ACM e-Energy Keynote
Adam Wierman, Caltech
Online Optimization and Energy
June 26, 2019
Abstract: Online optimization is a powerful framework in machine learning that has seen numerous applications to problems in energy and sustainability. In my group at Caltech, we began by applying online optimization to ‘right-size’ capacity in data centers nearly a decade ago; and by now tools from online optimization have been applied to develop algorithms for geographical load balancing among data centers, demand response, generation planning, energy storage management, and beyond. In this talk, I will highlight both the applications of online optimization and the theoretical progress that has been driven by applications in energy and sustainability. Over a decade, we have moved from designing algorithms for one-dimensional problems with restrictive assumptions on costs to general results for high-dimensional problems that highlight the role of constraints, predictions, multi-timescale control, and more.
Biography: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he currently serves as Executive Officer. His research interests center around learning, optimization, and economics in networked systems. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been a coauthor on papers that received best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance, IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS.
FCRC Plenary Talks
Since ACM e-Energy is part of the FCRC, the following FCRC plenary talks are also part of the ACM e-Energy program.
Shriram Krishnamurthi, Brown University
The Role of Computer Science in Computer Science Education
June 26, 2019
Abstract: Computer science education is a difficult and fascinating problem, sitting at the intersection of the technical and human. It is also an increasingly urgent problem as countries around the world are rushing to add computing to their curricula and wrestling with broadening access to it. The needs are not limited to schoolchildren: working adults and the elderly use computers in ever more sophisticated ways.
What role can computer scientists play in this movement? Some examples (such as educational data mining) are obvious, but there are numerous other important (and perhaps subtle) facets of computing education that can all be enhanced by, or even depend on, computing research. In this talk I will provide a look at some of those questions, and identify a few of the numerous challenges the field has barely begun to address.
Biography: Shriram Krishnamurthi is a Professor of Computer Science at Brown University. With collaborators, he has created several influential systems and written multiple widely-used books. He also co-directs the Bootstrap integrated computing outreach program. His work has been recognized with SIGPLAN’s Robin Milner Young Researcher Award, SIGSOFT’s Influential Educator Award, and (jointly) SIGPLAN’s Software Award.
Jeannette Wing, Columbia University
Data for Good: Data Science at Columbia
June 27, 2019
Abstract: Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk.
The Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012, the Institute is now a university-level institute representing over 300 affiliated faculty from 12 different schools across campus. Data science literally touches every corner of the university.
In this talk, I will present examples of research and education projects to illustrate how data science is transforming every field, profession, and sector.
Biography: Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology.
Professor Wing’s general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM.
She is currently a member of: the National Library of Medicine Blue Ribbon Panel; the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).
Erik Lindahl, Stockholm University
Heterogeneous Acceleration and Challenges for Scientific Computing on The Exascale
June 28, 2019
Abstract: Modern computer hardware has become tremendously powerful in terms of FLOPS, memory bandwidth, multithreading and accelerators - but while codes could get close to the theoretical peak performance 20 years ago, many of today’s applications struggle to reach 10% due to the complexity of hardware. Here, I will showcase the challenges faced by real-world scientific applications that primarily focus on improving time-to-solution on increasingly powerful supercomputers rather than FLOP-counts, scaling, or relative acceleration. I will discuss how the end of Dennard scaling is a brick wall for many traditional algorithms (which scientists are only beginning to realise), and how we increasingly have been forced to go back and redesign fundamental algorithms that dominated fields as molecular simulation for more than 50 years. Still, with reasonable amounts of effort the future looks exceptionally promising: It is possible to redesign algorithms so they do not only work, but provide outstanding performance on new generations of processors and accelerators. However, this in turn leads to new challenges. To be able to utilise next-generation hardware efficiently many applications will need to move to heterogeneous acceleration where the various compute, memory, storage and network units work on different parts of a problem in parallel, with synchronisation requirements that can sometimes be on the microsecond scale. Finally, I will discuss the strategies needed for all these applications to be able to turn Exascale computing investments into scientific discoveries and industrial impact.
Biography: Erik Lindahl received a PhD from the KTH Royal Institute of Technology in 2001, and performed postdoctoral research at Groningen University, Stanford University and the Pasteur Institute. He is currently professor of Biophysics at Stockholm University, with a second appointment as professor of Theoretical Biophysics at the Royal Institute of Technology. Lindahl’s research is focused on understanding the molecular mechanisms of membrane proteins, in particular ion channels, through a combination of molecular simulations and experimental work involving cryo-EM and electrophysiology. He hasauthored some 130 scientific publications and is the recipient of an ERC starting grant.
Lindahl heads the international GROMACS molecular simulation project, which is one of the leading scientific codes to exploit parallelism on all levels from accelerators and assembly code to supercomputers and distributed computing. He is co-director of the Swedish e-Science Research Center as well as the Swedish National Bioinformatics Infrastructure, and lead scientist of the BioExcel Center-of-Excellence for Computational Biomolecular Research. His research work has been awarded with the Prix Jeune Chercheur Blaise Pascal, the Sven and Ebba-Christian Högberg prize, and the Wallenberg Consortium North prize. Lindahl is currently the chair of the PRACE Scientific Steering Committee.
Student Travel Grant Support
ACM e-Energy owes its success, in great part, to the generous backing of our academic and corporate partners. If you are interested in sponsoring ACM e-Energy 2019, please contact the General co-Chairs: Xiaojun Lin and Steven Low.