experience. Starting in Au 2016 this course was phased out and the sequence has been replaced with a new sequence better structured to serve the students. I had nothing to do with those curriculum decisions, and in teaching a recitation section I have little control over the course content. However, I have latitude in how the material is presented. To this end I spend time discussing meta-learning (e.g., the course is as much about the analytical tools as it is about the particular topic). There are two big challenges that I see in this course, the first is providing sufficient context for the students to relate to the relatively abstract concepts that they are facing in their first ECE course sequence. So, in ECE 2100 I incorporate a number of tools that I've learned over the years to help make the material

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tangible, e.g., mnemonics to keep concepts straight and explaining that a circuit diagram is really just a graphical user interface telling you what equations you need to write. I also take care to explain that this class is so much more than simply analyzing circuits or simple systems. No matter what engineering major you take, there is a class where the students must learn how to apply the mathematical tools they have learned (e.g., using linear algebra to solve circuits) as they start developing their own analytical skills. Doing so in the context of basic circuits serves double duty, by also teaching the students fundamental concepts that they need to intuitively know in their domain (e.g., Ohm's law and Kirchhoff's equations). The second challenge is the large discrepancies between the ECE students who are motivated to take a course in their major and the CSE students who are required to take a demanding course outside of their major. For the non-major students, it is a tougher sell, many view the course as an unwelcome burden no matter what you do. This contrast is readily apparent in the SEI reports from Sp 2014, when I taught two sections with the same lecture style and content, one section with only 5% ECE majors and the other with 64% ECE majors. Understandably, these non-major students often ask that I focus narrowly on what will be on the exams. I take care to explain the purpose of non-major courses as I see it. Specifically, no matter what the discipline, as an engineer their career will include encountering problems they have never seen before and for which they have to quickly get up to speed. Required non-major courses provide the students with an opportunity start tackling these types challenges in a safe and forgiving environment (in this case all of the answers are known to the instructor and no one will be injured or lose their job if a mistake is made). As an added bonus, the students are exposed to solution methods that help broaden their perspective, expands their own personal analytical toolbox, and the new knowledge might just help them make connections in their professional career.

6. TEACHING APPROACH, GOALS, ACCOMPLISHMENTS, AND FUTURE PLANS Why do we give exams? Is it to test the students' knowledge? No, that is only the answer to why students take exams. The goal of an education system is to impart knowledge that will help the students succeed. Our curriculum is proactive, teaching concepts before they are applied, e.g., requiring calculus and physics before those tools are used in an engineering course. Doing well in required courses will ensure that the student has learned what they need to know to be an effective engineer. The problem with this proactive approach is that at the time of presentation, the student has little context to appreciate the value of the new knowledge being offered to them. Without this context, there is no reason why a student should want to study a new topic. A freshman should not appreciate that calculus is useful until a few courses later when they apply it to surmount a difficult challenge. But how do you motivate them to learn calculus at the time it is presented? That is why we give exams, an artificial construct to motivate students to learn material for which they have little context to appreciate its importance. If a student can demonstrate their knowledge on the exam, the presumption is that they have learned the material to a depth necessary for subsequent courses, and ultimately for their careers. If the only guidance for a midterm is that it will cover specific topics, lectures, or chapters, then to prepare for the midterm the student will have to review all of that material. By the time of a midterm the student should have reviewed the material at least 4 times: once in lecture, once reading the text, once solving the homework, and once preparing for the exam. This midterm preparation provides a key opportunity to integrate information across the topics on the exam. Another review to integrate information over the entire course comes when preparing for the final exam. In my opinion, this final review is the most important for deep learning, only after seeing all of the course material can they appreciate the subtle nuances presented at the start of the course. It is this process of review and re-review of the material that imparts deep learning.

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The distinction between deep learning and surface learning is often overlooked. Deep learning comes from repeated exposure, repeated practice, and deliberate integration. There are two trends in education that I see undermining deep learning. The first is the practice of handing out lecture slides. Thus, giving the false impression that a student need not actively take notes. Through active notetaking the student remains engaged with the lecture and uses far more regions of their brain than if they were just passively listening. The more regions of the brain engaged, the more information that is retained. The second trend is practice exams or overly-specific reviews that telegraph what will be on an exam. This telegraphy fosters shallow learning, leading the student to narrowly focus on the specific questions that will be on the exam without promoting the deep learning that comes from reviewing all of the material. I make a deliberate decision to foster deep learning knowing that it risks negatively impacting my SEI scores. A student expecting telegraphic exams will be displeased by having to review the material on their own as they prepare for an exam, and many will not appreciate the deep learning until they use that knowledge long after the course is complete. But I also recognize that violating expectations will catch some students off guard. I have progressively added many feedback processes and safety nets. I provide clear bounds on what material might be on an exam and to what depth. In my lower division courses, I often integrate discussion sections into lecture because I found that the students who most needed office hours (based on exam scores) were the ones least likely to come. I give partial credit for homework submitted after the solutions have posted because copying the solutions by hand is still more valuable than casually glancing at them. I employ an on-line quiz system that requires the students to test their knowledge so that they can discover any deficiencies before they take an exam. The next evolution for my lower division courses will be to invite the OSU Dennis Learning Center to hold an in-class workshop during the first week of the term on methods of successful learning (reading with a purpose, chunking the learning process, etc.).

7. EVALUATION OF TEACHING I use the SEI to shape my offerings. Usually my highest SEI numerical ratings are #3, #4 and #6, while #10 is usually my lowest with a bimodal distribution where a few extreme responses lower the mean. Over the review period 2/3's of my courses had a large number of non-major students (ECE 2100, with 31%-95% CSE majors) or are on a topic perceived to be outside the major (CE 2050). CE 2050 has a reputation of being an easy course. My offerings are more demanding than most (e.g., covering statistical analysis), so students expecting an easy ride are understandably upset. Some comment learning more from the book than from lecture. That was by design in my choice of books, I would expect many 3rd-4th year students should be able to learn a sophomore level course from the book (especially when repeating basic statistics). I also look for revealed measures like overall performance on the exams across offerings to gauge effectiveness. I have had limited benefit of peer reviews. Most of my peer reviews were conducted by ECE, but as per department policy, these reviews are not shared with the instructor. Meanwhile, the CEG culture is to share the peer reviews to provide advice to improve the faculty member's teaching. Of my two peer reviews from CEG, the first in CE 5760 (Au 2015) was positive, while providing constructive feedback that I subsequently acted upon. The second in CE 2050 (Sp 2017) was very positive and provided a valuable perspective.

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RESEARCH

1. BOOKS, ARTICLES, AND OTHER PUBLISHED PAPERS Current and former advisees denoted with "*"

d) Bulletins and technical reports (45 total) [r41] Coifman, B., Lee, H.*, LIDAR Based Vehicle Classification, NEXTRANS Project No.

123OSUY2.1, June 2017, 24p. Contribution: 75%, I supervised the research and guided the student. [r42] Coifman, B., Wu, M.*, Redmill, K., Thornton, D.*, Segmenting, Grouping and Tracking Vehicles

in LIDAR Data, NEXTRANS Project No. 124OSUY2.1, June 2017, 33p. Contribution: 60%, I supervised the research and guided the students.

[r43] Coifman, B., Advancing Traffic Flow Theory Using Empirical Microscopic Data, NEXTRANS Project No. 174OSUY2.2, June 2017, 24p. Contribution: 60%, I supervised the research and guided the students.

[r44] McCord, M., Mishalani, R., Coifman, B. Intelligent Transportation System Pilot Project- Final Report, Federal Transit Administration, 230p. June 2017. Contribution: 20%, I led one facet of the research program.

[r45] McCord, M., Coifman, B., Mishalani, R., Roadway Traffic Data Collection from Mobile Platforms, NEXTRANS Project No. 176OSUY2.2, July 2017, 26p. Contribution: 33%, We collaboratively led the research roughly equally.

e) Peer reviewed journal articles (57 total) [j52] Kim, S.*, Coifman, B., "Assessing the performance of SpeedInfo Radar Traffic Sensors" Journal of

Intelligent Transportation Systems. Vol 21, No 3, 2017, pp179-189. DOI:10.1080/15472450.2016.1273779 Contribution: 60%, I supervised the research and guided the student, I was corresponding author.

[j53] Coifman, B., Redmill, K., Yang, R.*, Mishalani, R., McCord, M., "Municipal Vehicles as Sensor Platforms to Monitor Roadway Traffic," Transportation Research Record 2644, 2017, pp 48-54. Contribution: 40%, I led this facet of the research, guided the student, and I was corresponding author.

[j54] Ponnu, B.*, Coifman, B. "When Adjacent Lane Dependencies Dominate the Uncongested Regime of the Fundamental Relationship," Transportation Research Part B. Vol. 104, 2017, pp 602-615. Contribution: 60%, I supervised the research and guided the student, I was corresponding author.

[j55] Coifman, B., Li, L.*, "A Critical Evaluation of the Next Generation Simulation (NGSIM) Vehicle Trajectory Dataset," Transportation Research Part B. Vol. 105, 2017, pp 362-377. Contribution: 60%, I supervised the research and guided the student, I was corresponding author.

[j56] Douglass, J., Dissanayake, D., Coifman, B., Chen, W., Ali, F., "Measuring the effectiveness of a transit agency's social-media engagement with travellers," [accepted for publication in] Transportation Research Record. (peer reviewed). Contribution: 15%, I contributed to the research and documentation.

[j57] Coifman, B., Li, L.*, Xiao, W., "Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers - with New Insights into a Controversial Hysteresis," [accepted for publication in] Transportation

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Research Record. (peer reviewed). Contribution: 70%, I supervised all of the research, did much of the coding, and wrote most of the document.

i) Papers in proceedings (73 total) Ponnu, B.*, Coifman, B., "Speed-Spacing Dependency on Relative Speed from the Adjacent Lane: Presenting New Insights for Car Following Models" Proc. of the 96th Annual Meeting of the Transportation Research Board, 2017. (peer reviewed). Contribution: 70%, I supervised the research and guided the student, I was corresponding author. Douglass, J., Dissanayake, D., Coifman, B., Chen, W., Ali, F., "Measuring the effectiveness of a transit agency's social-media engagement with travellers," Proc. of the 97th Annual Meeting of the Transportation Research Board, 2018. (peer reviewed). Contribution: 15%, I contributed to the research and documentation. Coifman, B., Li, L.*, Xiao, W., "Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers- with New Insights into a Controversial Hysteresis [Abridged]," Proc. of the 97th Annual Meeting of the Transportation Research Board, 2018. (peer reviewed). Contribution: 80%, I supervised all of the research, did much of the coding, and wrote most of the document. Ponnu, B.*, Coifman, B. "When Adjacent Lane Dependencies Dominate the Uncongested Regime of the Fundamental Relationship- Abridged," Proc. of the 97th Annual Meeting of the Transportation Research Board, 2018. (peer reviewed). Contribution: 60%, I supervised the research and guided the student, I was corresponding author.

j) Unpublished scholarly presentations (95 total) McCord, M., Mishalani, R., Coifman, B., "Empirical Demonstration of Traffic Flow Estimates from Repeated Passes of a Mobile Sensing Platform" Presented at The 96th Annual Meeting of the Transportation Research Board, 2017. (peer reviewed). Contribution: 20%, I contributed to the research and documentation. Coifman, B., Redmill, K., Yang, R.*, Mishalani, R., McCord, M., "Municipal Vehicles as Sensor Platforms to Monitor Roadway Traffic" Presented at The 96th Annual Meeting of the Transportation Research Board, 2017. (peer reviewed). Contribution: 40%, I led this facet of the research, guided the student, and I was corresponding author. Ponnu, B.*, Coifman, B., "Adjacent Lane Dependencies of the Uncongested Speed-Flow Relationships," Crash Imminent Safety UTC Annual Meeting, Columbus, Ohio, September 22, 2017. Contribution: 60%, I supervised the research and guided the student. Ponnu, B.*, Coifman, B., "Safety Implications of Traffic Dynamics in Freeway Traffic," Crash Imminent Safety UTC Annual Meeting, Columbus, Ohio, September 22, 2017. Contribution: 60%, I supervised the research and guided the student. McCord, M., Mishalani, R., Coifman, B., "Determining Traffic Flows and Speeds across Spatially Extensive Urban Areas from Transit Buses: Empirical Results using a Sensor-equipped Mobile Sensing Platform," the Ohio Transportation Engineering Conference, Columbus, OH, October 10-11, 2017. Contribution 25%, the talk and preparation was done collaboratively Coifman, B., "Questioning Fundamental Assumptions and Getting Unexpected Answers- a Parable from the Land of Traffic Flow Theory," Presented at the Newcastle University Transport Seminar Programme, October 11, 2017.

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3. FOCUS OF THE CANDIDATE'S RESEARCH Throughout my career, I have sought to understand the nuances of traffic dynamics. As an empiricist, I quickly discovered that the available traffic data was of poor quality. So launched my second research thrust: collecting high quality, high-resolution traffic data; both by extracting more information from conventional vehicle detectors and by developing new sensor technologies. These pursuits are complementary. Understanding traffic dynamics shapes my focus for advancing the traffic monitoring tools, while the traffic monitoring provides invaluable data for understanding traffic dynamics. Ultimately, more accurate surveillance and an improved understanding of traffic dynamics are crucial for advancing highway safety and efficiency through improved designs and controls. Most operating agencies pursue research that yields immediate benefits, and so one facet of my research focuses on traffic detector performance [j7, j17, j20, j21, j22, j30, j32, j34, j38, j45, j46, j52], and another focuses on improving speed measurements and vehicle classification [j9, j14, j19, j28, j39, j41, j43]. Through these efforts, I have developed trust and respect from practitioners who often regard academics as being out-of-touch with practical problems. As a result of this trust, I have been able to leverage my insights to extract considerably more information from conventional vehicle detectors. Normally vehicle detectors only report macroscopic measurements at one or two locations per mile. I have developed techniques to reidentify vehicles between successive detector stations and thus, extend the surveillance region to the entire freeway [j5, j10, j11, j12, j13, j15, j25]. I am particularly interested in advancing our understanding of traffic dynamics, but this fundamental research takes much longer to yield returns. By tackling the applied traffic monitoring problems for operating agencies, it provides data and resources that are also critical for traffic dynamics research. Most traffic dynamics studies use little or no empirical microscopic data, instead they reproduce macroscopic observations for validation. Unfortunately, this common approach allows plausible but incorrect hypotheses to perpetuate because different mechanisms can give rise to the same macroscopic outcome. This practice persists because microscopic traffic data are very difficult to collect. Vehicles can travel 100 ft/s, interact with scores of other vehicles, and yet vehicle spacing needs to be accurate to under a foot. To address this need, my group has developed an instrumented probe vehicle (IPV) with localization (GPS and inertial navigation) and perception (LIDAR and radar) to monitor the ambient traffic and collect vehicle trajectory data [c3, j51, d1]. The IPV has revealed insights into capacity reducing phenomena [c1, j33], including dispelling the commonly used point-bottleneck model in favor of an extended-bottleneck-mechanism [j31]. I have pursued vehicle trajectory data in several other ways [r3, j6], with recent work identifying chronic errors in the widely used NGSIM dataset and using machine-assisted manual re-extraction to rectify those errors [j55, d2], and then extending these tools to also resurrect Treiterer's seminal vehicle trajectories [j57, d3]. My work has revealed numerous traffic dynamics dependencies that are absent from conventional traffic flow models, including driver relaxation [j26, j33] and mechanisms underlying capacity drop [j37]. Examining deficiencies in deriving the fundamental relationship (FR) from conventional detector data [j42] led to a new technique to overcome those deficiencies [j44]; which revealed that, contrary to conventional wisdom, (1) within a queue the speed at which disturbances propagate upstream depends on the vehicle length (moving faster through trucks than cars) [j48], and (2) the shape of the FR in one lane depends on conditions in adjacent lanes [j49, j54]. Leveraging the IPV my group has examined new ways to monitor traffic [j35, j40]. We have explored using probes to monitor traffic [j29] and identified a key issue using transit vehicles: by servicing passengers, a bus is pulled out of the signal progression and so the resulting travel time may not be representative of general traffic [j47]. Working with colleagues, we are developing ways to use transit

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vehicles as sensor platforms to monitor ambient traffic conditions without the transit maneuvers impacting the measurements [j53]. This terrestrial application is actually an outgrowth of our airborne/spaceborne traffic monitoring [j16, j18], that included one of the earliest use of drones to monitor traffic [e3]. Looking ahead, I see a need to anticipate the behavior of autonomous vehicles (CAV). You cannot effectively automate or control a system that you do not understand. Most CAV research fails to anticipate that CAV will remain bounded by traffic dynamics, e.g., slowing for curves, incidents, and weather events. With CAV, queues could spread in excess of 300 mph, gridlocking an entire city in minutes. I want to anticipate the dynamics before widespread deployment; thereby leapfrogging the development cycle to prevent early design deficiencies from creating a legacy of problems.

4. QUALITY INDICATORS OF THE CANDIDATE'S RESEARCH

(as of May 7, 2018)

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(as of May 7, 2018)

5. RESEARCH FUNDING

a) Funded research - principal investigator Contract with USDOT/CrIS, "Safety Implications of Traffic Dynamics in Congested Freeway Traffic,"

October 1, 2014 - September 30, 2017, $50,401 (plus cost share of $9,640). 60047410 Contract with National Science Foundation, CMMI-Unsolicited, "Changing Lanes- Using Advance

Sensor Technology to Understand Driver Behavior," August 2015 - July 2019, $373,716. 60048887 Contract with USDOT/NEXTRANS, "Advancing Traffic Flow Theory Using Empirical Microscopic

Data," July 2015-Dec 2017, $55,419 (plus $39,999 cost share). 60052110 Contract with USDOT/NEXTRANS, "Urban Roadway Traffic Data Collection from Mobile Platforms,"

Mark McCord, Rabi Mishalani, and Benjamin Coifman; July 2015-Dec 2017, $117,382 (plus $43,741 cost share). Contribution: 33%, contributed to the experimental design. 60051310

Contract with National Science Foundation, CMMI- Research Experience for Undergraduates, "Changing lanes - Using advance sensor technology to understand driver behavior," Aug 2015-July 2018, $10,000. 60052954

Contract USDOT/Mobility21, "Using municipal vehicles as sensor platforms to monitor the health and performance of the traffic control system," November 2016-September 2022, $57,809 (year 1), Benjamin Coifman, Mark McCord, and Rabi Mishalani. Contribution: 33%, contributed to the experimental design. 60059144

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Contract with ODOT, "Student Study: Investigating the Feasibility of Coordinated Ramp Metering Along Freeway Corridors in Ohio," August 2017-November 2018, $39,979. 60059969

b) Funded research - co-investigator Including only those proposals for which I made a significant contribution to but I was not listed as a principal investigator on the proposal.

Contract with USDOT Federal Highway Administration, "Human factors for crash imminent safety in intelligent vehicles," October 1, 2015 to September 30, 2018, $923,700. Umit Ozguner, Eylem Ekici, Beth-Anne Schuelke-Leech, Fusun Ozguner, David Woods, Keith Redmill, Janet Weisenberger, John Bolte, and Benjamin Coifman. Contribution: 7%, contributed to the experimental design. GRT00031810

Contract with Carnegie Mellon University (sub USDOT), "Mobility 21: National University Transportation Center for improving mobility," October 1, 2016 to September 30, 2018, $378,000. Keith Redmill, Levent Guvenc, Umit Ozguner, Arda Kurt, Mark McCord, Rabi Mishalani, and Benjamin Coifman. Contribution: 10%, contributed to the experimental design. GRT00046706

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SERVICE

1. EDITORIAL LEADERSHIP AND SERVICEEditorial Leadership Associate Editor Journal of Intelligent Transportation Systems, 2006-present

This position includes selecting reviewers, managing the review process, and making final acceptance decisions for publication in the journal.

Member of the Editorial Advisory Board for Transportation Research Part A, 2003-present I regard this journal to be among the top 5 journals in transportation.

Member of the Editorial Advisory Board for Transportation Research Part B, 2005-present I regard this journal to be among the top 2 journals in transportation.

Service as a Reviewer Paper reviewer for Transportation Research: Part-A 1 Paper reviewer for Transportation Research: Part-B 4 Paper reviewer for Transportation Research: Part-C 2 Paper reviewer for Transportation Research Record: Journal of the Transportation Research Board 16 Paper reviewer for The International Symposium on Transportation and Traffic Theory (ISTTT) [all selected papers will also be published in top transportation journals] 19

2. PROFESSIONAL SOCIETIES AND COMMITTEES Societies Served

Transportation Research Board (TRB) American Society of Civil Engineers (ASCE) Institute of Electrical and Electronics Engineers (IEEE) World Conference on Transport Research Society (WCTRS)

Committee Membership Member of the TRB Committee on Traffic Flow Theory and Characteristics, 2002-2013, 2016-present

TRB standing committee memberships are of limited numbers (typically 25), are nominated by the committee chair, and are typically term limited to 9 years. The committee members actively review submissions to the TRB annual meeting, and the TRB journal series: Transportation Research Record- Journal of the Transportation Research Board. The committees are national in scope with international membership. The committees typically meet twice a year and establish research priorities for TRB and USDOT.

Member of the TRB Freeway Operations- Research Subcommittee, 2000-present Member of the TRB Highway Traffic Monitoring- Archived Data Users Service (ADUS) Subcommittee,

2002-present

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TRB subcommittee memberships are open. The subcommittee responsibilities are similar to standing committees (paper reviews, meeting frequency, and establishing research priorities) but on a more focused topic and a much smaller number of submissions to review.

Member of the WCTRS Special Interest Group (SIG) C1 Traffic Theory and Modelling, 2017-present WCTRS SIG committee memberships are nominated by the committee chair and members typically serve 3 year terms. WCTRS created the SIGs to facilitate the exchange of ideas between researchers around the world interested in the same topic area. The SIGs roles are to form the session tracks of the conference. Responsibilities include reviewing manuscripts for WCTRS Conferences and the selection committee of best paper.

Organizing and Scientific Committee, 22nd International Symposium on Transportation and Traffic Theory, July 23-26, 2017, Evanston, Illinois.

International Scientific Committee, Conference on Traffic and Granular Flow, July 19-22, 2017, Washington, DC.

3. LIST OF CONSULTATION ACTIVITY Visiting Professor, Newcastle University, 2017-2018.

5. OTHER PROFESSIONAL/PUBLIC SERVICE TRB Representative at OSU, 2003-present Proposal reviewer for National Science Foundation (NSF) (10 proposal reviews in the time window)

6. ADMINISTRATIVE SERVICE a) Unit committees

Member, Graduate Admissions Committee, Electrical and Computer Engineering, 2011-present Chair, Scholarship Committee, Department of Civil, Environmental and Geodetic Engineering, 2015-

2017. Lead, CE 2050 course standardization, Department of Civil, Environmental and Geodetic Engineering,

2016-2017. Recognizing the problem of inconsistent offerings of CE 2050, in summer 2016 I volunteered to lead a complete overhaul of CE 2050 and develop a new set of standard course material that would be used for all offerings (lecture notes, textbook, problem and exam sets, etc.). This effort culminated with my piloting the new material in the Sp 2017 offering.

7. ADVISOR TO STUDENT GROUPS AND ORGANIZATIONS

Advisor to the OSU student chapter of the Institute of Transportation Engineers, 2002-2017 Responsibilities include: supervise, advise, and guide the officers; liaise with the local and national

branches of ITE; provide general support for the group. Advisor to the OSU Transportation Student Group, 2003-2017

Responsibilities include: supervise, advise, and guide the officers; help with events each term (ranging from social gatherings, to field trips, and hosting speakers); provide general support for the group.

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Advisor to the OSU Aerial Robotics Team, 2004-2017 Responsibilities include: supervise, advise, and guide the officers; liaise with campus entities for

space (ECE) and matching funds (COE); provide general support for the group.