Report of the 2015 NSF CyberBridges Workshop:

Developing the Next Generation of Cyberinfrastructure Faculty for Computational and

Data-enabled Science and Engineering

August 31 – September 1, 2015 Arlington, Virginia

Linwei Wang Thomas Hacker Rochester Institute of Technology Purdue University

Suzanne Shontz University of Kansas

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Table of Contents

1.0 Executive Summary .................................................................................................2

2.0 Workshop Overview ................................................................................................3

2.1 Workshop Attendees ....................................................................................4

2.2 Attendee Selection Process ..........................................................................5

3.0 Workshop Themes and Invited Speakers .................................................................5

4.0 Invited Speakers and Panelists from the National Science Foundation .................24

5.0 Attendee Feedback Survey ....................................................................................29

6.0 Observations from the Workshop ..........................................................................33

7.0 Lessons Learned from the Workshop ....................................................................34

Appendix A. Detailed Survey Results ...........................................................................37

Appendix B. Speaker and Attendee Biographies and Photos ........................................42

Appendix C. Attendee Breakout Session .......................................................................67

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1.0 Executive Summary

The fourth NSF CyberBridges workshop for ACI CAREER awardees was held on August 31 and September 1, 2015 at the CEB Waterview Conference Center in Arlington, Virginia. A total of 31 NSF CAREER awardees and 5 keynote speakers participated in the workshop. The keynote speakers presented on five broad topic areas: Computational- and Data-enabled Science and Engineering, Visualization, High Performance Computing, Future Directions in Cyberinfrastructure, and Grand Challenges in Cyberinfrastructure and Interdisciplinary Research. Each presentation was followed by a focused discussion session during which workshop participants discussed questions posed by the speaker related to the topic area. As in prior years, the workshop provided a venue to facilitate and foster continued discussions among ACI CAREER awardees, keynote speakers, and NSF program directors. Based on attendee feedback from prior years, the workshop this year replaced the poster session held in prior years with a breakout discussion session in which attendees were divided into five groups to discuss research and education challenges they encounter in their work and to discuss potential solutions.

Several broad themes emerged from workshop presentations and discussions.

The need for exascale computing capabilities for faster and more detailed simulations and analysis is becoming clearer. There are significant research and development challenges to be overcome by the community to provide effective and efficient exascale systems that include power limits, fault tolerance techniques, higher level programming languages, and the need for researchers using these systems to be more fully trained in basic software engineering. In terms of non-traditional computing hardware, there is a need for a broader research approach in computer science in the areas of algorithms and systems to fully integrate and exploit the potential of heterogeneous computing resources as an element of the effort to increase computing power.

A common theme from past workshops that was also discussed at this workshop is interest in and the need for an interdisciplinary and multidisciplinary approach that combines the expertise and strengths of faculty across domains. The challenges in this area affecting early career faculty are related to long-term planning and social aspects. The first is the need for deliberate planning to shape a research agenda that affords excellence in the depth and impact of research efforts. The second involves social challenges in terms of creating and managing multidisciplinary efforts, as well as fulfilling the expectations of the department and discipline for tenure and promotion.

The need for better ways of visualizing information was another focus area. There is a need for information visualization to help researchers better understand data from the micro to macro spatial and temporal scales across many domains and to better analyze and communicate high dimensional data both in traditional publications as well as in online dissemination venues.

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The final broad area related to the need for automation of repetitive data collection and analysis tasks and the need for platforms for service-oriented research computing. To help free researcher time from repetitive data collection and analysis tasks (characterized by keynote speaker Dr. Foster as data friction), there is an emerging need for service-oriented computing platforms, such as cloud computing services and storage services, to support research by automating or outsourcing much of the repetitive functions such as data sharing.

2.0 Workshop Overview

Beginning in 2012, the community of NSF CAREER awardees funded by the Division of Advanced Cyberinfrastructure (ACI) within the Directorate for Computer and Information Science and Engineering (CISE) at the NSF has met annually at individual workshops with the objective of providing a venue for CAREER awardees to meet other early career faculty working in the areas of cyberinfrastructure and computational- and data-enabled science engineering and to learn about and discuss advances and pressing problems in topical areas related to cyberinfrastructure from senior faculty leaders. The workshops helped to build bridges among early career faculty and faculty leaders in the areas of education and research that involve cyberinfrastructure. Over the past four years, the workshop has been attended by 71 unique NSF CAREER faculty awardees from 55 unique institutions, 19 unique keynote speakers from 16 unique institutions, and 21 unique NSF speakers and program directors – a total of 111 unique attendees over the four-year period. Of the 71 CAREER awardees, 43 attended one workshop, 15 attended two, 8 attended three workshops, and four attended all four workshops. Two of the keynote speakers (Sushil Prasad and Ed Seidel) attended two of the workshops, and seven NSF speakers and program directors attended two workshops.

The 2015 CyberBridges workshop was attended by 31 NSF CAREER awardees and five keynote speakers from thirty-two institutions. The objectives of the workshop were to encourage the development of collaborations and networking between CAREER awardees and senior keynote researchers, to foster the development of the cyberinfrastructure research community, and to provide feedback to the NSF on the challenges and opportunities faced by the community of computationally-focused CAREER faculty. Similar to prior years, the workshop was structured to provide adequate time for focused discussions and networking centered on the areas related to cyberinfrastructure and computational- and data-enabled science and engineering. The format of the workshop was a presentation by a senior faculty leader in a thematic area related to cyberinfrastructure and computational- and data-enabled science and engineering followed by a breakout session. Each speaker was asked to pose approximately five discussion questions to attendees, who would then gather in breakout groups focused on each question. At the end of the breakout session, a scribe briefly reported on discussions in the group related to the question. In prior years, the workshop included a poster session. However, attendee survey results indicated a declining satisfaction with the poster session. To improve this aspect of the workshop, we replaced the poster session with a focused breakout discussion session in which attendees gathered in pre-assigned breakout groups, and each attendee gave a brief presentation to their group that identified a major challenge related to his/her research and education interests to facilitate discussion within the breakout group on potential strategies for addressing the

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challenge. Attendees noted greater satisfaction with the attendee breakout session compared with the poster session last year.

2.1 Workshop Attendees

Thirty-one NSF CAREER awardees and five keynote speakers attended the workshop, from thirty-two institutions. Fifteen of these attendees were funded at least in part through the Division of Advanced Cyberinfrastructure, while others held awards funded through other five directorates and ten divisions, including Computing and Communication Foundations (CISE/CCF), Computer and Network Systems (CISE/CNS), Mathematical Sciences (MPS/DMS), Materials Research and Education (MPS/DMR), Biological Infrastructure (BIO/DBI), Molecular and Cellular Biosciences (BIO/MCB), Chemical, Bioengineering, Environmental and Transport Systems (ENG/CBET), Civil, Mechanical and Manufacturing Innovation (ENG/CMMM), and Behavioral and Cognitive Sciences (SBE/BCS). Figure 1 summarizes the distribution of CyberBridges 2015 attendee CAREER funding sources.

Figure 1. Distribution of NSF divisions funding 2015 CyberBridges Workshop attendees. CAREER Awards funded through multiple divisions are split evenly between the relevant

offices.

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2.2 Attendee Selection Process

Similar to prior years, active CAREER awardees invited to attend CyberBridges 2015 were selected based upon two primary goals. First, a balance among the divisions funding attendee NSF CAREER awards and attendee home departments was sought, with the goal to bring in an interdisciplinary set of researchers and educators in both cyberinfrastructure-focused and cyberinfrastructure-enabled research. In addition, a prioritized invitation process was used so that ACI funded or co-funded CAREER awardees were invited first, followed by awardees from other NSF directories and divisions whose CAREER research has an important computational- and data-enabled component.

Second, a mix of previous and new attendees was sought with the goal to build a steadily growing cyberinfrastructure community. Among the twelve attendees who returned from previous workshops, four attended one of the previous workshops, six attended two of the previous workshops, and two attended all three previous workshops. This steady Cyberinfrastructure community built from the previous CyberBridges workshops continues to grow through the involvement of sixteen new attendees this year.

Figure 2. Thirty-one workshop participants attended the 2015 CyberBridges Workshop. Shown here is the workshop during the presentation by the Division Director of CISE/ACI.

3.0 Workshop Themes

The workshop focused on five thematic areas that reflect the spectrum of research and education activities in which the Division of Advanced Cyberinfrastructure (ACI) is focused. They

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encompass the types of computational and data-enabled science and engineering (CDS&E) in which ACI and many other NSF directorates are engaged.

1. Computational and Data-enabled Science and Engineering The first thematic area of the workshop focused on computational and data-enabled science and engineering, which involves the development of algorithms and cyberinfrastructure necessary to perform large-scale simulations or to process and interpret data generated from experiments, simulations, models, and observations in science and engineering. Dr. Nikos Chrisochoides gave a keynote talk on the development of medical image computing algorithms, more specifically deformable registration and extreme scale mesh generation algorithms, and led the ensuing discussion among workshop participants. Currently, hospitals use rigid registration (with affine transformations) in order to align intra-operation and pre-operation medical images during a brain tumor resection. Such a technique is used in order to assess mechanical deformations during surgery. However, rigid registration algorithms typically yield greater error than is desired by medical doctors. Instead, non-rigid registration algorithms should be used as the corresponding deformations typically correspond more closely to the actual physics of the brain deformations during surgery. Non-rigid registration can be achieved by solving a partial differential equation (PDE) problem based on the mechanical energy in the brain deformation in addition to the energy needed to match the pre-operative and intra-operative images. High-quality meshes upon which the PDE is solved are crucial for achieving minimal error. There are several challenges of performing non-rigid registration of the brain including: (1) sparse/incomplete data (outliers); (2) partial understanding of all of the parameters; (3) approximations must be used to solve the PDE approximately; (4) the boundary conditions are very noisy; and (5) guaranteed high-quality meshes must be generated. The scale of the problem is such that both the mesh generation and numerical PDE solution must be performed in parallel. Exascale computing is required to solve both problems at greater speeds and across a greater range of scales. This may allow non-rigid registration to be used by neurosurgeons in the clinic, as both low registration error and real-time computing are required in this context. It would also be useful to be able to employ a domain-specific language in which it is fairly easy to specify the organization, data dependency, and type of mesh, and have it be automatically generated by the programming language software. There are numerous other applications where exascale mesh generation can be applied including development of less-invasive patient treatments, treating lung cancer, 3D scanning, 3D printing, and aerospace engineering. Dr. Chrisochoides also encouraged NSF CAREER Awardees to align their research agendas with national priorities, to look for collaborators everywhere (locally, regionally, nationally, and internationally), to conduct very high quality research which will eventually be noticed, and to pursue innovation. He also indicated that it was important to mentor and excite students and help them identify why they should care about a problem; transforming them as citizens is also crucial.

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Dr. Chrisochoides posed the following questions to workshop attendees:

1. Discovery: How does one pursue his/her research agenda in order to have maximum broader impact and align it with the nation’s priorities?

Scribe: Hazeline Asuncion

The breakout group discussed several strategies to determine the national priorities. First, one can review the literature published by federal funding agencies to determine what the priorities are. Second, national priorities are often discussed at NSF grant workshops. Third, in certain areas, panels at conference include program directors as panelists. The program directors are knowledgeable about national priorities and often discuss them when participating on such panels. Several strategies for pursuing a research agenda that is aligned with national priorities were also discussed. First, it is helpful to have a big picture vision for one’s research agenda which includes both short-term and long-term research goals. This is a better strategy than attempting to reverse engineer a research vision from a collection of papers. Second, the research can be repackaged to align with national research priorities in accordance with one’s long-term research goals. In order to do this, it is important to articulate how the proposed research fits with national priorities. This allows one to conduct the research he/she is interested in while also making it more probable to obtain increased funding for the research. A third strategy is for senior researchers who often have greater understanding of national priorities to give input to junior faculty as to the national priorities. Finally, information can be shared in regards to best practices for pursuing research and establishing a lab. This can help give faculty momentum.

Several different ways in which one’s research can have greater impacts were also discussed by the group. In particular, a faculty member can be intentional about establishing outreach events for K-12 students. For research involving studies or participants, feedback can be given immediately to participants of the study. Through collaborations, research can have impacts on other areas of science. Technology transfer can also be undertaken for successful projects in collaboration with industry.

2. Teaching: What does it take to have long lasting impact as a teacher scholar?

Scribe: Lorena Barba

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In order to have long-lasting impact as a teacher scholar, Dr. Chrisochoides indicated that it was important to mentor and excite students in addition to looking to make a permanent change in the students that makes them better citizens. To achieve this, faculty should seek to put computing in context, especially on topics that students care a lot about (e.g., the application of computing to health or climate). To be more effective as teachers, faculty should become informed on new approaches to teaching which are supported by the learning sciences, such as using a flipped classroom or hands-on demonstrations in the classroom (to achieve active learning) or incorporation of technology. Collaborations with other teachers brings a social aspect to teaching that we already experience in our research collaborations. In order to share our knowledge of teaching with others, we should publish our teaching materials and share them openly through open courseware, open education, and open-source lessons.

3. Knowledge Integration: How does one train highly competitive Ph.D., Masters, and

undergraduate (REU) students that feel comfortable working within multi-disciplinary teams?

Scribe: Shantenu Jha

The group discussed three main issues in the area of knowledge integration. First, faculty must become comfortable doing multidisciplinary research before they should expect to train students to be comfortable doing multidisciplinary research. Second, in order to train students in a multidisciplinary way – both for teaching and research – something else must be given up. It is not possible to tack on a significant amount of additional knowledge in another area. What should be given up and how is difficult to judge. The nature of multidisciplinary research needs to be examined, particularly as it ties to undergraduate and graduate education.

4. Outreach: How does one create learning opportunities to motivate and excite

undergraduate and high-school students to pursue studies in STEM education?

Scribe: Ioan Raicu Numerous strategies for getting students interested in studies and careers in computer science and engineering were discussed by the breakout group. First, children should be exposed to programming concepts as early as first grade. For example, young students can be introduced to visual programming with Scratch. Second, it is important to encourage students to pursue computer science and engineering early on with activities that expose them to computer science and engineering. Engaging students through fun games that teach them important computing skills is one way to do this. Third, in order

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to get children interested in careers in computer science or engineering, it is important for them to have role models and/or mentors in these disciplines that they can observe doing computer science or engineering. Only a small percentage of the high schools in the United States have courses in computer science or engineering, and hence there is a large shortage of students graduating from American colleges having majored in computer science or engineering and filling the top paid jobs in industry. Fourth, some faculty have found great success with reaching out state-wide to high schools with hands-on activities ranging from robotics to coding to working with Scratch. Fifth, faculty can look to the DiscoverE (for engineering) and Code.org (for computer science and programming) websites for focused material. Sixth, we need to educate the workforce so that they will encourage computing more, such as in mathematics, physics, chemistry, etc., where computing can be integrated with the science being taught. If computing is integrated in these disciplines in an appropriate manner, students will have the skillset needed to use computing to advance in their fields upon receipt of their science degree. Seventh, at the high school level, some faculty have involved students in small manageable research projects together with undergrads and graduate students. In addition, high school students can participate in opportunities at Department of Energy national laboratories (e.g., Argonne National Laboratory, Los Alamos National Laboratory, Lawrence Berkeley National Laboratory, etcetera) and can get involved in student competitions in computing. The competitions are an ideal testbed in which to engage undergraduate students, as well. Finally, undergraduate students can also get involved in NSF Research Experiences for Undergraduates (REU) programs which will also encourage them to pursue graduate studies.

5. Innovation: How does one initiate entrepreneurial opportunities to transfer technology

from University Labs to industry and create new sources for funding in challenging emerging areas in Computational- and Data-enabled Science and Engineering?

Scribe: Liqiang Wang

The group discussed several ways in which faculty can initiate entrepreneurial opportunities to transfer technology. First, faculty can start by participating in consultation to accumulate additional experience in industry and marketing. This will give them credibility for working in a niche market related to a specific area/application. Second, federal agencies may provide grant opportunities, such as SBIR/STTR, to support start-up research-oriented firms. Computing resources could be made available besides just grant funding. Third, universities and states often have small internal grants which could be used for faculty and students to start-up such efforts. Fourth, angel investors and venture capitalists are always investing in innovative technology.

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Other issues involved in the tech transfer process include the need for user-friendly interfaces for products developed. In addition, the products should be even better than those generated in the lab. Copyright and intellectual property issues also need to be handled properly.

2. Visualization The second thematic area of the workshop focused on Visualization. On this topic, Dr. Katy Borner gave a keynote talk on information visualization that can be used to enable us to understand the increasingly large amount of data across micro to meso to macro scales, in meeting different insight needs such as statistical, temporal, geospatial, topic, and network analyses. In particular, Dr. Borner’s keynote touched on three current states and challenges of information visualization. First, Dr. Borner talked about how information visualization can be used to visualize the dynamics behind science and the feedback cycles. Dr. Borner gave examples of how different visualization techniques can be used to study the evolution and impact of co-authorship networks and scientific collaborations across individual, regional, national, and global levels, to map the topic evolution and knowledge diffusion within and across different fields, to understand the feedback cycle such as the impact of different funding mechanisms on science, and to evaluate the impact of sciences by studying how they are cited and used. All of these examples demonstrate that we are at an era where information visualization can be used to understand the dynamics of science itself, towards the future of science forecasting. Second, within the quick-changing science landscapes nowadays, Dr. Borner discussed the needs of new visualization tools that can enable the efficient sharing of algorithm components among domain scientists. An example is the “plug-and-play macroscope” developed by Dr. Borner and collaborators, which supports the design of modular, compatible algorithm and tool plug-ins that can be easily combined into scientific workflows and packaged as custom tools. Such visualization tools will enable domain scientists to assemble existing and most up-to-date plug-ins (continuously changing bundles of software) into workflows with little or no help from computer scientists, which will have a significant impact on promoting knowledge diffusion and interdisciplinary collaboration. Finally, Dr. Borner touched the aspect of “open education”, using her Information Visualization MOOC course, which currently involves students from about 100 different countries and more than 370 faculty members across the globe, as an example. Dr. Borner posed the following question to workshop attendees:

1. What data analytics and visualizations would be most useful for your R&D?

Scribe: Ningfang Mi

Discussions of this group mainly centered around the question of how to use visualization tools to better present research outcomes in publications, e.g., visualization beyond 2D

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figures and tables. The group discussed the possibility of including hyperlinks in publications that are tied to visualization results that can help better present experimental results and algorithms. This possibility is tied to challenges including how to effectively visualize high-dimensional data so that such results are easily interpretable, where to store such data, and visualization tools that can direct the audience’s attention to “regions of interest” within the massive information. Many of these challenges also apply to how to facilitate the researchers (faculty and students) to interpret and analyze their own experimental data and algorithm results.

2. What functionality would your “dream-come-true” visual analytics tool have?

Scribe: Baskar Ganapathysubramanian

The group discussed several needs revolving around this question. The first main need reflects the fact that data and algorithms often come in tandem. A dream-come-true visualization tool should then integrate the data with the algorithm, look at the attributes of the algorithm such as uncertainty quantification and propagation, and allow on-the-fly steering of algorithms. The second main need centers around the support of software parallelization. A dream-come-true software will be one that supports visual representation of how software is parallelized, memory management, leaks, etc. Additional discussions touched upon the needs of parallel visualization software that will scale to exascale machines, visual tools for large network layout (e.g., a plugin), and software that can allow easy and versatile attribute selection. During this discussion, keynote speaker Dr. Borner also suggested to the attendees that researchers with specific visualization needs can submit proposals of visualization projects to IVCMOOC courses, where some projects may get picked as course projects. Such projects may help address specific visualization needs from diverse communities, may help inform visualization researchers of the current needs, and may provide students from the courses with valuable opportunities of research engagement and potentially research publications.

3. What “data visualization literacy” is needed in your domain? What should an IVMOOC teach?

Scribe: Qing Chang

This group includes faculty members from a diverse range of backgrounds including computing security, computational fluid dynamics, computational biology, GIS, and mechanical engineering. From discussion, it was clear that data visualization literacy is closely tied to specific needs of specific domains. However, in general, the group believes the general requirements of data visualization literacy includes the following aspects: the ability

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to understand data visualization, to present data through visualization, and to understand the caveats in data visualization such as its incompleteness and uncertainty. In terms of education, the group highlights the importance of integrating technical and art education in future education.

3. High Performance Computing Dr. Valerie Taylor discussed the tradeoffs between performance, execution time, and power use of high performance computing systems used for large-scale scientific applications. To stay within power constraints for exascale systems, the community will need to improve performance without significantly increasing power use and to create more power-aware applications. She discussed the mechanisms available today to measure power use, such as PowerPack and PowerAPI. She discussed techniques to conserve power based on both hardware techniques, such as resource sleeping techniques, and software techniques, such as dynamic voltage scaling. She described her experience with a scientific application (GTC) using power conserving techniques to reduce both runtime and power consumption. Dr. Taylor described the MuMMI (Multiple Metrics Modeling Infrastructure) project, which seeks to exploit available system information on performance and power consumption collected during application runtime to predict and analyze application performance and energy use. She described examples of using MuMMI to reduce execution time and energy use for scientific applications. For one application, her research team found that they could significantly decrease network energy use. The challenges facing the community involved in addressing power and performance issues include the need to build a database of application refinements to improve the performance and energy use of applications, developing new techniques for modeling and analysis, and incorporating these new techniques to manage performance and power use within scheduling systems for future systems with a constrained power budget. The questions Dr. Taylor posed to the attendees were:

1. Future high end systems will likely have a power budget. How do we address the power budget from a scheduling viewpoint? What about the application viewpoint?

Scribe: Waheed Bajwa

The group discussed the issues of scheduling and power consumption. First, adding power to the scheduling will increase the complexity of the problem. From the application perspective, it could help, but adding awareness of power consumption and scheduling would also add complexity at the application level and make it even more difficult for programmers. What is important is to consider this issue from an overall return-on-investment perspective.

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2. Exascale systems are expected to reach the level of billion way hardware concurrency. How do we go about addressing this level of needed concurrency, which may be three orders of magnitude more than needed today? Scribe: Kamesh Madduri

The group considered the problem of achieving exascale performance from two perspectives: the application and the system. From the applications perspective, the need is to exploit parallelism and concurrency at all levels of granularity, and to restructure applications based on the target hardware. There is a hierarchy of capabilities within the hardware, memory, and network that could potentially be exploited to improve concurrency. These capabilities include tightly coupled cores on a chip (which could be used for speculative execution) and parallel network paths. Conceptually, developers could seek to exploit concurrency as early as possible in the problem definition, rather than waiting until later. Some questions to consider for application design are: what are the implicit layers of parallelism within an application? What appropriate tools are available? Where can these tools be used within the system architecture hierarchy? The other perspective is from the systems side. One possible approach is to exploit parallelism implicitly through the use of languages and tools, which may be able to ease the process of discovering inherent parallelism and simplifying the issues involved in the manual reasoning used today for synchronization and communication.

3. Another key issue with exascale systems is that of reliability. Given the large number of

components (CPUs, memory, etc.) with such systems, it is expected that there will be a high rate of faults. What is needed to develop applications to adapt to the high rate of faults? Scribe: Rong Ge The group discussed two approaches for responding to faults: reactive and proactive. The drawback of a reactive approach is that techniques such as checkpointing fail to scale. A proactive approach could potentially be used to help avoid faults using techniques such as the analysis of system logs to predict the future state of individual nodes within the system and potentially help the scheduler avoid using low reliability nodes. To predict hardware failures, several possible approaches were discussed that included analyzing system logs, hardware counters, and semantic analysis to search for relationships between causal events and their effects. In terms of software failures, the group discussed the potential causes of failures that included software defects, memory

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leaks, and hardware failures that manifest themselves as software failures. The group also noted that MPI programs are prone to failure due to software defects.

4. Given that high-end systems are changing rapidly, how do we address this rapid change so that applications can execute efficiently on new systems? Scribe: Kaushik Chowdhury

There were two main viewpoints expressed in the group. The first is that there is a need for a layered programming architecture that would use a middleware layer that could convert high level code into something more platform specific. One possible approach for this is to use agent-based systems that could be deployed that target system-specific hardware to act as the middleware to convert the high level programming into something system-specific.

The second viewpoint is that some guidelines and coding standards are needed that could be followed by developers writing research code. One of the goals of this would be to ease the handoff of codes between generations of graduate students.

5. What are the key curriculum issues that need to be addressed to prepare undergraduate students to be prepared for jobs that utilize high end systems or graduate programs in HPC? Scribe: Behcet Acikmese

The group discussed the issues involved in curriculum that need to be addressed to help to prepare undergraduate students. Several potential approaches were discussed. One possible approach is an early start to programming, e.g., starting in middle school. A sustained effort is needed for teaching programming through the middle school, high school, and undergraduate levels. At the undergraduate level, a course in high performance computing that includes the basics of computer hardware and networking along with undergraduate courses in basic algorithms and numerical analysis would also be helpful. Finally, a topic called “software carpentry”, which focuses on basic best practices for software development, might be another useful addition for students.

4. Future Directions in Cyberinfrastructure Dr. Ian Foster described several new and emerging technologies that are helping to solve difficult problems in cyberinfrastructure. The first technology he described is the growing outsourcing and automation of computing in the form of cloud computing technologies. Companies are now offering cloud computing software, platform, and infrastructure services that

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can be used by laboratories. The availability of software as a service has the potential to transform the ways in which computing is used within laboratories for research by reducing the cost of many data-oriented activities that take place today within a laboratory. The question is: what would be involved in providing software as a service for laboratory activities rather than “shrink-wrapped” software. The problem was characterized by Dr. Foster as a problem of “data friction”, which are obstacles to data access, discovery, sharing, and analysis that slow the progress of discovery and represent “mundane activities” that could perhaps be automated. Dr. Foster described the Globus project, which is focused on providing essential research data management services and data movement between data sources and computing resources. The Globus group has been expanding the suite of services to support services such as data sharing, access control, the publication of data, data curation, and metadata extraction. Dr. Foster described some of the services provided by Globus, and some of the advantages of providing software as a service compared with the traditional approach of providing downloadable software. He described the research challenges involved in the directions needed to provide software as a service for research communities. These include determining the parts of science that can effectively be automated and outsourced, identifying the technical obstacles that need to be overcome, developing a sustainability model for funding efforts in providing software-as-a-service, how to evaluate the effectiveness of services, and issues involved in providing services and apps that span providers such as security, provenance, and reproducibility. Dr. Foster described some future directions and challenges in using services to accelerate data-driven discovery, which include data discovery and accessibility, automating the capture, indexing, and the linking of data. Another area is the need for “discovery engines”, a new type of scientific instrument that can facilitate computation over large sets of data. Dr. Foster posed several questions to workshop attendees:

1. What do you see as the biggest barrier to data- and computation-driven discovery in science and engineering?

Scribe: Judy Qiu

There are several large barriers that the group discussed. One barrier is that there is not just one data step or issue, such as the transfer of a large volume of data, but instead there are barriers involved in a pipeline of data transformations that change over time. One example of this is the question of automated data collection, and the challenges inherent in interdisciplinary use of data (such as vocabulary, data formats, and interpretation of the data). The computational and storage platforms are often heterogeneous, which complicates management of these resources. Another aspect of the problem involves the education, training, and social aspects. In terms of education and training, the issue is developing techniques and practices to bridge the gap among

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communities, and fostering mutual understanding across different science domains. In terms of the social aspects, there are social and organizational problems. The question is how to train faculty and students to take responsibility to communicate and collaborate across disciplines. Interdisciplinary work requires significant overhead.

2. Imagine a service to which you could outsource routine research processes in the same

way as you outsource email and search to Google and shopping to Amazon. What capabilities would that service provide? How would it change your science? Scribe: Kamesh Madduri

The group discussed several potential services that could be used as part of a strategy to outsource routine research processes. These services included: a code optimization service, a parallelization service, portability services, better debugging tools, memory profiling tools, visualization, and data conversion services. Another service mentioned was a service to automatically organize the email of research projects. The overall goal of these services would be to increase the productivity of researchers, and to reduce the time spent searching.

3. What cyberinfrastructure skills do you see most in demand for your students? What is

most lacking? Scribe: Kaushik Chowdhury

The discussion generated a list of cyberinfrastructure skills that are needed by students as well as faculty. These skills were in two categories: sharing and resource management and code development skills. In the first category, what is needed are file management systems for historical use and sharing with external entities, cloud based collaborative paper and code writing platforms and software. On the code development side, what is needed are skills in version control and online code repositories, understanding how to translate procedural code into parallel implementations for parallel processing, basic Linux/UNIX skills and shell scripting for non-computer science students, and project management tools for distributed projects based on standard tools used in industry.

5. Grand Challenges in Cyberinfrastructure and Interdisciplinary Research Dr. Clint Dawson presented on “Grand Challenges in Cyberinfrastructure and Interdisciplinary Research”. In his presentation, he described how computers can be used to simulate and predict

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hurricane damage from storm surge. He then used Hurricane Katrina as a case study to illustrate how computational simulations could be used to determine how the damage caused by Hurricane Katrina happened and what caused it to happen. An important ingredient of the simulations is their ability to accurately predict high water marks. Dr. Dawson described several grand challenges common to cyberinfrastructure and interdisciplinary research through his case study in storm surge modeling and simulation. The challenges include the need to: (1) filter, process, and efficiently combine data from multiple sources (in the absence of central repositories, standards for data, and open source tools to use) in order to convert the raw data to input data; (2) model the physical problem accurately including the description of the domain and at the level of the parameters; (3) develop algorithms that scale well on today’s cyberinfrastructure (e.g., hybrid CPU/GPU architectures); and (4) run the simulation while simultaneously performing the analysis to determine how to improve the simulation. This may lead, for example, to new sources of data, a more complex model, the incorporation of uncertainty quantification of the data and simulation, or the use of another numerical method that scales better on today’s cyberinfrastructure. Analysis tools are still often slow, difficult to use, employ only the simplest data formats, and are not of sufficient resolution as required for many real-world applications. Another challenge is keeping users in academic, government, and consulting abreast of any large-scale changes to code that researchers may decide to make as a result of the analysis. Dr. Dawson then described two cyberinfrastructure projects he is involved which involve simulations of hazards. The first project is the STORM project in which the research team is developing next-generation storm surge modeling capabilities for use with novel high-order algorithms and novel algorithms for parallel data management. The algorithms are being developed for use in simulations of storm surge to be run on novel parallel architectures. The second project is the DesignSafe CI Cyberinfrastructure project in which the research team is developing cyberinfrastructure for use with earthquake and wind-related hazards research. For this project, experiments will be used to inform the simulations. The DesignSafe CI cyberinfrastructure is based on the iPlant cyberinfrastructure. Dr. Dawson ended with some advice for the NSF CAREER Awardees. He encouraged the junior faculty to teach applications people how they can benefit from the cyberinfrastructure research and about the skill set one has to offer. He also recommended connecting with people outside of one’s own building in order to grow one’s career. The questions he posed to the group were:

1. What, in your opinion, are the types of cyberinfrastructure that span multiple application domains?

Scribe: Han Liu

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There have been numerous technological advances which allow us to integrate hundreds and soon to be thousands of heterogeneous cores (e.g., CPUs, GPUs, and application-specific accelerators) on a single chip through exotic communication infrastructures (e.g., networks-on-chip). We can create and reconfigure the topology to sustain various interaction patterns that are characteristics of Big Data applications. Yet, much of the current focus is on how to modify the algorithms to fit in the old architectural paradigms and not on how to take advantage of the new exotic technologies. We believe that cyberinfrastructure research efforts should focus on embracing these new technological advances and develop optimization algorithms and design systems that can truly scale to millions of tasks for which data scientists are aiming. This will imply a cross-cutting effort from the application/software level to the algorithmic level to the hardware/system/data-center-on-a-chip level.

Application task mapping, scheduling, communication routing, and related optimization issues can no longer be solved in a centralized fashion in the big data era. From the system perspective, there are a number of optimization problems that need to be solved in order to embrace decentralization and self-organization as basic principles to be adopted for use in overcoming the curse of dimensionality. More precisely this implies the need to design randomized algorithms that implement adjustable localized interaction rules that self-optimize the platform. Simply speaking, the architecture will morph to match the application demands and will reconfigure if new real-time requirements are enforced as data or data structures if new tasks are coming in. These network-on-chip, many-core platforms will dramatically change the landscape of high performance computing and related research as we know it. In addition to designing these systems, there is a stringent need for developing performance models to predict the performance of an application running on various architectures. Stochasticity, heterogeneity, interdependence, and various levels of locality are important factors that can affect the overall system solution and hence need to be addressed.

From the data and application/software perspective, there are a number of issues, as well. First, data provenance is an important concern across application domains. All domains have scientists who need to understand the history of the data (e.g., what processing steps were performed, who performed them, where the data came from, etc.). Second, software traceability focuses on the issue of understanding the software others have written and the ramifications of changes made to the software. Domain scientists need to be able to trace the software given that more software is used to analyze and visualize data across application domains. It is also important to note that application domains also pose restrictions or constraints to the problems. Several application domains which are the main drivers for cyberinfrastructure research involve large-scale nonconvex optimization, large-scale simulations, and management of databases.

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Another important perspective is the design of cyberinfrastructure components. In particular, the group discussed the need to design cyberinfrastructure components that can enable or improve the performance of current interactive public spaces such as HUBzero (a platform for scientific collaboration), the iPlant Collaborative (a platform for life scientists to connect to public datasets, and manage and store their own data and experiments), and OSGi/CIShell (a tool which is powered by Macroscopes that support the plug-and-play of datasets and algorithms and their bundling into custom tools that serve the specific needs of a user group or research community).

Cyberinfrastructure components have also been directed for use with educational activities. For example, Indiana University has created an Information Visualization MOOC. The course is in self-paced mode right now but will be taught for credit again in January 2016. The course teaches the theoretical and technical skills to create information visualizations. It also gives students the opportunity to work with real-world clients and data as a team.

2. What mechanisms are needed to best connect developers of cyberinfrastructure with users of cyberinfrastructure?

Scribe: Richard Hennig

Several different ways in which developers of cyberinfrastructure can be connected with users of cyberinfrastructure were discussed by the group. One way in which the two groups can be connected is through the development of a prototype of the cyberinfrastructure that can be shown to potential users in order to obtain early feedback. In addition, the users can give input on extension of features. It should be noted that, even if some aspects of the algorithm or software do not survive, user feedback is still useful, and the project is still a success if the ideas are generated in other cyberinfrastructure. Another mechanism for connecting cyberinfrastructure developers with users is by developing a public relations campaign, e.g., by giving tutorials and putting on demonstrations at conferences or workshops, putting together videos on the cyberinfrastructure, and creating websites that explain how the cyberinfrastructure works. Hubs, such as Nanohub, can also be used to promote one’s software and distribute tutorials to the community. Software can also be delivered on a virtual machine or using a container approach in order to overcome barriers and encourage users to try out new cyberinfrastructure.

In order to obtain a critical mass of users for cyberinfrastructure, a significant amount of work is required initially. At some point, using word-of-mouth to spread information about one’s cyberinfrastructure will accelerate its use. On the flip side, if the cyberinfrastructure spreads too quickly, issues can be created for the development team

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which will have trouble keeping up with the fast pace of the adoption. In particular, the cyberinfrastructure may not be ready to work for just any user requirement. Thus, it is important to set the right expectations and not over promote your tool. One way to address the issue is by building a larger team of developers by moving to an open software development platform (e.g., Github) to allow the cyberinfrastructure development and growth to become a more sustainable effort.

3. How do you envision your research fitting into an interdisciplinary research team? What

do you see as the pros and cons of single investigator vs. interdisciplinary research projects?

Scribe: Adrian Feiguin

The group discussed how they envisioned their research fitting into an interdisciplinary research team including the challenges associated with such efforts and how to address them. The first challenge the group identified was how to connect to people from other disciplines both within and outside one’s institution. In particular, how can one go about engaging researchers from various disciplines and convincing them to pursue a common project? Interdisciplinary workshops, such as the NSF CyberBridges Workshop, help in connecting researchers from various disciplines. However, once potential collaborators are identified, there are language barriers associated with discussing research with researchers in different areas. Thus, it is important to learn about the second research area, the vocabulary of the area, and what the important research problems are in that area. This will help inform an interdisciplinary research project.

When discussing potential interdisciplinary research projects with collaborators, it is also important to determine what funding may be available for the project. Universities often will provide a small amount of seed money to an interdisciplinary team looking to obtain preliminary data/results for use in a research proposal. Alternatively, it is sometimes possible to submit a proposal to conduct exploratory research. Such proposals are often considered high risk, and it is important to mitigate the risk.

Another important question when writing an interdisciplinary proposal is how to go about writing it. If one is a younger faculty member, it may be that he/she should consider writing the entire proposal him/herself. This will force one to put in the extra effort to learn new things associated with the other research area. It will also ensure that the various components of the proposal are more tightly integrated. The broader impact section of an interdisciplinary research proposal requires special care in that hand-waving and buzzwords no longer work. Instead, a real broader impact must be described. In order to address this, one can seek areas where similar

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techniques/methods have been applied (e.g., computer science, machine learning, etcetera).

Writing proposals and obtaining funding for larger research projects is more challenging. In particular, one must first determine the potential sources of funding for a large project. This involves talking to numerous program directors associated with the various research areas of an interdisciplinary proposal. Program directors at NSF may encourage the team to try to obtain EAGER or RAPID funding as a means of obtaining initial funding; however, very few such proposals are funded per year. If the team decides to apply for funding from an existing research program, it is important to identify the main message and determine how to package the research so that it fits into the existing program. It is also important to describe the direct/indirect relevance of the proposed project to the existing program. Alternatively, a larger project can be partitioned into smaller projects, and each part of the larger project can be submitted as a smaller research proposal to the division most relevant to that part of the project. The challenge with this approach is that it may be the case that some parts of the project get funded, but not all of the parts of the project are funded at the same time. Then it becomes challenging to determine how to advance the research of the entire team in such a case. Certainly, one advantage of obtaining funding for a larger project through a single proposal is the larger amount of research funding that comes with it. However, some of the largest projects come with funding that is to be used for graduate student salaries and not faculty summer salary.

Independent of the type of funding obtained, there are also challenges associated with working with a large research team. In particular, the PI finds himself spending a significant amount of his/her time directing a large team towards proposal writing and conducting the research once the project is funded. In order to achieve this, it is important for you to get to know your collaborators and choose team members that will have good chemistry with each other. Sometimes research groups can conduct a student exchange in order to get to know the other group’s research.

4. Is your institution appreciative of interdisciplinary research? Are reward structures in place to encourage young faculty to participate in multi-investigator projects? If not, how best to change the culture?

Scribe: Melissa Smith

The current culture at many research universities is such that the upper administration puts the highest value on the amount of funding and the number of publications in science and engineering departments. Of less importance is whether the funding came from a

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single PI or multiple investigator collaboration. Further, they may value large collaborations that bring heightened awareness and/or prestige to the university.

One challenge for junior faculty is that the senior faculty who decide on one’s tenure and promotion tend to see multiple investigator research projects as diversionary and a risky path for junior faculty to follow for their research. These senior faculty tend to value individual work more, particularly high impact scholarly work. This is likely due to an existing generational divide between younger faculty and their tenure and promotion committee, department, and dean that view single and multiple investigator research very differently than themselves.

In order to change the culture, junior faculty need to place value on more general scholarly activities or products that are often produced in collaborative work. For example, it should be determined how to weigh publications versus other products, such as tools, data, software, etcetera, that are produced from the research.

Changes to the tenure and promotion process are also needed in that mechanisms that value/measure output from a collaboration should be introduced into the process. For example, letters of support from collaborators could be introduced into the process. Such a change is important in that science is increasingly collaborative and NSF recognizes this. Hence, it is worth the effort needed to try to get promotion and tenure committees, departments, and deans to adopt the idea.

5. In general, is computer literacy encouraged by your institution, and if so, by what

mechanisms? (This group also addressed the question: How is cyberinfrastructure research integrated into the classroom?)

Scribe: Hong Qin

The group discussed several of the challenges associated with integrating cyberinfrastructure research into the classroom. One major issue is the sustainability of a continuously changing curriculum. It can be difficult for faculty to keep up with all of the latest cyberinfrastructure research trends in addition to integrating them into the curriculum. Similarly, there is typically outdated technology in the classroom, as universities cannot keep up with the continuously changing landscape. In particular, the technology is already outdated when it arrives in the classroom. One reason for this is there is often little money for teaching infrastructure/support. The lack of current technology makes it challenging to cover novel cyberinfrastructure trends in class. It is also difficult to integrate concepts from research programs into the classroom because the concepts are less polished than what is typically required for adoption in an undergraduate class. Another challenge is that purchasing of mobile technology devices

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is often discouraged, despite this being a current cyberinfrastructure trend. Thus, faculty may be encouraged to consider emulated environments, where available.

In regards to computer literacy, one of the key challenges is teaching students both the subject area content along with programming skills in a balanced manner. For engineering or science, this is more challenging than it is for computer science, for example, where the topics are more closely connected. General computer literacy courses are too simple for science/engineering students, whereas programming courses in computer science involve too many advanced concepts and do not cover enough applications of interest to science/engineering students. The solution has been to create a version of Introduction to Programming for engineering students. Programming in such courses is typically done in either Python or Matlab which are seen as easier to program in than an actual programming language, such as C++, for example. In addition, such courses sometimes include applications of interest to engineering students. The downside of using Matlab in such courses is that it does not provide students with a sufficient understanding of computational concepts. Hence the ideas do not always translate to a programming language like C++ that the student may be expected to be able to program in for solving an engineering problem in a more advanced course. In particular, higher level undergraduate engineering courses do not spend any time on teaching computer programming due to the complexity of the engineering problems being taught.

Figure 3: CyberBridges Workshop attendees network during a break.

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4.0 Invited Speakers and Panelists from the National Science Foundation

Dr. James Kurose discussed numerous opportunities in computational- and data-enabled science and engineering research related to cyberinfrastructure within the ACI in the CISE Directorate at the NSF. In particular, he mentioned several national initiatives tied to cyberinfrastructure research in the areas of big data, robotics, understanding the brain, and high performance computing. In addition, cybersecurity was the focus of a recent national initiative. Research in these areas supports the mission of the NSF, which is to “promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense....”, according to the NSF website. Dr. Kurose also discussed the NSF’s Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21). Within the CIF21 framework, he identified several themes for cyberinfrastructure-related research including big data, data infrastructure, sustainable software, sustained innovation, data science and engineering, data science pilots, multiscale modeling, and optimization of coupled systems, among others. Dr. Kurose also encouraged workshop attendees to develop personal interactions and collaborations, particularly since science is increasingly done in larger groups. This is particularly true of interdisciplinary research. He also encouraged attendees to think big in their careers and to focus not only on the science but also the long-term impact of the work. The importance of good communication and writing, particularly with respect to proposal writing, were stressed by Dr. Kurose.

Ms. Irene Qualters discussed the importance of cyberinfrastructure and how NSF investments in cyberinfrastructure are transforming science and driving economic competitiveness. as well as informing international policies. She discussed some of NSF’s history in cyberinfrastructure, which began with high performance computing. She described the growth in the use and demand for NSF supported cyberinfrastructure. The number of active institutions has doubled, the number of PIs per project has tripled, and the number of active users has quintupled. Ms. Qualters described how research cyberinfrastructure is a key component to the investment strategy for NSF. She described several NSF programs, which included DIBBS, BIGDATA, and described the Research Data Alliance, the funding group for Collaborative Research E-Infrastructures (CRE, which is focused on international cooperation on coordinated funding calls for proposals), and investments in national and international networking to improve connections. She also described computer networking activities within ACI that seek to improve national and international network connectivity for science. On the software side, ACI is continuing the software strategy focused on software elements through software institutes that seek to create and sustain software infrastructure. Learning and workforce development efforts are on two fronts: CI-focused cyberscientists, who focus on developing, piloting, and delivering new capabilities, and CI-enabled domain scientists seeking to explore and exploit new capabilities. She described ACI efforts in engagement with the community, specifically a report under development by the National Academy of Sciences and efforts within the National Computing Strategic Initiative (NCSI). Ms. Qualters described some of the detailed objectives in the NSCI Executive Order. NSCI objectives 2, 3, and 4, focused on increased coherence between

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computation and data, establishing a viable path forward for future HPC systems, and increasing the capability and capacity of the national HPC ecosystem, are closely aligned with the NSF mission and priorities. In summary, NSF’s cyberinfrastructure investments are yielding scientific breakthroughs, ongoing community engagement will help to inform future strategic directions, and optimizing these opportunities will help maintain leadership in advancing the frontier of science and engineering.

Dr. Amy Apon introduced several main NSF CISE solicitations according to the way they are structured, starting from core programs within each division in CISE, to cross-division solicitations, such as BIGDATA and CPS, and cross-agency solicitations, such as the BRAIN. Dr. Apon emphasized the importance of knowing and fitting the contribution of one’s proposal to the audience and what is being sought for of each solicitation, for example, whether the proposal emphasizes core computer science innovations or interdisciplinary contributions. Dr. Apon also discussed the EAGER mechanism and how it allows investigators an opportunity to interact directly with the NSF program director on research ideas that fall outside the scope of regular solicitations.

Dr. Karen Cone described proposal opportunities at the interface of BIO and CI. After highlighting challenges associated with the archiving, dissemination, integration, and analysis of multi-scale, heterogeneous, and big biological data, Dr. Cone showcased two BIO major data investments: one is the iPlant Collaborative that supports a CI platform for high performance computing on grand challenge questions in life sciences, of which many spin-off projects were seen in the CyberBridges workshop beyond the original project. The other is the project NEON (National Ecological Observatory Network) that supports a platform for continental-scale collection of terrestrial and aquatic data to enable examination of ecological change over time. Dr. Cone further introduced the structure of the BIO Directorate, the interests of each division, and outlined funding opportunities in BIO with a computational emphasis, funding opportunities using NEON resources, and funding opportunities to promote education at the interface of BIO and CI. Dr. Cone also echoed with Dr. Apon multiple times in the importance in making the scope of a proposal appropriate for the solicitation and directorate to which it is being submitted.

Dr. Srienc Friedrich described the ENG/CBET structure and funding opportunities within the division that have computational focuses. In particular, Dr. Friedrich discussed in detail the areas of emphasis of the Biotechnology program and emphasized its focus in not only generating but also using knowledge in biology. Using concrete examples, Dr. Friedrich illustrated the need for computation in biotechnology, ranging from the analysis of the DNA sequence, to network analysis, to simulation of a single to a population of cells. Dr. Friedrich also emphasized and encouraged CyberBridges attendees to take advantage of supplement funding opportunities for current CAREER awardees, such as the research opportunities in Europe supported by the NSF.

Dr. Nigel Sharp discussed some of the pressing problems in astronomy and astrophysics that could benefit from the involvement of researchers with expertise in cyberinfrastructure and high

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performance computing. One of the current research questions being highlighted is crowd-sourcing and citizen science and the question on whether we can take the need for a large amount of human input out of the loop. Dr. Sharp also gave valuable advice to the workshop attendees, including where to look for funding programs and paying attention to the change of their names that may affect where to find them, the constant change in NSF program officers and solicitations, and the need for the investigators to be up-to-date with these changes, the importance of getting to know the program officers in the areas of one’s interest, and the key ingredients when reaching out to program officers in discussing a potential project. Dr. Sharp also pointed the workshop attendees to the NSF Grants Conference where the investigators can be further educated on their interactions with the NSF.

Dr. Sushil Prasad described a vision within ACI focused on “Innovations in Cyberinfrastructure Education and Workforce Development” in which NSF seeks to build robust career paths in CI and CDS&E. He described funding opportunities including CRII, which is designed for early-career junior faculty, and NRT, which is designed for graduate education. Dr. Prasad also encouraged CAREER awardees to provide feedback to the NSF and to help the NSF identify curricular gaps in domain/STEM discipline for computational/HPC topics, needs in updating and sustaining educational infrastructure in different computational disciplines, gaps in funding programs, needs of access to advanced resources arising from research, education, their fusion into industry, and challenges in obtaining recognition and sustenance of innovations and artifacts within investigators’ institution, community, and society.

Dr. Jack Snoeyink emphasized the importance of and provided advice for interdisciplinary collaborative work, including how to collaborate with domain scientists as computer scientists who are interested in methods, what types of contributions computer scientists can make in such collaborations, and how to keep a balance in making contributions between one’s own areas and interdisciplinary efforts. He also pointed out that CISE funds PIs in many disciplines outside of computer science.

Program Director Panel Breakout Notes

1. What kinds of programmatic approaches could NSF consider to facilitate interdisciplinary research for early career faculty?

Scribe: Thomas Hacker

The group discussed the inherent risks for early career faculty who make a significant investment of time in interdisciplinary research. There is an impression that early career faculty are expected to establish their credibility in their own field prior to engaging in interdisciplinary work and that interdisciplinary programs may be systematically ranked lower by review panels. To address this, one idea discussed by the group is that the NSF could specifically create interdisciplinary review panels with membership from a diverse group of interdisciplinary reviewers, and that program directors who oversee these panels

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could educate panelists on interdisciplinary programs as a part of the review process. Another idea discussed was to establish a program to pair up very well-established domain researchers with early career computational faculty to provide experience, aid, and expertise in development of very high quality proposals that would be strong in both domain and computational science. One other possible approach is to establish co-funding programs with other federal agencies, as well as industry. It might be helpful to ask industry to work with NSF to prepare proposals work on very difficult grand challenge problems.

2. How can NSF encourage an increased number of funded international research

collaborations?

Scribe: Kerk Kee

This group discussed and made the following two main recommendations to the NSF regarding this question. First, it may be useful to examine how other country’s equivalents of NSF do this – e.g., Israel has an equivalent of NSF. They fund collaborative projects by a PI from Israel and a PI from the US. Funding level is about $150K for 3 years. Second, it could be valuable to fund international workshops, or a small workshop, like the CyberBridges Workshop, to stimulate international collaborations for funding proposals. Encourage international participants/PIs to submit joint proposals. It was also explained during the discussion that the NSF CAREER in Europe program requires that there is at least 1 year left on CAREER project, the PI’s dept pays his/her salary when the PI is in Europe, the EU partner pays for the PI’s living expenses, and NSF pays for travel. The PI must be in Europe for at least 6 months out of the 12 month period.

3. What cyberinfrastructure capabilities are needed to support research and education in your

primary area of expertise as well as for other domains in which you collaborate?

Scribe: Linwei Wang

The group discussed three aspects of this question. First, regarding hardware infrastructure, there is a need to support the development of hardware infrastructure beyond centrally-managed format, which allows for the appropriate level of granularity and allows access to and manipulation of low-level parts of the system/architecture and root permission. Second, regarding software infrastructure, there is a need for support for software that will create a gateway to one’s research product/algorithms that is customized to different domain needs/data formats, and software that could virtualize the hardware and capsulate different versions that will work for different platforms. Finally, regarding education, there is a need for formal training modules (such as software carpentry) responsible conduct of research, something similar to CRC training, but targeted at computational research covering topics such as version control, data management, and quality control.

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4. How could NSF best create an environment that would encourage and facilitate the exploration and initiation of collaborative work between early career computational faculty with established domain research projects that would benefit from the involvement of these faculty?

Scribe: Iman Borazjani

The group discussed this question and made the following recommendations. First, it might be a good idea to encourage EAGER grants in which junior faculty team up with bigger established projects. A supplemental funding mechanism to support such collaboration could be another possibility. Second, it is important to recognize that the goal of computational and domain scientists may not be the same during collaborations. Hence sometimes it might be better to demarcate the computing and domain side of the things. Third, it will be helpful to train people in multiple disciplines so they can be versed with the languages of different fields. Fourth, it would be useful to add the involvement of early-career faculty as a component of broader impact; a component in the annual report could be added, as well. Finally, it is important to find a mechanism for pairing expertise with problem needs.

5. How can NSF encourage the development of novel approaches for education and curriculum

development for computationally oriented domains?

Scribes: Waheed Bajwa

The group discussed and identified the following challenges and needs in education and curriculum development for computationally oriented domains. First, there is need of free access to resources and travel grants for students to workshops and visit HPC facilities to motivate them to continue in computational science. Second, there is a need for basic level computer science skills for students in engineering and other fields to understand the complexity and basics of algorithms and their efficiency. There is also a gap between mathematics and the physics, i.e., implementing the math in computational science to understand physics. Third, there is a need to better motivate computer science students for engineering applications in order to see the value of the algorithms and the methods learned. Finally, it is suggested that NSF could build a repository for different domains and disciplines of applications and usage of

Figure 4. Keynote speaker Dr. Katy Borner participates in a breakout session

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computational science for students, and create an interdisciplinary program between computer science and engineering to bring cyberinfrastructure to users.

5.0 Attendee Feedback Survey

We conducted an anonymous survey of attendees using Qualtrics to collect attendee feedback about the workshop as well as to seek feedback on potential improvements for the workshop. The number of attendees who responded to the survey was 26 out of a possible 31 attendees (86%). Table 1 below describes a portion of the survey results. The entire survey along with responses are contained in Appendix A.

Table 1. Workshop survey responses reported as the mean and standard deviation (S.D.) of individual survey responses. Survey participants answered questions on a Likert Scale with the mapping: Strongly Disagree (SD = 1), Disagree (D = 2), Neither Agree nor Disagree (AD = 3), Agree (A = 4), and Strongly Agree (SA = 5).

Question Mean S.D.

The five focus areas of the workshop included my area of research and education.

Wor

ksho

p fo

cus a

reas

(a

ttend

ees c

ould

cho

ose

mor

e th

an o

ne)

Grand Challenges in Cyberinfrastructure and Interdisciplinary Research

4.3 0.7

Visualization 3.5 1.1

Future Directions in Cyberinfrastructure 4.4 0.7

High Performance Computing 4.3 0.8

Computational- and Data-enabled Science and Engineering 4.5 0.7

The disciplinary areas of workshop attendees (Computer Science, Physics, etc.) were sufficiently broad to facilitate interdisciplinary engagement.

4.3 0.7

The workshop format (keynote talks followed by discussion) was useful and engaging.

4.3 0.7

The talks were relative, informative, and interesting. 4.3 0.8

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The breakout discussion session was useful and engaging. 4.4 0.7

There were sufficient opportunities for networking and collaboration. 4.3 0.7

The hotel accommodations, meeting space, and meals were adequate. 4.9 0.3

The workshop was helpful in learning more about the NSF and available funding opportunities at NSF.

4.5 0.6

The workshop should include CAREER awardees beyond ACI. 3.8 1.1

The workshop should include attendees from outside the NSF CAREER program.

3.2 0.9

The length of the workshop (1.5 days) is sufficient. 4.2 0.93

The number of attendees is about right. 4.5 0.6

For the workshop this year, we broke out the question about the thematic areas of the workshop to seek detailed responses for each area. Attendees could select more than one area. In terms of areas of research and education, among the five areas, participants agreed most strongly that the broad area of Computational- and Data-enabled Science and Engineering related to their area of research and education. Future Directions in Cyberinfrastructure was another strong area, with slightly less agreement than Computational- and Data-enabled Science and Engineering. Tied for third place was Grand Challenges in Cyberinfrastructure and Interdisciplinary Research and High Performance Computing, and Visualization was by far (3.5) the least relevant area. Overall, workshop attendees agreed that the disciplinary areas of the workshop were sufficiently broad enough to facilitate interdisciplinary engagement. In terms of broad areas that attendees would like to see covered in a follow-on workshop, the areas included data science, big data, data challenges, data analytics more closely linked with CI and HPC, and secure and trustworthy cyberinfrastructure. Another question asked attendees to comment on broad new areas they would like to see covered in a follow-on workshop. Attendee’s comments indicated areas that included data, biology, cybersecurity, computer architecture, and real-time embedded computing. One comment was that the topics were already broad, and that “a harder part is to truly understand the details and changes in interdisciplinary research”, which perhaps indicates a need to address interdisciplinary research as a stand-alone topical area.

Attendees agreed that the format of keynote talks followed by discussion was useful and engaging, that the talks were informative and interesting, that the breakout discussion session

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was useful and engaging, and that there were sufficient opportunities for networking and collaboration. Attendees suggested 15 possible keynote speakers for follow-on workshops, and one commented that it “…would be helpful for some talks to be broader in scope.”.

Attendees strongly agreed (4.9) that the hotel accommodations, meeting space, and meals were adequate.

Participants agreed (4.5) that the workshop was helpful in learning more about the NSF and available funding opportunities at NSF. In terms of inviting a broader community of researchers, attendees were neutral to agreeable (3.8) that the workshop include CAREER awardees beyond ACI, and were almost neutral (3.2) to including attendees from outside the NSF CAREER program. Attendees also agreed that the length of the workshop was sufficient (4.2), and more strongly agreed (4.5) that the number of attendees was about right.

5.1 Comparing survey results for the 2012-2015 NSF CyberBridges workshops

Figure 5 shows a comparison of survey results for the 2012, 2013, 2014, and 2015 CyberBridges workshops with a Likert scale with the mapping Strongly Disagree (SD = 1), Disagree (D = 2), Neither Agree nor Disagree (AD = 3), Agree (A = 4), and Strongly Agree (SA = 5). Results from the 2015 workshop are shown in green, with prior workshops shown in progressively darker shades of grey for prior workshops with the lightest shade representing the workshop from the prior year (2014), dark grey for the 2013 workshop, and black for the first workshop in 2012. The length of each bar represents the average of the survey responses, and the error bar represents the computed standard deviation of the responses.

For the survey this year, we modified questions about the number of attendees and the length of the workshop from a 1:3 scale to a 1:5 Likert scale, and we modified the question regarding the poster session to ask about the breakout discussion session, since we replaced the poster session with a breakout discussion session this year.

In terms of length of the workshop, attendees continued to agree that the size of the workshop was about right and the length of the workshop was sufficient. Survey respondents also continued to be neutral about including attendees from outside the NSF CAREER program, and agreed more strongly (with a slightly declining trend in agreement over the years) that the workshop should include CAREER awardees beyond ACI, which may be an indication that attendees are beginning to recognize themselves as a distinct community. Future workshop surveys should investigate this further. Attendees also agreed more strongly than in prior years that the workshop was helpful in learning more about the NSF and available funding opportunities. In terms of hotel accommodations, meeting space, and meals, attendees reported the strongest agreement of any prior workshop. The replacement of the poster session with a breakout discussion session resulting in a substantially increased agreement that the breakout discussion session was useful and engaging. Survey respondents also indicated an increased

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agreement compared with prior years that the talks were informative and interesting, that the workshop format was useful and engaging, that the disciplinary areas of the workshop were sufficiently broad to facilitate interdisciplinary engagement, and that the five focus areas of the workshop included their area of research and education.

Figure 5. Comparison of survey responses from 2012, 2013, 2014, and 2015 CyberBridges workshops.

0 1 2 3 4 5 6

The five focus areas of the workshop (GrandChallenges, Data, Visualization, Computational…

The disciplinary areas of workshop attendees weresufficiently broad to facilitate interdisciplinary…

The workshop format (keynote talks followed bydiscussion) was useful and engaging.

The talks were relative, informative, and interesting.

The poster session was useful and engaging.

The breakout discussion session was useful andengaging.

There were sufficient opportunities for networking andcollaboration.

The hotel accommodations, meeting space, and mealswere adequate.

The workshop was helpful in learning more about theNSF and available funding opportunities.

The workshop should include CAREER awardeesbeyond ACI.

The workshop should include attendees from outsidethe NSF CAREER program.

Length of the workshop (Too short/too few (1); Aboutright (2); and Too long/too many (3))

The length of the workshop (1.5 days) is sufficient (1:5Likert scale).

Number of attendees (Too short/too few (1); Aboutright (2); and Too long/too many (3))

The number of attendees is about right (1:5 Likertscale)

2015 2014 2013 2012

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6.0 Observations from the Workshop

From the presentations and discussions during the workshop, we distilled several observations in each of the thematic areas of the workshop. In the areas of Computational and Data-enabled Science and Engineering, and High Performance Computing, there is an emerging awareness of the need for exascale computing capabilities to increase the speed and depth of analysis for more detailed and accurate analysis and simulations of the human body. The successful development and use of exascale systems will require a rethinking of applications and systems software to add an awareness of power use and limits to maximize utility and performance within the constraints of a tight power budget for the system. Improved fault analysis and tolerance techniques, higher level programming languages, and more emphasis on basic software engineering skills will also be important elements of an exascale strategy.

On the general topic of faculty career planning and interdisciplinary work, it may be helpful for researchers to follow a deliberate process of strategically thinking out and planning their research agenda, especially in the context of aligning their short- and long-term efforts with national priorities. Adopting a deliberate approach for initiating and maintaining multidisciplinary efforts with others outside their domain may also help the current generation of researchers to become more comfortable engaging with researchers in other domains, and to encourage these researchers to impart the requisite skills of developing and sustaining multidisciplinary efforts to their students. One important element of meeting the challenge of effectively using exascale capabilities is to improve the knowledge and skills of students who will be using these capabilities through more effective mentoring and teaching beginning with education at the high school level to encourage interest in STEM disciplines.

In the area of visualization, there is a need for information visualization to help researchers understand data from the micro to macro spatial and temporal scales across many domains, each of which often have their own unique set of specific needs. There is a specific need for information visualization to better analyze and communicate high-dimensional data both in traditional publications as well as in online dissemination venues. There are also needs for visualization that go beyond data into the realm of algorithms to visually represent the flow of numeric uncertainties through algorithms, as well as for visualizing the parallel computing aspects of algorithms.

In the area of future directions for cyberinfrastructure, the emergence of service-oriented computing platforms, such as cloud computing services and storage services, are providing a reliable platform that can be used to support research by automating or outsourcing much of the repetitive functions such as data sharing. The exploitation of these capabilities may help to free up valuable researcher time spent today on repetitive computing activities. A research agenda for future directions for cyberinfrastructure should include the identification of activities that could be automated, identifying the obstacles to be overcome, and investigating sustainable and scalable models for funding cyberinfrastructure over a long time period. Open challenges in this

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area discussed by attendees included creating data workflow pipelines that can exploit heterogeneous computing resources, bridging disciplinary divides, training faculty and students in code development skills, data sharing, and parallel computing.

In the area of Grand Challenges in Cyberinfrastructure and Interdisciplinary Research, the grand challenges identified by attendees were focused on several areas. The first was the need for well-functioning data cyberinfrastructure combined with computing cyberinfrastructure to help facilitate the quick collection and analysis of data from multiple sources. Also, there is a need for a broader research approach in computer science in the area of algorithms and systems to fully integrate and exploit the capabilities of heterogeneous computing resources. There are also challenges in the areas of data and software provenance and traceability, and also social challenges involved in creating and sustaining interdisciplinary collaborations in an adequately symbiotic manner that affords excellence in the depth and impact of research efforts for early career faculty for them to satisfy tenure and promotion criteria for their primary discipline.

7.0 Lessons Learned from the Workshop

We have conducted the CyberBridges workshop for the past four years and have identified some “lessons learned” to benefit future workshops. This year the workshop was held in the CEB Waterview Conference Center in Arlington, VA. This was a change from previous years when the workshops were held in various hotels near the National Science Foundation. Attendees, keynote speakers, and NSF program directors found this location to be convenient (as it was near a metro stop). In addition, the conference center layout was ideal for the size of our group. Another change in this year’s workshop was the replacement of the poster session with an attendee breakout session in which attendees presented their work in an interactive format. In particular, each attendee identified a major challenge related to his/her research and education interests; the breakout group then discussed potential strategies for addressing the challenge. Attendees noted greater satisfaction with the attendee breakout session. Another change with this year’s workshop was the addition of a third workshop co-chair, Linwei Wang, from Rochester Institute of Technology. Linwei brought new energy and ideas to the workshop; the workshop ran smoothly with a three-person leadership team.

As we reflect on the past four workshops, we find the workshops have been successful in regards to encouraging new collaborations among attendees with NSF CAREER Awards from the Division of Advanced Cyberinfrastructure and related areas. In addition, the workshops have encouraged networking among NSF CAREER Awardees and nationally- and internationally-recognized leaders in cyberinfrastructure research and education. Finally, the workshop has provided guidance on how to further build the community of cyberinfrastructure researchers. With the latter focus on mind, we strive to create additional community-building activities for cyberinfrastructure researchers.

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Over the past four workshops, we have perceived at least two common themes emerging from the discussions among attendees. First, there are inherent structural, cultural, and disciplinary challenges encountered by early career faculty interested in pursuing interdisciplinary and multidisciplinary academic work. These challenges involve areas such as seeking and securing research funding, earning tenure, and seeking disciplinary recognition. The second theme involves the interest of early career faculty in seeking acceptance and credit for work that fully leverages computing technology available today for artifacts such as software they develop that is widely used by the community, and for critical datasets that could be referenced and used by the broader academic community.

For future workshops involving ACI CAREER awardees, a potential direction for consideration would be to begin discussions about potential solutions and new approaches for fostering and encouraging multidisciplinary work, and to accelerate the awareness of and interest in digital scholarship as an integral part of recognized academic work.

Acknowledgements

Our efforts in planning and conducting the CyberBridges workshop and the development of this report were supported by Kristopher Kirby (Purdue), Stacy Walters (University of Kansas), Jordan Blair (University of Kansas), and Lorrie Jo Turner (Rochester Institute of Technology). This workshop was supported by National Science Foundation Award ACI-1543630.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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REFERENCES

[1] Hacker, T., & Shontz, S. (2012). Developing the Next Generation of Cyberinfrastructure Faculty for Computational and Data-enabled Science and Engineering.

[2] Shontz, S., & Hacker, T. (2013). Report of the 2013 NSF CyberBridges Workshop on Developing the Next Generation of Cyberinfrastructure Faculty for Computational and Data-enabled Science and Engineering.

[3] Hacker, T., & Shontz, S. (2014). Report of the 2014 NSF CyberBridges Workshop on Developing the Next Generation of Cyberinfrastructure Faculty for Computational and Data-enabled Science and Engineering.

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Appendix A. Detailed Survey Results

The complete detailed text questions and attendee responses to the survey sent out to attendees are described below.

In the first section, survey participants answered questions on a Likert Scale with the following numeric assignment. Strongly Disagree (SD = 1), Disagree (D = 2), Neither Agree nor Disagree (AD = 3), Agree (A = 4), and Strongly Agree (SA = 5).

1. The five focus areas of the workshop included my area of research and education. Results:

a. Grand Challenges in Cyberinfrastructure and Interdisciplinary Research Mean Value: 4.3. Responses: (0) SD, (0) D, (3) AD, (9) A, (10) SA

b. Visualization Mean Value: 3.5. Responses: (0) SD, (5) D, (4) AD, (8) A, (4) SA

c. Future Directions in Cyberinfrastructure Mean Value: 4.4. Responses: (0) SD, (0) D, (3) AD, (7) A, (12) SA

d. High Performance Computing Mean Value: 4.3. Responses: (0) SD, (0) D, (5) AD, (5) A, (12) SA

e. Computational- and Data-enabled Science and Engineering Mean Value: 4.5. Responses: (0) SD, (1) D, (0) AD, (9) A, (12) SA

2. Please provide a comment describing a new broad area you would like to see covered in a

follow-on workshop. Results (Text responses): Data Science

Secure and trustworthy cyberinfrastructure - challenges, invectives, application domains.

Topics discussing data challenges big data I would like to see data analytics more closely linked with CI and HPC. N/A na

3. The disciplinary areas of workshop attendees (Computer Science, Physics, etc.) were sufficiently broad to facilitate interdisciplinary engagement.

Results: Mean Value: 4.3. Responses: (0) SD, (0) D, (3) AD, (10) A, (9) SA

4. The format (keynote talk followed by discussion) was useful and engaging. Results: Mean Value: 4.3. Responses: (0) SD, (0) D, (3) AD, (9) A, (10) SA

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5. The talks were relative, informative, and interesting. Results: Mean Value: 4.3. Responses: (0) SD, (1) D, (1) AD, (11) A, (9) SA

6. The breakout discussion session was useful and engaging. Results: Mean Value: 4.4. Responses: (0) SD, (0) D, (3) AD, (8) A, (11) SA

7. There were sufficient opportunities for networking and collaboration. Results: Mean Value: 4.3. Responses: (0) SD, (0) D, (3) AD, (10) A, (9) SA

8. The hotel accommodations, meeting space, and meals were adequate. Results: Mean Value: 4.9. Responses (0) SD, (0) D, (0) AD, (2) A, (19) SA

9. The workshop was helpful in learning more about the NSF and available funding opportunities at NSF. Results: Mean Value: 4.5. Responses (0) SD, (0) D, (1) AD, (8) A, (12) SA

10. The workshop should include NSF CAREER awardees beyond ACI. Results: Mean Value: 3.8. Responses (1) SD, (1) D, (6) AD, (7) A, (6) SA

11. The workshop should include attendees from outside the NSF CAREER program. Results: Mean Value: 3.2. Responses (1) SD, (3) D, (8) AD, (8) A, (1) SA

12. The length of the workshop (1.5 days) is sufficient. Results: Mean Value: 4.2. Responses (0) SD, (2) D, (1) AD, (9) A, (9) SA

13. The number of attendees is about right. Results: Mean Value: 4.5. Responses (0) SD, (0) D, (1) AD, (8) A, (12) SA

The next questions asked about the frequency and travel funding for the workshop, and about the respondent’s source of CAREER funding.

14. Would you be interested in attending the workshop in the future without full travel reimbursement? Results: Yes 32%, No 9%, Maybe 59%

15. How frequently (i.e. annually or biannually) should the workshop be held? Annually 82%, Biannually 18%, Other 0%

16. Is a component of your CAREER award funded from ACI? Results: Yes 55%, No 45%

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The final questions provided an open form to allow participants to provide written feedback:

Please provide a comment describing a new broad area you would like to see covered in a follow-on workshop.

1. Are there any new broad areas or topics that you would like to see covered in a follow on workshop?

a. Real-time embedded computing b. new/novel computer architectures, chemistry/materials c. Promoting open source, open data, and good practices for reproducible science d. big data e. Data analytics. f. The topics are broad already. A harder part is to truly understand the details and

changes in interdisciplinary research. g. No h. applications in aerospace engineering and computational medicine i. computational biology, system biology, control j. cyber security

2. What changes or improvements could we make to the workshop in the future?

Responses:

a. It's fantastic! b. reduce number of discussions; assign people to groups; have a poster session c. A little more break time. d. more time and opportunities for interacting with PMs e. Interact a little with the keynote speakers so that they customize the talk to the

purpose of the workshop, rather than rehash old talks f. excellent run workshop! g. I really liked the one-slide discussion and thought that was the best part of the

program. I wonder, however, if the star groups were formed after studying the collaboration interests of the faculty. That might be one thing to consider in planning.

h. It was an informative workshop. The organizers did an excellent job. Can we get access to the slides and summaries presented during the workshop?

i. More time for networking j. The workshop would be more helpful to me (a repeat attendee) if there were

opportunities to network with more senior faculty. It would also help if there were something that the community did as a group inbetween workshops

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k. everything was nicely planned and good. Thanks l. The timing may be better in Oct after the Fall semester is well underway.

3. Please provide suggestions for keynote speakers for follow-on workshops.

a. OK to go deep in your area but would be helpful for some talks to be broader in

scope. b. Bill Daly, Stanford c. Kristen Persson, LBNL - Materialsproject.org d. Jack Dongarra, Marc Snir, Arthur Barney Maccabe, Manish Parashar, Andrew

Chien, Bob Grossman, Peter Dinda, Alok Choudhary, and lots more. All these folks are highly accomplished, and would engage the workshop attendees with much enthusiasm!

e. It would be helpful to have access to their slides after the workshop. f. Discuss more about their future plan g. James Chelikowsky, UT Austin (computational materials science); Qiang Du,

Columbia University (PDE modeling and applications) h. the keynotes were very good i. Ed Seidel (UIUC), Dan Atkins (UMich), Gabrielle Allen (UIUC)

4. Please provide suggestions for community building activities during the workshop.

a. Maybe have some sample end products that during the workshop we can work towards in groups.

b. poster session would be more efficient c. Lightning talks (5 min max, quick back to back) d. current activities were excellent! I liked that we did not have posters, but the 2

minute slide presentation! Perhaps we could do the 2 min slide presentation to the entire group, and then break out into groups to brainstorm based on interests.

e. People describing open problems that they WISH they could have someone work with them and then discussing in groups possible collaborators for those problems.

f. One workshop may not provide sufficient opportunity to build collaborations. g. Replace a breakout session with informal networking time. We spend a lot of time

in breakout sessions but not enough time h. I think the CAREER breakout sessions were very fruitful for intra-focus

collaboration... the inter-focus.. across discipline and much more difficult to facilitate.

i. 10-15 minutes short talk/poster to allow everyone to present their research focus.

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5. Please provide suggestions for community building activities outside of the workshop. a. inviting the program directors to dinner and sharing a table with them. b. Twitter c. LinkedIn group perhaps. d. Providing opportunities to tap into data-resources of big projects. e. NA f. BOF at Supercomputing; Open Spaces meeting like done at the 2015 International

Meshing Roundtable. Set-up a meeting, possibly within an existing conference, and have people discuss what their needs and wants are for the community and go from there.

g. Collecting data of conferences people attend and trying to have BOFs or meeting times at said conferences.

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Appendix B. Speaker and Attendee Biographies and Photos Biographies were current as of August 2015, at the time of the workshop Keynote Speakers

Clint Dawson received BA and MS degrees from Texas Tech University and his PhD from Rice University. He was an NSF Postdoctoral Fellow and Dickson Instructor at the University of Chicago. He began his academic career at Rice University, and soon after moved to the University of Texas at Austin. He is a professor of aerospace engineering and engineering mechanics, and holds the John J McKetta Centennial Energy Chair in Engineering. He is also an affiliate of the Institute for Computational Engineering and Sciences at UT Austin.

Katy Börner is the Victor H. Yngve Professor of Information Science in the Department of Information and Library Science, School of Informatics and Computing, Adjunct Professor at the Department of Statistics in the College of Arts and Sciences, Core Faculty of Cognitive Science, Research Affiliate of the Center for Complex Networks and Systems Research and Biocomplexity Institute, Member of the Advanced Visualization Laboratory, Leader of the Information Visualization Lab, and Founding Director of the Cyberinfrastructure for Network Science Center at Indiana University in Bloomington, IN and

Visiting Professor at the Royal Netherlands Academy of Arts and Sciences (KNAW) in The Netherlands. She is a curator of the international Places & Spaces: Mapping Science exhibit. She holds a MS in Electrical Engineering from the University of Technology in Leipzig, 1991 and a Ph.D. in Computer Science from the University of Kaiserslautern, 1997. She became an American Association for the Advancement of Science (AAAS) Fellow in 2012.

Nikos Chrisochoides is the Richard T. Cheng Distinguished Professor of Computer Science and John Simon Guggenheim Fellow in Medicine & Health at Old Dominion University. In 2012, he was elected Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK. He founded Medical Imaging Software Technologies LLC. Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. He worked at Northeast Parallel Architectures Center in Syracuse and Advanced Computing Research Institute at Cornell Theory Center. In 1997 he joined the Computer Science and Engineering Department at Notre Dame and in 2000 the College

of William and Mary. He has held visiting positions at MIT, Harvard Medical School, Brown University, and at NASA/Langley.

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Ian Foster is the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago and an Argonne Distinguished Fellow at Argonne National Laboratory. His research is concerned with the acceleration of discovery in a networked world. Dr. Foster is a fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and the British Computer Society. Awards include the British Computer Society's Lovelace Medal, honorary doctorates from the University of Canterbury, New Zealand, and CINVESTAV, Mexico, and the IEEE Tsutomu Kanai award.

Valerie Taylor is the Senior Associate Dean of Academic Affairs in the Dwight Look College of Engineering and a Regents Professor and the Royce E. Wisenbaker Professor in the Department of Computer Science and Engineering at Texas A&M University. In 2003, she joined Texas A&M University as the Department Head of CSE, where she remained in that position until 2011. Prior to joining Texas A&M, Dr. Taylor was a member of the faculty in the EECS

Department at Northwestern University for eleven years. She has authored or co-authored over 100 papers in the area of high performance computing. She is also the Executive Director of the Center for Minorities and People with Disabilities in IT (CMD-IT). Dr. Taylor is an IEEE Fellow and has received numerous awards for distinguished research and leadership, including the 2001 IEEE Harriet B. Rigas Award for a woman with significant contributions in engineering education, the 2002 Outstanding Young Engineering Alumni from the University of California at Berkeley, the 2002 CRA Nico Habermann Award for increasing the diversity in computing, and the 2005 Tapia Achievement Award for Scientific Scholarship, Civic Science, and Diversifying Computing. Dr. Taylor is a member of ACM. Valerie E. Taylor earned her B.S. in ECE and M.S. in Computer Engineering from Purdue University in 1985 and 1986, respectively, and a Ph.D. in EECS from the University of California, Berkeley, in 1991. NSF Speakers and Program Director Panelists

Jim Kurose is the Assistant Director of the National Science Foundation (NSF) for Computer and Information Science and Engineering (CISE). He leads the CISE Directorate, with an annual budget of more than $900 million, in its mission to uphold the nation's leadership in scientific discovery and engineering innovation through its support of fundamental research in computer and information science and engineering and transformative advances in cyberinfrastructure.

Dr. Kurose is on leave from the University of Massachusetts,

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Amherst, where he is a Distinguished Professor in the School of Computer Science. He has also served in a number of administrative roles at UMass and has been a Visiting Scientist at IBM Research, INRIA, Institut EURECOM, the University of Paris, the Laboratory for Information, Network and Communication Sciences, and Technicolor Research Labs. His research interests include network protocols and architecture, network measurement, sensor networks, multimedia communication, and modeling and performance evaluation. Dr. Kurose received his Ph.D. in computer science from Columbia University and a Bachelor of Arts degree in physics from Wesleyan University. He is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronic Engineers (IEEE).

Irene M. Qualters is currently Division Director of Advanced Cyberinfrastructure at NSF. She has previously been a Program Director in NSF’s Advanced Cyberinfrastructure Division (ACI) overseeing several projects within the division’s portfolio, including the Blue Waters project at NCSA/UIUC and the Stampede project at TACC/UT at Austin. Irene has a Masters degree in Computer Science. Prior to beginning her Program Director responsibilities in December 2009, she had a distinguished 30-year career in industry, with executive leadership positions for research and development organizations within the technology sector. During her twenty years at Cray Research, in increasingly larger leadership roles, she participated in the development

of the first commercially successful vectorizing compiler, the first multiprocessor version of Unix and Cray’s landmark massively parallel computer, the T3E. Subsequently, for six years, as Vice President, she led the Research Information Systems for Merck Research Labs (MRL). She is expert in parallel computer system architectures and in a wide variety of software from scientific applications to system software.

Dr. Amy Apon serves NSF as a rotator from Clemson University. She is a Program Director in the Computer Systems Research (CSR), eXploiting Parallelism and Scalability (XPS), and BigData programs. At Clemson University, Apon has held the position of Professor and Chair of the Computer Science Division in the School of Computing since 2011. As Chair, Apon has led the creation of a new program to grow the graduate enrollment, “CI SEEDS – Seeding the Next Generation Cyberinfrastructure Ecosystem” and has seen the number of

publications and research expenditures more than double in the Division. Apon is co-Director of the Complex Systems, Analytics, and Visualization Institute (CSAVI), which includes the Big Data Systems and Analytics Lab. Her research focus includes performance modeling and analysis of parallel and distributed systems, data-intensive computing in the application area of intelligent transportation systems, technologies for cluster computing, and the impact of high performance computing to research competiveness.

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Apon was elected Vice Chair and then Chair from 2009-2012 of the Coalition for Academic Scientific Computation, an organization of more than 70 leading U.S. academic institutions. Apon has led multiple successful collaborative NSF-funded projects that support high performance computing, including several awards from the NSF MRI program. Prior to joining Clemson, Apon was Professor at the University of Arkansas where she led the effort to develop the high performance computing capability for the State of Arkansas. The Arkansas High Performance Computing (HPC) Center was funded by the Arkansas Science and Technology Authority in May 2008, and established under her direction. The acquisition of Red Diamond was the first computer in Arkansas ranked on the Top 500 list, in June 2005. The Arkansas Cyberinfrastructure Task Force Act was passed through her efforts in 2009. Dr. Apon has published over 100 peer-reviewed publications in areas of research, education, and impact of parallel and distributed computing.

Dr. Karen Cone is a Program Director in the Genetic Mechanisms Cluster in the Division of Molecular and Cellular Biosciences in the BIO Directorate at the National Science Foundation. She currently leads the management team for the iPlant Collaborative, which is developing cyberinfrastructure to enable life science research, and she is a member of BIO’s Science Engagement Working Group for the National Ecological Observatory Network, a groundbreaking continent-wide observatory designed to collect and stream ecological data from dozens of sites across the U.S. for the next 30 years. Before joining NSF in 2009, Cone was a faculty member for

20+ years in Biological Sciences at the University of Missouri in Columbia, where her research on plant genetics, epigenetic regulation, and genomics was funded primarily by NSF--including a Presidential Young Investigator Award. Cone is a fellow of the American Association for the Advancement of Science and holds B.S. and M.S. degrees in Microbiology from the University of Georgia and a Ph.D. in Biochemistry and Genetics from Duke University.

Sushil K. Prasad (BTech’85 IIT Kharagpur, MS’86 Washington State, Pullman; PhD’90 Central Florida, Orlando - all in Computer Science/Engineering) is a Program Director for the National Science Foundation CISE/ACI Division of Advanced Cyberinfrastructure. He is a Professor of Computer Science at Georgia State University (GSU) and Director of Distributed and Mobile Systems (DiMoS) Lab. He has carried out theoretical as well as experimental research in parallel and distributed computing, resulting in 120+ refereed publications, several patent applications, and about $3M in external

research funds as principal investigator and over $6M overall (NSF/NIH/GRA/Industry). Sushil has been honored as an ACM Distinguished Scientist in Fall 2013 for his research on parallel data structures and applications.

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Nigel Sharp is the Program Director for the Large Synoptic Survey Telescope project, in the Division of Astronomical Sciences (AST) in the Directorate for Mathematical and Physical Sciences (MPS) of the National Science Foundation (NSF). He has some additional more minor programmatic responsibilities. After three degrees in physics, mathematics, and astrophysics at the University of Cambridge (the real one), Nigel moved serially to Texas, Australia, and Arizona, and had a varied career in astronomy theory and observation, including instrumentation and telescope management. His service work has

included supercomputer access and numerical methods consulting, and systems management, networking and security at an NSF FFRDC. After all that, it made sense to join NSF and continue to work on service to the community from the funding end of things. He has been involved in NSF’s cyberinfrastructure initiatives, was part of the working group for interdisciplinary research, and helped to define and to implement NSF’s data management plan requirement.

Jack Snoeyink is a Program Director in the Algorithmic Foundations cluster of CISE Computing and Communication Foundations (CCF/AF), rotating from the Department of Computer Science of the University of North Carolina at Chapel Hill since January 2015. Through his PhD from Stanford in 1990, postdoc in Utrecht, and stints at UBC and UNC, his research has been in the area of discrete and computational geometry, and its application in molecular modeling, robotics, computer-aided design (CAD), and geographic information systems (GIS). He mostly tries to find clever solutions that avoid cyberinfrastructure, such as the work of Martin Isenburg on LAStools for

LiDAR processing. He finds a wonderful cross-fertilization between different areas by looking at their core problems geometrically.

Prof. Friedrich Srienc is a faculty member of the Department of Chemical Engineering and Materials Science and of the BioTechnology Institute at the University of Minnesota in Minneapolis/St.Paul. He received a doctoral degree in biotechnology from the Technical University in Graz, Austria. After postdoctoral studies at the California Institute of Technology in Pasadena, CA, he joined the faculty in Minnesota in 1985. His interests in Biochemical Engineering include cell population dynamics, flow cytometry, biopolymers, and metabolic engineering. His group has pioneered the application of single-cell measuring techniques for

monitoring and controlling microbial and mammalian cell cultures, and more recently, the application of systems biological tools for the design of optimized cells. Currently, he serves as Program Director of the Biotechnology and Biochemical Engineering Program at the National Science Foundation.

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Faculty Attendees

Behçet Açıkmeşe is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. He received his Ph.D. in Aerospace Engineering in 2002 from Purdue University. He was a Visiting Assistant Professor of Aerospace Engineering at Purdue University before joining NASA Jet Propulsion Laboratory (JPL) in 2003. He was a senior technologist at JPL and a lecturer in GALCIT at Caltech. At JPL, Dr. Açıkmeşe developed Guidance, Navigation, and Control (GN&C) algorithms for planetary landing, formation flying spacecraft, and

asteroid and comet sample return missions. He was the developer of the “flyaway" GN&C algorithms in Mars Science Laboratory, which successfully landed on Mars in August 2012. His convex optimization based planetary landing algorithm is the first real-time optimization algorithm used for onboard trajectory optimization on a NASA test rocket. He is an Associate Fellow of AIAA and a Senior Member of IEEE. Website: http://behcet.ae.utexas.edu NSF CAREER project summary: The goal of my career project is to build both a rigorous analytical and a reliable real-time optimization-based computational framework for high-performance control of autonomous systems. Areas of Collaboration:

• Autonomy • Power networks • Robotics • Science of human decision making • Real-time scientific computing • Modeling and control of biological systems

Alexander Alexeev is an Associate Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. He obtained his Ph.D. degree in Mechanical Engineering in 2003 from the Technion - Israel Institute of Technology. He joined Georgia Tech in 2008 after finishing his postdoctoral studies at TU Darmstadt in Germany and at the University of Pittsburgh. He uses modeling and simulations to solve problems at the intersection of engineering, medicine, and biology. His research interests include mesoscale modeling of complex fluids, soft and active materials, interfacial phenomena, and microfluidics.

Website: http://cfms.gatech.edu/

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NSF CAREER project: The project goal is to develop a mesoscale computational model of stimuli-sensitive polymer networks that can explicitly capture micromechanics, transport properties and responses to environmental stimuli. Areas of collaboration:

• High performance computing • Computational fluid dynamics • Mesoscale and particle based computational methods

Hazeline U. Asuncion is an Assistant Professor at the Computing, and Software Systems, School of Science, Technology, Engineering, and Math, at the University of Washington, Bothell. She received her PhD in Information and Computer Science at the University of California, Irvine. As a software engineer, she is interested in examining how software techniques can assist domain scientists in capturing data provenance. Her research areas are software traceability and data provenance for e-Science. Website: http://faculty.washington.edu/hazeline/

NSF CAREER project: This CAREER Award project focuses on the crucial interplay between data and software in eScience by using a conceptual framework, iProvenance, that integrates data provenance with software traceability and is grounded in a holistic examination of provenance challenges. Areas of collaboration:

• Data Provenance

Dr. Omar I. Abdul-Aziz is an Assistant Professor of Civil & Environmental Engineering at the West Virginia University (WVU), Morgantown, WV. He obtained a Ph.D. from the University of Minnesota, Twin Cities in 2008, M.Sc. from the University of Waterloo, Canada in 2004, and B.Sc. from the Bangladesh University of Engineering and Technology, Dhaka in 2002, all in Civil Engineering. He was a tenure-track Assistant Professor of Civil & Environmental Engineering at the Florida International University (FIU), Miami, FL during 2011-15. Prior to joining FIU in Fall 2011, he worked as a scientist with the U.S. Geological Survey (USGS) and University of Washington, Seattle. Dr. Abdul-Aziz conducts state-of-the-art, highly interdisciplinary research in

the interface of ecological and water resources engineering, incorporating topics related to the coupled human-natural systems and sustainability sciences/engineering. He developed the Ecological and Water Resources Engineering Lab (http://www.ewrel.fiu.edu/) at FIU. His current research focuses on developing robust, user-friendly engineering models to predict and assess (1) wetland and forest greenhouse gas (GHG) fluxes and carbon sequestration (funded by NSF and NOAA); (2) stream/river water quality and ecosystem health (funded by NSF and US

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DOE); and (3) urban stormwater flooding and drainage sustainability under extreme climate events (funded by the State of Florida).

Website: https://ewrel.fiu.edu/

NSF CAREER project: The goal of my NSF CAREER project is to investigate and robustly predict stream water quality and ecosystem health in complex urban-natural basins.

Areas of collaboration:

• Modeling and predictions/forecasting of urban flooding and drainage sustainability, as well as that of stream/river water quality and ecosystem health.

Waheed U. Bajwa received MS and PhD degrees in electrical engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. He was a Postdoctoral Research Associate in the Program in Applied and Computational Mathematics at Princeton University from 2009 to 2010, and a Research Scientist in the Department of Electrical and Computer Engineering at Duke University from 2010 to 2011. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University. His research interests include harmonic analysis, high-dimensional statistics, machine learning, statistical signal processing, and wireless communications. Dr. Bajwa received the Army Research Office Young Investigator Award in 2014 and the National

Science Foundation CAREER Award in 2015. He co-guest edited a special issue of Elsevier Physical Communication Journal on “Compressive Sensing in Communications” (2012), and co-chaired CPSWeek 2013 Workshop on Signal Processing Advances in Sensor Networks and IEEE GlobalSIP 2013 Symposium on New Sensing and Statistical Inference Methods. He currently serves as the Publicity and Publications Chair of IEEE CAMSAP 2015, and is an Associate Editor of the IEEE Signal Processing Letters and a Senior Member of the IEEE. Website: http://inspire.rutgers.edu/ NSF CAREER project: Establish theoretical and algorithmic foundations for modeling of 21st century signals and data, as well as design and analyze methods for processing massively-large, distributed data sets that might be incomplete, mislabeled, and/or erroneous.

Areas of collaboration:

• Inference from large-scale sensor data • Machine learning for internet of things (IoT) • Remote sensing; biomedical imaging

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Lorena A. Barba is an Associate Professor of Mechanical and Aerospace Engineering at the George Washington University, in Washington, DC. Previously, she was an assistant professor in the Boston University College of Engineering and a lecturer in applied mathematics at University of Bristol, UK. She has a PhD in Aeronautics from the California Institute of Technology and a mechanical engineering degree from Universidad Técnica Federico Santa María in Valparaíso, Chile. Prof. Barba received the NSF Faculty Early CAREER award (2012), was named CUDA Fellow by NVIDIA Corp. (2012), is an awardee of the UK Engineering and Physical Sciences Research Council

(EPSRC) First Grant program (2007), is an Amelia Earhart Fellow of the Zonta Foundation (1999) and a leader in computational science and engineering internationally. Her research includes computational fluid dynamics, high-performance computing, computational biophysics and animal flight. She is an advocate of open-source software for science and open educational resources, and has shared open courseware on iTunesU, YouTube and GitHub. She is interested in education technology, social learning and the recent spread of massively open online courses, as well as innovations in STEM education, including flipped classrooms and other forms of blended learning. Her first MOOC, “Practical Numerical Methods with Python,” was an independent, self-promoted open course that attracted more than 5,000 participants. Website: http://lorenabarba.com/ NSF CAREER project: The goal of my NSF CAREER project is the development of highly scalable algorithms and scientific software that can achieve maximum performance on many-core hardware in preparation for the exascale era.

Areas of collaboration • High-performance computing • GPU computing • Fast-multipole method • Boundary element method • Immersed boundary method • Sparse linear solvers in GPU

Paul Bogdan received his Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh. He is an assistant professor in the Ming Hsieh Department of Electrical Engineering at University of Southern California. His work has been recognized with a number of distinctions, including the 2012 A.G. Jordan Award from the Electrical and Computer Engineering Department, Carnegie Mellon University for outstanding Ph.D. thesis and service, the 2012 Best Paper Award from the Networks-on-Chip Symposium (NOCS), the 2012 D.O. Pederson Best Paper Award from IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the 2012

Best Paper Award from the International Conference on Hardware/Software Codesign and

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System Synthesis (CODES+ISSS), the 2013 Best Paper Award from the 18th Asia and South Pacific Design Automation Conference, and the 2009 Roberto Rocca Ph.D. Fellowship. His research interests include performance analysis and design methodologies for multicore systems, the theoretical foundations of cyber-physical systems, the modeling and analysis of biological systems and regenerative medicine.

Website: http://ceng.usc.edu/cps/

NSF CAREER project: The efficient design of future medical devices and artificial organs calls for a more careful consideration of the complexity of biological processes, which are context-dependent and entail individual characteristics that need to be monitored over broader spans of time. The proposed research seeks to define a science of cyber-physical systems design and optimization while capturing the complex multiscale and fractal characteristics of physical processes.

Areas of collaboration:

• Cyber-physical systems • Real-time control and decision making • Distributed control • Multiscale mathematical modeling • Data-driven modeling • Time series data mining and modeling • Large scale optimization • Uncertainty quantification • Molecular communication • Synthetic and system biology • System-on-chip architectures for data-intensive computing applications

Iman Borazjani is an Assistant Professor in the Mechanical and Aerospace Engineering department at University at Buffalo, State University of New York (SUNY) since 2010. He obtained his Ph.D. degree in Mechanical Engineering in 2008 from the University of Minnesota and M.Sc. in 2005 from Georgia Tech. He is the recipient of the NSF Career Award, AHA Scientific Development Award, and ACS New Investigator Award. His research interests are in developing advanced computational tools for biofluids and fluid-structure interaction problems, which we employ to advance knowledge and gain insights into the physics of important biological/engineering flows.

Website: http://www.eng.buffalo.edu/~iman/

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NSF CAREER project: We develop methods for fluid-structure interaction simulations of biological flows using a sharp-interface immersed boundary method with a Newton-Krylov solver for fluid flow coupled with a finite element method for structural deformations.

Areas of Collaboration: • Uncertainty quantification • Multi-scale simulation • Modeling large tissue deformation • Fluid-structure interaction

Kevin Butler is an associate professor of Computer and Information Science and Engineering at the University of Florida, where he leads research in computer systems security within the Florida Institute for Cyber Security. His research focuses on the security of systems and data, with a concentration on storage and embedded systems, mobile security and privacy, and cloud security. He also researchers Internet security and applied cryptography. Kevin received his Ph.D. in Computer Science and Engineering from the Pennsylvania State University in 2010, an M.S. in electrical engineering from Columbia University in 2004, and a B.Sc. in electrical engineering from Queen’s University in 1999. He received the National Science Foundation CAREER award in 2013 and the Symantec

Research Labs Graduate Fellowship in 2009.

Website: www.kevinbutler.org

NSF CAREER project: The goal of this work is to develop techniques and architectures for securely storing and monitoring embedded system state in critical infrastructure, including mechanisms to provide secure storage for data and its associated provenance, cryptographically-verifiable transaction logs, and secure interfaces for private querying and communication.

Areas of collaboration:

• Systems security • Storage security • Web and SSL • Cyber-physical and embedded systems

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Kaushik Chowdhury (krc@ece.neu.edu) is Associate Professor in the Electrical and Computer Engineering Department at Northeastern University, Boston, MA, USA. He was earlier an Assistant Professor in the same department from 2009-15. He received his M.S. in Computer Science from the University of Cincinnati, OH, in 2006, and Ph.D. from the Georgia Institute of Technology, Atlanta, GA in 2009. His M.S. thesis was given the outstanding thesis award jointly by the ECE and CS departments at the University of Cincinnati. He received the Best Paper Award at the IEEE ICC Conference in 2009, 2012, 2013, as well as the Best Paper award in the ICNC Conference in 2013. He was given the NSF CAREER award in 2015. He serves as

area editor for the Elsevier Ad Hoc and Computer Communications journals. He is Sr. Member of the IEEE and is the present Chair for the IEEE Technical Committee on Simulation. Website: http://genesys-lab.org/ NSF CAREER project: My CAREER project aims to realize long-lived sensors through contact-less wireless charging via RF radiation, which involves systems design and protocol development to balance both the energy and data transferring needs within the network. Areas of collaboration:

• Big data • Wireless networking • Energy-harvesting sensor networks • Crowdsourcing with mobile users

Adrian Feiguin joined Northeastern University as Assistant Professor in 2012, after spending 3 years as Assistant Professor at the University of Wyoming. His field of expertise is computational condensed matter, focusing on theoretical and computational aspects of low-dimensional strongly interacting quantum systems. This physics is realized under extreme conditions, such as very low temperatures, high pressure, or high magnetic fields, and low spatial dimensions, and it is mostly governed by the collective behavior of the electrons inside a solid. Website: http://www.northeastern.edu/afeiguin/

NSF CAREER project: A. Feiguin conducts research in computational "hard" condensed matter physics, studying problems that involve quantum many-body physics, correlation effects, and out-of-equilibrium dynamics. He uses the time-dependent density matrix renormalization group method to advance timely open problems, that address fundamental questions, including: What are the universal scaling laws governing far from equilibrium transport? What are the fundamental processes in the coherent dynamics and decoherence of many-body systems? How does integrability affect thermalization and equilibration of systems out of equilibrium? In

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addition, he studies exotic phases of matter of quantum origin, quantum magnetism, and superconductivity. Areas of collaboration:

• Quantum information • Quantum chemistry

Baskar Ganapathysubramanian is an Associate Professor of Mechanical Engineering and Electrical and Computer Engineering at Iowa State University. His research interests are in multi-scale multi-physics modeling, design of materials and processes using computational techniques, and stochastic analysis. The recent focus of his group is on advanced energy technologies including solar cells, and green buildings. Ganapathysubramanian completed his PhD and MS from Cornell University and holds a BS degree from the Indian Institute of Technology-Madras.

Website: http://www.me.iastate.edu/bglab/ NSF CAREER project: A predictive modeling framework to explore fabrication of organic solar cells. Areas of collaboration:

• Parallel adaptive mesh generation • Data mining • Inverse problems

Rong Ge is an Associate Professor in the School of Computing at Clemson University. Before joining Clemson University she was an Assistant Professor at Marquette University. She received her Ph.D. in Computer Science from Virginia Tech under the supervision of Professor Kirk W. Cameron. Her research interest includes high performance computing, parallel and distributed systems, and performance and power analysis and modeling.. Website: http://people.cs.clemson.edu/~rge/index.html.

NSF CAREER project: Dr. Ge’s NSF CAREER project investigates power bounded high performance computing on emergent and future heterogeneous computer clusters Areas of collaboration:

• Large-scale systems • Energy efficient computing • Performance analysis and modeling • Power management

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Thomas Hacker is an Associate Professor of Computer and Information Technology at Purdue University and Visiting Professor in the Department of Electrical Engineering and Computer Science at the University of Stavanger in Norway. Dr. Hacker’s research interests center around high-performance computing and networking on the operating system and middleware layers. Recently his research has focused on cloud computing, cyberinfrastructure, scientific workflows, and data-oriented infrastructure. Website: https://polytechnic.purdue.edu/profile/tjhacker

NSF CAREER project: Dr. Hacker’s research is focused on understanding the reliability characteristics of large scale HPC systems and seeks to improve the reliability profile of computational jobs that utilize these systems. Areas of collaboration:

• Reliability • High performance computing

Richard Hennig is an associate professor in Materials Science and Engineering at the University of Florida. He received his Diploma in Physics at the University of Göttingen in 1997 and his Ph.D. in Physics from Washington University in St. Louis in 2000. After working as a postdoctoral researcher and research scientist at Ohio State University, he joined the faculty of the Department of Materials Science and Engineering at Cornell in 2006 as an Assistant Professor. In 2014 he moved to the University of Florida as an Associate Professor. His research interests include the development and application of computational methods for the discovery and design of novel materials for energy technologies and electronic devices.

Website: http://hennig.mse.ufl.edu NSF CAREER project: Materials interfaces, particularly between solid and liquid phases, are at the core of many modern technologies ranging from energy conversion to biomaterials and present a challenge to many computational methods; we develop multi-scale methods and high-throughput approaches to accurately and efficiently predict the structure and properties of such interfaces. Areas of collaboration:

• Experimentalists and theorists working on 2D materials • Electrochemical systems • Solid/liquid interfaces

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Shantenu Jha is an Associate Professor of Computer Engineering at Rutgers University. His research interests lie at the triple point of Cyberinfrastructure R&D, Applied Computing and Computational Science. Shantenu leads the RADICAL-Cybertools (http://radical-cybertools.github.com) project which are a suite of standards-driven and abstractions-based tools used to support large-scale science and engineering applications.

Website: http://radical.rutgers.edu/shantenu NSF CAREER project: Research and develop middleware/abstractions for scalable computational science problems involving distributed data requirements. Areas of collaboration:

• Computational Medicine • Complex Software Engineering development practice and principles • Computational Science applications with distributed data requirements

Kerk Kee is an Assistant Professor in the Department of Communication Studies at Chapman University, Orange, California. He received his Ph.D. in Organizational Communication from the University of Texas at Austin, after completing his B.S. in Mechanical Engineering at the University of Nebraska-Lincoln, and M.A. in Organizational Communication at San Diego State University. Kerk directs the Organizing, Communication, & Technology (OCT) Group at Chapman University (www.octgroup.org). His research centers on the diffusion of innovations theory as it applies to organizational communication and health communication. More specifically, he studies the spread of cyberinfrastructure and its associated technologies through cross-disciplinary collaborations in scientific organizations, and the flow

of health information through social clusters in online communities. Besides studying cyberinfrastructure diffusion as an organizational researcher, he collaborates with mathematicians on applying the mathematical theory of simplicial complexes to model social clustering in social networks. His diffusion research has been funded by the National Science Foundation and the Bill & Melinda Gates Foundation. He is the PI of two current NSF projects funded by the VOSS (Virtual Organizations as Sociotechnical Systems) program for 2013-2016, and CAREER (Faculty Early Career Development) program for 2015-2020. Website: www.ekerk.com <http://www.ekerk.com> NSF CAREER project: Dr. Kee’s CAREER project explores the organizational capacity of virtual organizations (or dispersed e-science projects) necessary for effective, efficient, and

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productive development, implementation, and diffusion of cyberinfrastructure and related technologies. Areas of collaborations:

• Technology adoption • Innovation diffusion • Virtual organizations in e-science • Capacity building for cyberinfrastructure.

Wenwen Li is an Assistant Professor in the GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University (ASU). She obtained her PhD degree in Earth System and Geoinformation Science in 2010 from George Mason University. She joined ASU after finishing her postdoctoral research at the University of California, Santa Barbara from 2010-2012. Her research interest is geographic information science with a focus on cyberinfrastructure, big data, semantic interoperability, spatial information retrieval, and distributed geospatial information processing.

Website: http://www.public.asu.edu/~wenwenl1 NSF CAREER project: Dr. Li’s CAREER project aims to develop new methods and techniques for establishing an integrated, sustainable and smart cyberinfrastructure to revolutionize knowledge discovery in data and computational intensive geographical sciences. Areas of collaboration:

• Cyberinfrastructure • Geospatial semantics • Spatiotemporal data analysis and reasoning • Spatial decision support • Intelligent web search

Han Liu is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University, where he leads the Statistical Machine Learning (SMiLe) Laboratory. In 2011, he received the joint PhD in Statistics and Machine Learning from the Machine Learning Department at the Carnegie Mellon University. His theoretical research focuses on nonparametric graphical models, nonconvex statistical optimization, and post-regularization inference. His applied research focuses on brain science, genomics, and computational finance. Han Liu is the recipient of several research awards including the Tweedie New Researcher Award (from IMS), the Noether Young Scholar Award (from ASA), the NSF

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CAREER Award (from DMS), the Howard B Wentz Award (from Princeton SEAS), and has received several best paper awards in the International Conference on Machine Learning and International Conference on Artificial Intelligence and Statistics. Website: http://www.princeton.edu/~hanliu/ NSF CAREER project: Statistical machine learning, Data enabled science and machine intelligence, nonparametric graphical models, nonconvex statistical optimization, post-regularization inference, brain science and computational finance. Areas of collaboration:

• Theoretical computer science • Brain science • Programming language and software engineering

Laurence Loewe is an Assistant Professor at the University of Wisconsin- Madison. He investigates questions in the new field of evolutionary systems biology, merging systems biology and population genetics. The coding and computing challenges that need to be mastered towards this end are substantial and require a computing environment that provides long-term stability with explicit support for diverse programming constructs and notions that facilitate the implementation of biological models and link them to real-world observations. To enable this, he is developing Evolvix, the first general programming language designed by biologists for biologists (http://evolvix.org, current download is in early pre-alpha stage). The main purpose of Evolvix is to make it easy for

biologists to accurately describe the systems they study, in order to make it easy for computers to analyze and simulate the corresponding models. Led by Kate Scheuer, his group is currently using Evolvix to develop a cutting edge circadian clock simulation model, improving Evolvix in the process. To recruit the computing power necessary to analyze models too complex for a single computer, Evolvix will have language features that facilitate the use of globally distributed computing systems like evolution@home or the Open Science Grid built on the HTCondor distributed computing infrastructure. Website: http://wid.wisc.edu/profile/laurence-loewe/ NSF CAREER project: Design, implement and test enough of the new programming language Evolvix to ensure it makes accurate modeling of highly concurrent biological models as easy as possible in as many contexts as possible. Areas of collaboration:

• As any new programming language, Evolvix needs reviewers to improve the current language design before major features get locked in and become impossible to change.

• Experts with time and interest in any area of importance for making a programming language highly reliable and usable.

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• Experts who enjoy finding names that many people easily recognize as excellent descriptions of much more complicated concepts to help select the best trans-disciplinary keywords for Evolvix.

Kamesh Madduri is an assistant professor in the Computer Science and Engineering department at The Pennsylvania State University. He received his PhD in Computer Science from Georgia Institute of Technology's College of Computing in 2008, and was previously a Luis W. Alvarez postdoctoral fellow at Lawrence Berkeley National Laboratory. His research interests include high-performance computing, parallel graph algorithms, and massive scientific data analysis. He is a recipient of the NSF CAREER award (2013), a co-recipient of the best paper award at the 42nd International

Conference on Parallel Processing (2013), and was awarded the first Junior Scientist prize from the SIAM Activity group on Supercomputing (2010). He is a member of IEEE, ACM, and SIAM. Website: http://www.cse.psu.edu/~madduri/ NSF CAREER project: We are designing scalable parallel algorithms and software for novel graph computations on shared-memory multicores, manycore accelerators, and distributed-memory clusters. Areas of collaboration:

• Graph analytics • Bioinformatics and genomics • Scientific data management • Parallel algorithms

Arya Mazumdar is an assistant professor in the University of Minnesota - Twin Cities (UMN) since 2013. Before coming to UMN, he was a postdoctoral scholar (2011-12) at the Massachusetts Institute of Technology. Arya received his Ph.D. from the University of Maryland, College Park, in 2011. He spent the summers of 2008 and 2010 at the Hewlett-Packard Laboratories, Palo Alto, CA, and IBM Almaden Research Center, San Jose, CA, respectively. Arya is a recipient of NSF CAREER award (2015), the IEEE ISIT Best Student Paper Award (2010), and the 2011 Distinguished Dissertation Award (at University of Maryland). Arya’s research interests include

information and coding theory and their applications to storage, security and biology. Website: http://www.ece.umn.edu/users/arya/

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NSF CAREER project: We analyze the fundamental limits of reliable distributed storage systems, as well as propose explicit (fast algorithmic) constructions of codes suitable for such systems. Areas of collaboration:

• Information and Coding Theory • Distributed Systems • Machine Learning • Networking and Network Coding

Ningfang Mi is an Assistant Professor in Department of Electrical and Computer Engineering at Northeastern University. She obtained her Ph.D. from the College of William and Mary, Department of Computer Science in 2009, under the guidance of Prof. Evgenia Smirni. Dr. Mi was a recipient of 2015 NSF CAREER Award, 2014 AFOSR Young Investigator Research Program (YIP) Award and 2010 IBM Faculty Award. The goal of her research is to develop innovative techniques and algorithms to build adequate system models and support performance and reliability analysis for explaining large system behavior, predicting application performance, and ensuring high resource efficiency and system dependability.

Website: http://www.ece.neu.edu/~ningfang/ NSF CAREER project: My NSF Career project mainly focuses on (1) building adequate system models to explain large system behavior and predict performance and reliability in a much more accurate manner, and (2) developing new techniques for workload measurements and model parameterizations to capture essential processing information in system models. Areas of collaboration:

• Capacity planning • Mapreduce scheduling • Cloud computing • Resource management • Performance evaluation

Hong Qin has been working at Spelman College since 2009. He becomes an Associate Professor of Biology in Fall of 2015. He received his Ph.D. in Biochemistry and Molecular Biology and postdoc training of statistical genetics at the University of Chicago. One of his main research interests is to develop a probabilistic gene network model for cellular aging and apply it to interpret empirical results, funded by his recent NSF Career award. Another of his research interests is to understand how genetic variations influences

health disparity. He is also devoted to developing undergraduate curricula on computational and mathematical biology. His YouTube educational channel is http://youtube.com/qinstat.

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Website: http://www.spelman.edu/academics/faculty/hong-qin NSF CAREER project: Using a probabilistic gene network modeling of cellular aging to investigate the conserved mechanism of lifespan extension effect of calorie restriction. Areas of collaboration:

• Probabilistic graph modeling of gene networks • Network permutation • Network aging • Genome analysis • Undergraduate education of computational science

Judy Qiu is an assistant professor of Computer Science at Indiana University. Her general area of research is in data-intensive computing at the intersection of Cloud and HPC multicore technologies. This includes a specialization on programming models that support iterative computation, ranging from storage to analysis which can scalably execute data intensive applications. Her research has been funded by NSF, NIH, Microsoft, Google, Intel and Indiana University. Judy Qiu is the director of a new Intel Parallel Computing Center at IU. She is the recipient of a NSF CAREER Award in 2012, Indiana University Trustees Award for Teaching Excellence in 2013-

2014, and Indiana University Outstanding Junior Faculty Award in 2015. Website: http://www.cs.indiana.edu/~xqiu/ NSF CAREER Project: Data-Enabled Discovery Environments for Science and Engineering with novel technologies driven by applications. Areas of collaboration:

• Large scale data analysis • Streaming analysis • Online course using MOOC and Cloud technologies

Ioan Raicu is an Assistant Professor in the Computer Science Department at Illinois Institute of Technology (IIT), as well as a guest research faculty at Argonne National Laboratory. He is also the founder and director of the Data-Intensive Distributed Systems Laboratory (DataSys) at IIT. His expertise lies in resource management in the general area of distributed systems. His work focuses on a relatively new paradigm of Many-Task Computing (MTC), which aims to bridge the gap between two predominant paradigms from distributed systems, High-Throughput Computing (HTC) and High-Performance Computing (HPC). He has received numerous awards, including the

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TCSC Young Achievers in Scalable Computing (2014), the Outstanding Service Award from IEEE/ACM CCGrid (2014), the Junior Faculty Research Award from IIT (2013), the prestigious NSF CAREER award (2011) for his innovative work on distributed file systems for extreme-scales, the NSF/CRA Computation Innovation Fellowship at Northwestern University (2009), and the GSRP Fellowship from NASA Ames Research Center (2006). He obtained his Ph.D. in CS from University of Chicago (UChicago) under the guidance of Dr. Ian Foster in 2009. His publications have appeared in 100 peer-reviewed articles (including 1 book, 18 journal papers, 4 book chapters, 38 conference papers, 17 workshop papers, and 22 extended abstracts), which received 6094 citations (according to Google Scholar). His H-index is 31 and his M-Index is 2.38; these metrics are often used as useful indexes to characterize the scientific output of researchers. His work has been funded by the NASA ARC, DOE ASCR, NSF/CRA CIFellows, NSF CAREER, the NSF REU, DOE’s ANL, DOE’s Fermi National Accelerator Laboratory (FNAL), and DOE’s Los Alamos National Laboratory (LANL). Website: http://www.cs.iit.edu/~iraicu/ NSF CAREER Project: My NSF CAREER award has focused on the design and implementation of scalable storage systems towards extreme scales distributed systems through innovations in data management techniques advancing the state of the art in distributed metadata management, cooperative caching, dynamic compression, information dispersal algorithms, and data-aware scheduling. Areas of collaboration:

• Data-intensive computing • Workflow systems • Production distributed storage systems

Suzanne Shontz is an Associate Professor in the Department of Electrical Engineering and Computer Science at The University of Kansas (KU). She is also affiliated with the Graduate Program in Bioengineering and the Information and Telecommunication Technology Center. Suzanne’s research is in parallel scientific computing and focuses on the development of meshing and numerical optimization algorithms and their applications in medicine, image processing, electronic circuits, and materials. Suzanne is the recipient of a 2011 NSF PECASE Award from President Obama for her research in computational- and data-enabled science and engineering. She also received a 2011 NSF CAREER Award for her research on parallel

dynamic meshing algorithms, software, and theory for simulation-assisted medical interventions. Along with Thomas Hacker of Purdue University and Linwei Wang of the Rochester Institute of Technology, she is a co-chair of the 2015 NSF CyberBridges Workshop. She and Thomas Hacker co-founded the NSF CyberBridges Workshops and served as co-chairs of the workshops in 2012-2014. Suzanne chaired the 2010 International Meshing Roundtable, the premier conference in unstructured mesh generation, and has served on numerous program committees

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for international conferences in computational- and data-enabled science and engineering. She is also an Associate Editor of the De Gruyter Open Book Series in Medicine. Website: http://people.eecs.ku.edu/~shontz NSF CAREER Project: The goal of my NSF CAREER project is to research and develop parallel dynamic meshing algorithms, theory, and software for use in the design of patient-specific treatments for diseases such as deep vein thrombosis and hydrocephalus. Areas of collaboration

• Biomechanics modeling • Medical image processing • Shape and topology optimization

Melissa Smith is an Associate Professor of Electrical and Computer Engineering at Clemson University in South Carolina. Before her appointment to Clemson in 2006, Dr. Smith was a research associate at the Oak Ridge National Laboratory (ORNL) for 12 years. In 2004, Dr. Smith began collaborations with the newly formed Future Technologies Group at ORNL and conducted research on emerging computing architectures including reconfigurable computers, multi-core, and optical processors. She continues to collaborate with some of the top research scientists at ORNL and across the country in areas of heterogeneous high-performance computing and System Performance Modeling and Analysis.

Dr. Smith’s current research activities focus on the applied use of emerging heterogeneous computing architectures. Her research group is interested in the performance computer architectures for various application domains including scientific applications (modeling and simulation), high-performance or real-time embedded applications, and medical and image processing. Her group explores optimization techniques and performance analysis for emerging heterogeneous platforms, including many-core processors, Graphical Processing Units (GPUs) and Field-Programmable Gate Array-based (FPGA-based) reconfigurable computers. Also of interest are the tools and methodologies that are needed to efficiently and effectively program and utilize these architectures.

Website: http://www.clemson.edu/ces/departments/ece/faculty_staff/faculty/msmith.html

NSF CAREER project: This CAREER research will formulate an inclusive hierarchical framework for performance modeling and analysis of hybrid computing systems that include multi-core processors and accelerators including a coarse-grained model for rapid assessment of the fitness match between the application and the hybrid system and a fine-grained model for predicting execution time and workload balance analysis. Areas of collaboration

• Big data applications • Heterogeneous HPC system users

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• Performance modeling and analysis

Dr. A. Selcuk Uluagac is currently an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Florida International University (FIU). Before joining FIU, he was a Senior Research Engineer in the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Technology. Prior to Georgia Tech, he was a Senior Research Engineer at Symantec. He earned his Ph.D. with a concentration in information security and networking from the School of ECE, Georgia Tech in 2010. He also received an M.Sc. in Information Security from the School of Computer Science, Georgia Tech and an M.Sc. in ECE from Carnegie Mellon University in 2009 and 2002, respectively.

The focus of his research is on cyber security topics with an emphasis on its practical and applied aspects. He is interested in and currently working on problems pertinent to the security of Cyber-Physical Systems and Internet of Things. In 2015, he received a Faculty Early Career Development (CAREER) Award from the US National Science Foundation (NSF) and was selected to receive fellowship from the US Air Force Office of Sponsored Research (AFOSR)’s 2015 Summer Faculty Fellowship Program. In 2007, he received the “Outstanding ECE Graduate Teaching Assistant Award” from the School of ECE, Georgia Tech. He is also an active member of IEEE (senior grade), ACM, and ASEE and a regular contributor to national panels and leading journals and conferences in the field. Currently, he is the area editor of Elsevier Journal of Network and Computer Applications and serves on the editorial board of the IEEE Communication Surveys and Tutorials. Website: http://web.eng.fiu.edu/selcuk. NSF CAREER project: Dr. Uluagac's CAREER project investigates the sensory side-channel (e.g., acoustic, seismic, light, temperature) threats to Cyber-Physical Systems (CPS) devices and applications and evaluates the feasibility and practicality of the attacks on real CPS equipment with an end goal of designing novel sensory side-channel-aware security tools and techniques for the CPS devices. Areas of collaboration:

• Cybersecurity • Security of Internet of Things (IoT) • Security of Cyber-Physical Systems (CPS) • Big data security • Networking • Educational research

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Linwei Wang is an Associate Professor in the PhD Program of Computing and Information Sciences at the Rochester Institute of Technology in Rochester, NY. Her research interests center around data-driven modeling, statistical inference, and data inversion of complex systems, with application to computational physiology and personalized biomedicine. She currently directs the Computational Biomedical Lab in RIT, with a recent research focus on personalized modeling of in-vivo cardiovascular systems using noninvasive biomedical data, and its application to improve patient care in cardiac arrhythmia and other heart diseases. Her research is funded by the National Science Foundation and

the National Institutes of Health. Dr. Wang obtained her bachelor degree in Optic-Electrical Engineering from Zhejiang University (China) in 2005, her master degree in Electronic and Computer Engineering from Hong Kong University of Science and Technology in 2007, and her PhD in Computing and Information Sciences from RIT prior to joining the faculty of RIT in 2009. Website: http://phd.gccis.rit.edu/linweiwang/ NSF CAREER project: Dr. Wang’s NSF CAREER projects investigates the improvement of data-driven inference and learning by the integration of physics-based modeling, with a driving application in healthcare and medicine. Areas of collaboration:

• Multi-physics, multi-scale modeling and simulation • Statistical inference & optimization • Machine learning • Signal and image analysis • High performance computing • Scientific visualization

Liqiang (Eric) Wang is a Castagne Associate Professor in the Department of Computer Science at the University of Wyoming. He has been an assistant professor (2006-2012) and an associate professor (2012-present) in the same department. He received Ph.D. in Computer Science from Stony Brook University in 2006. He was a visiting Research Scientist in IBM T.J. Watson Research Center during 2012-2013. His research interest is the design and analysis of parallel systems for big-data computing, which includes two aspects: design and analysis. For design, he is currently working on optimizing performance, scalability, resilience, and load balancing of data-intensive computing, especially on Cloud, GPU, and multicore platforms. For the aspect of analysis, he

focuses on using program analysis to detect programming errors and performance defects in large-scale parallel computing systems.

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He received an NSF CAREER Award in 2011, and an Overseas Scholars Collaborative Research Award (a.k.a. Outstanding Chinese Overseas Young Scholar Award) by Natural Science Foundation of China (NSFC) in 2014. Website: http://www.cs.uwyo.edu/~lwang7/ NSF CAREER project: The goal of my NSF CAREER project is to design innovative algorithms and develop a scalable toolkit to efficiently and effectively analyze parallel programs and detect potential errors on the emerging heterogeneous and extreme scale computing platforms.

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Appendix C. Attendee Breakout Session

In reflection of the feedback from previous years’ attendee feedback, in CyberBridges 2015 the poster session was replaced by a breakout session. During this 1-hour long session, the attendees were divided into 5 groups. Each attendee was instructed to prepare one slide to describe a current research or education challenge in their career that they would like to discuss with the group and elicit potential collaborations. The discussions from each group are summarized below. Red Group: (Scribe: Linwei Wang) The group discussed challenges in several areas:

• Avoiding Achilles heel in exascale computing with distributed file systems. The challenges in this area relate to storage and file systems for distributed systems, and the need for a general solution across domains. The specific challenges discussed by the group were in going from a research prototype to a production environment, and collaboration with data-intensive science. The discussion topics included collaboration with industry, solutions for extensive file I/O, and scaling to tens of thousands of nodes.

• Computational condensed matter / many-body physics (quantum mechanics). The group discussed the area of managing matrix operations for large and sparse matrices, and the challenges inherent in parallelizing and using these matrices for condensed mater and many-body physics.

• Performance modeling of algorithms on heterogeneous compute platforms. The group discussed the gap between domain-specific application and general software/HPC infrastructure, and the challenges related to multi-disciplinary training in domain science, computer science theory, computer science architecture, and computational science performance engineering.

• Automatic pre-processing of heterogeneous data before inputting them to computational models. The group discussed the challenges in the area of urban flooding and drainage sustainability related to pre-processing data to increase the level of automation to reduce the tedium of preprocessing data, and the need for a common CI based for data-based and simulation approaches.

• The importance of software carpentry and the challenges and importance of open software. The final area of discussion was the in the area of “software carpentry” and version control as it relates to students and their software development efforts.

Blue Group: (Scribe: Suzanne Shontz) Suzanne Shontz For research and development of parallel dynamic meshing algorithms and software, finite element codes that employ unstructured meshes suffer from low cache utilization and reduced performance due to irregular data structure and access. Topological changes to the mesh further alter the access pattern in a dynamic manner. The challenge is how to improve data locality of such codes in an automatic manner, particularly as we move towards exascale computing.

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Baskar Ganapathysubramanian To explore process-structure-property maps in organic solar cells, two challenges are identified. One is 3D parallel adaptive grid generation, where mesh refinement could evolve as the interface evolves and the refined and evolved meshes are domain decomposed with awareness of load balance and communication overhead. The second challenge is feature extraction for large morphology datasets.

Arya Mazumdar In a distributed storage network, not all servers are connected to each other and some links are easy to establish considering physical proximity, architecture, platform, and connections of the servers. A recoverable distributed storage network is one in which data on every node can be recovered from its neighbors. This type of network has interesting machine learning applications in, for example, neural networks (synthesis problems) via low-density parity-check (LDPC) code and structured matrices for low-rank approximations/regression.

Kerk Lee In organizational research, there is a challenge of securing sponsors/champions who are willing to help provide access to or find organizations for data collection (such as interviews and surveys). It is also difficult to strike a balance between being domain-specific and achieving generalizability across domains. Being in an undergraduate school, the challenge comes from primarily working with undergraduate students who, while they may be motivated, are not always trained to participate in research at the desired level. There is also a disconnection between class and research which limits the opportunity to recruit students for research.

Behcet Acikmese Real-time optimization-based autonomous control examines questions such as whether autonomous control problems can be formulated completely and accurately as tractable optimization problems, whether robust real-time implementable numerical methods can be developed for onboard computations, and whether the performance of the algorithm can be verified and validated. At the theoretical level, questions arise regarding what are the methods of convexification for control problems, what are the tractable alternatives if convexification is not possible, and how can different layers of decision-making be integrated systematically. At the application level, questions arise including whether the optimization can be implemented in real- time and how to verify the algorithms. In regards to education, challenging questions needs to be answered, such as what new classes are needed in applied mathematics, whether the current undergraduate classes in control systems are sufficient, what new classes are needed in scientific computing and software engineering, and how can we teach modeling in a general setting.

Paul Bogdan A cyber-physical (CPS) systems approach to personalized medicine includes biosensors in the physical domain that communicate sensed biomarkers for analysis, short-range biosensor-to-network-on-chip communications for computational feedback, long-range communication to send the sensed data for medical experts to review, and the resulting interventional processes. One example application is the artificial pancreas, in which short-range communication between

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biosensors, the controller, and the actuator determine the optimal amount of insulin based on data-driven modeling and optimal control. Eric Wang Challenges were discussed within research in big data, HPC, and cloud computing, such as the optimization of big data platforms, MPI vs Hadoop, and how to achieve good memory and speed. Silver Group: (Scribe: Rong Ge) Alexander Alexeev The goal of mesoscale modeling of responsive polymer networks is to develop computationally-efficient models for stimuli-sensitive polymer networks. Such models have many applications in modeling synthetic and natural materials, such as in drug delivery and tissue engineering. The challenges include performing computational simulations that are time consuming and validation of the models and simulations with experimental data. Collaborations need to be formed between people who can do experiments and people who are computational scientists and engineers. Challenges also are found in education in which the projects are too complicated for students and the faculty are responsible for decomposing the projects into smaller pieces for the students. Ningfang Mi The goal of research in capacity planning for large clusters with heterogeneous architectures and diverse applications is to predict application performance and system behavior to ensure high resource efficiency and high system reliability. This involves performance modeling that studies the interaction between system activity hardware and the application. Research challenges include the development of performance models to determine the best application platform, the incorporation of critical workload information into capacity modeling, and the building of an integrated model to capture the characteristics of both system workloads and failure events. Melissa Smith As we move towards exascale computing, heterogeneous supercomputers and clusters are become more popular such as Trinity, Cori, Microsoft Cluster (with Altera FPGA). Furthermore, there has been an increasing number of architectures of interest for scientific applications as well as programming languages available to the end user. To achieve optimal performance, it is important to appropriately map the application to an optimal architecture. In this research, challenges can be approached in two ways. A qualitative approach is more useful for cases where end users do not have detailed information about the application. Challenges with this approach are to identify important architecture-independent classifiers. This approach is intuitive and easy to implement, but architecture updates require re-evaluation and attention for scaling issues. A quantitative approach is more insightful when the user has detailed, in-depth profiling information available. The challenges include multiple classification factors and the fact that benchmark-based quantification can be biased. This approach is accurate and can allow for run-time prediction, more quantitative estimation of the performance between architectures, and can adjust for scaling. However, it is very time consuming and not appropriate for prototype implementation of pseudo algorithms. Collaborations are needed between experts in applications and algorithms.

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Ge Rong Power-bounded HPC considers power as the scarce resource and utilizes all available power to achieve maximum performance for applications. It considers questions such as how applications perform in a power-bounded situation, what is the best performance that can be achieved with this power limit, what can be achieved with unlimited power, and how to ensure that applications do not use more than the scheduled power. Challenges in power-bounded HPC include how to be aware of application power demands that may change over time, tradeoffs between power consumption and application performance, the limiting factor such as cooling capacity, and other factors. Richard Henning To model fluid-structure interaction, a sharp-interface immersed boundary method on overset-curvilinear grids is described. Challenges include how to develop effective preconditioners, how to improve communication between grids, how to improve the search algorithm, how to collaborate with people, and from where to obtain data. Wenwen Li In data-driven and service-oriented polar cyberinfrastructure, we look for service-oriented data sharing that can lead to data discovery and search, which in turn leads to data integration, analysis, and a knowledge creation engine. Research challenges include access to an HPC resource (with limited access based on permissions and which are difficult to customize) and collaboration with domain scientists. Educational challenges include how to inspire students, how to manage students (engineering vs. research), and how to improve international students’ writing skills. Suggestions are to encourage students to help each other, to require them to read and critique papers, to identify students for research, to set deadline for students, and to have students work in structured and unstructured ways. Green Group (Scribe: Thomas Hacker) Summary: In the area of educational infrastructure, capabilities, and skills, the challenges are to devise new techniques for teaching skills as well as knowledge quickly, teaching students in biology programming skills as a part of a course, and developing a cyberinfrastructure platform to meet education needs for the Internet of Things. In the area of research, there are two challenge fronts: provenance and more specifically data provenance. In the broader area of provenance, challenges are centered on ensuring the integrity of devices, data, and extracting knowledge and meaning from these data. At the device and storage level, there is a need for provenance systems to ensure the integrity of devices and storage systems. Another areas of need are focused on generalizing machine learning methods for different domains, a unified modeling language data rich scientific systems, and the need for collecting and representing data from these systems in a standard agreed-upon scheme. Also, there is a need for real-time provenance capture from a highly variable ecosystem of hardware, software, and people environments. Group members discussed several topics, described in detail below by presenter.

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Thomas Hacker: In terms of education, in contrast to the past, information is now easily and ubiquitously available from the internet. Understanding this information, and having the skills to put this knowledge into practical use is much more difficult today than finding the information. The question is: how can we transform education practices to go beyond a behaviorist epistemology (focused primarily on knowledge transfer) to a practice based constructivist epistemology that provides a learning environment that can fully engage the student to acquire understanding and skills? Hong Qin: Biology students do not have a background in computation and need to quickly learn to become functional in a programming language (specifically Python) for computational biology. R is becoming a very good tool for asking scientific questions for undergraduate students. The challenge is to train students to learn to use R. Selcuk Uluagac: Dr. Uluagac’s current research activities are in the areas of cyber-physical systems (CPS), the internet of things (IoT), security for critical infrastructure networks, and big data security. The challenges posed in the area of education is the need for collaborators in developing the cyberinfrastructure for new classes and for research in CPS and IoT. Han Liu: Dr. Liu is interested in machine learning and is working on statistical machine learning. The challenges in Dr. Liu’s research are: the need for a modeling and programming language for data science, how to develop a unified modelling and programming language for modelling the statistics and underlying systems, and gathering large-scale brain imaging datasets that can be collected and cleaned (especially data from fMRI). Asuncion Hazeline: Dr. Hazeline is investigating the integration of data provenance with software traceability. The challenges in this area are: real-time capture of provenance in a distributed computing platform with heterogeneous architectures with a variety of hardware, software, and “people” environments; capturing provenance in highly constrained devices with small memory or limited power; capturing data provenance in legacy software, and connecting software and data provenance in a distributed large research team using a variety of data collection methods. Kevin Butler: Dr. Butler is focused on research in system security, secure storage, and provenance. The research challenges in his area are to refine provenance systems to support device attestation and storage systems, especially for SCADA systems. Gold Group (Scribe: N/A) Note: The summary of this group’s discussion is based on the slides provided by group members. No scribe notes were submitted from this group.

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Waheed Bajwa:

Dr. Bajwa is investigating supervised machine learning using big, distributed data. The challenge Dr. Bajwa is encountering in his research is a problem of big data isolation in which there are massive local datasets that cannot be shared with other sites due to constraints such as transmission costs and delays, and privacy concerns. Although a site can locally train machine learning data sets, the ability to overcome barriers in sharing information could possibly lead to better performance.

Kaushik Chowdhury:

Dr. Chowdhury is involved in research in big data in spectrum management and networking applications.

Shantenu Jha:

Dr. Jha discussed challenges related to allowing applications to execute across multiple sites (“multi-site execution”). One challenge is to prevent applications from becoming “bound” to a specific resource and thus becoming unable to be run across multiple sites. The advantages of “seamless multi-site execution” include faster time-to-solutions and improved system throughput and utilization. Dr. Jha described that most XSEDE applications do not need to be bound to a specific resource. The challenges to be addressed for this include: interfaces and abstractions needed to federate resources` and the algorithms necessary for multi-site execution.

Lawrence Loewe:

Dr. Loewe described his experiences in developing Evolvix, a programming language designed for biologists.

Judy Qiu:

Dr. Qiu discussed her efforts in the area of data analysis and applications. She discussed a model (HPC-ABDS) that seeks to provide software building blocks with HPC style performance and broad functionality, and the Scalable Parallel Interoperable Data Analytics Library (SPIDAL) that is built upon this model. She also described a cloud computing MOOC course in the area of education and training.