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Biographical Information for Planning Committee Members

PETROS KOUMOUTSAKOS, Chair, is the Herbert S. Winokur Professor of Engineering and Applied Sciences and Area Chair of Applied Mathematics at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). He studied naval architecture (diploma from the National Technical University of Athens, MEng from the University of Michigan) and aeronautics and applied mathematics (PhD from the California Institute of Technology [Caltech]). Koumoutsakos has conducted postdoctoral studies at the Center for Parallel Computing at Caltech and at the Center for Turbulent Research at Stanford University and NASA Ames Research Center. He has served as the chair of computational science at ETHZ Zurich (1997–2020) and has held visiting fellow positions at Caltech, the University of Tokyo, the Massachusetts Institute of Technology (MIT), and the Radcliffe Institute of Advanced Study at Harvard University, and he is a Distinguished Affiliated Professor at the Technical University of Munich. Koumoutsakos is an elected fellow of the American Society of Mechanical Engineers (ASME), the American Physical Society (APS), the Society of Industrial and Applied Mathematics (SIAM), and the Collegium Helveticum. He is recipient of the Advanced Investigator Award by the European Research Council and the Association for Computing Machinery (ACM) Gordon Bell prize in supercomputing. He is an elected member of the National Academy of Engineering (NAE).

JORDAN ELLENBERG is the John D. MacArthur Professor of Mathematics at the University of Wisconsin–Madison. His research centers on

number theory and arithmetic geometry. He is a Discovery Fellow at the Wisconsin Institute for Discovery, where he is part of the Machine Learning group and the Institute for Foundations of Data Science. Ellenberg is a member of the Science Board of IPAM. He has been writing for a general audience about math for over 15 years; his work has appeared in The New York Times, The Wall Street Journal, The Washington Post, Wired, The Believer, and The Boston Globe, and he is the author of the “Do the Math” column in Slate. Ellenberg’s Wired feature story on compressed sensing appeared in the Best Writing on Mathematics 2011 anthology. His novel The Grasshopper King was a finalist for the 2004 New York Public Library Young Lions Fiction Award. Ellenberg’s 2014 book, How Not to Be Wrong, was a New York Times and Sunday Times (London) bestseller.

MELVIN GREER is chief data scientist at Americas, Intel Corporation. He is a technical expert in the application of advanced mathematics, artificial intelligence, machine learning (ML), blockchain zero trust models and neuromorphic computing. His systems and software engineering experience has resulted in patented inventions in cloud computing, synthetic biology, and Internet of Things (IoT) bio-sensors for edge analytics. He has authored 5 books and published over 400 research papers. His research has been cited over 4,000 times worldwide. He is a member of the American Association for the Advancement of Science (AAAS) and has served for 8 years on the National Academies of Sciences, Engineering, and Medicine’s Government-University-Industry Research Roundtable. Greer has been appointed to senior advisor and fellow at the FBI Information Technology and Data Division, where he accelerates the FBI mission via data analytics and advanced data science techniques. He has been recognized by LinkedIn as a Top 10 Voice in Data Science and Analytics and is the recipient of the BDPA Lifetime Achievement Award. Greer also received the WashingtonExec inaugural Pinnacle Award as Artificial Intelligence Executive of the Year. He has been awarded the Black Engineer of the Year Awards (BEYA) Technologist of the Year Award and has been inducted into the 2023 BEYA Hall of Fame which recognize his outstanding technical contributions. Greer also is adjunct faculty for the advanced academic program at Johns Hopkins University, where he teaches the master of science course, “Practical Applications of Artificial Intelligence.” He has also been appointed senior advisor at the University of California, Berkeley, Goldman School of Public Policy where he develops and accelerates the adoption of public policy for emerging and advanced technologies.

BRENDAN HASSETT joined the Brown University faculty in 2015 as a professor of mathematics. He assumed the directorship of the Institute for

Computational and Experimental Research in Mathematics in July 2016. His research focus is algebraic geometry—the study of geometric objects defined as solutions to polynomial equations. He has written 70 research papers and has authored or co-edited 8 books. Hassett was the chair of the mathematics department at Rice University from 2009 to 2014. His work has been recognized with a Sloan Research Fellowship, a National Science Foundation (NSF) CAREER award, and the Charles W. Duncan Award for Outstanding Faculty at Rice University. He is a fellow of the American Mathematical Society and the AAAS. Hassett received his PhD from Harvard University in 1996 and then spent 4 years at the University of Chicago as a Dickson Instructor and NSF postdoctoral fellow.

YANN LECUN is the vice president and chief artificial intelligence (AI) scientist at Meta and a Silver Professor at New York University (NYU) affiliated with the Courant Institute of Mathematical Sciences and the Center for Data Science. He was the founding director of FAIR and of the NYU Center for Data Science. Lecun received an engineering diploma from ESIEE (Paris) and a PhD from Sorbonne Université. After a postdoctoral in Toronto, he joined AT&T Bell Labs in 1988, and AT&T Labs in 1996 as head of image processing research. Lecu joined NYU as a professor in 2003 and Meta/Facebook in 2013. His interests include AI, ML, computer perception, robotics, and computational neuroscience. He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for “conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing,” and a member of the National Academy of Sciences (NAS), the NAE, and the French Académie des Sciences.

HEATHER MACBETH is an assistant professor of mathematics at Fordham University. She was previously a postdoctoral at MIT and at the ENS (Paris). Macbeth is a pure mathematician, specializing in the partial differential equations arising in differential geometry. Since 2020 she has also worked on the formalization of these subjects in interactive proof assistants. She holds a PhD in mathematics from Princeton University and a BSc (Hons) in mathematics from the University of Auckland.

TALIA RINGER is an assistant professor at the University of Illinois at Urbana-Champaign. Their main interest is in making program verification using interactive theorem provers more accessible through better proof engineering tools and practices, especially when it comes to maintaining proofs as programs change over time. Ringer’s vision is a future of verification that is accessible to all programmers, not just to experts. They got their PhD from the University of Washington in June 2021,

where they were an NSF Graduate Research Fellowship Program fellow. Prior to graduate school, they earned their bachelor’s in mathematics and computer science from the University of Maryland, then worked at Amazon as a software engineer for 3 years. Ringer recently visited Google AI working on ML for formal proof. They are the founder and previous chair of the SIGPLAN-M international long-term mentoring program, the founder and president of the Computing Connections Fellowship, and a contributor to the Coq interactive theorem prover.

KAVITHA SRINIVAS is currently a senior research scientist at IBM Research, worked in the past as a chief technical officer and co-founder of RivetLabs, with over 20 years in research (2001–2023). She has worked extensively in the areas of semantic web, ontology reasoning, graph databases and more recently, code generation for automated ML. Srinivas has received several outstanding technical achievements awards at IBM Research, is published in major conferences such as ACM’s SIGMOD (Special Interest Group on Management of Data), the Association for the Advancement of Artificial Intelligence, the International Joint Conferences on Artificial Intelligence, the International Conference on Very Large Data Bases, and the International Semantic Web Conference, and been on organizational committees and program committees of conferences such as the International Conference on Extending Database Technology, the International Joint Conference on Artificial Intelligence, the World Wide Web Conference, etc. She was trained as a cognitive psychologist and left the field as an associate professor at Boston College to switch fields.

TERENCE TAO is a professor of mathematics at the University of California, Los Angeles, where he has taught since 1996. Tao has authored and contributed to several books, and his numerous articles have appeared in such publications as the Annals of Mathematics, Acta Mathematica, and the American Journal of Mathematics. He received the Fields Medal in 2006 and is a member of the NAS. Tao received an MSc (1992) from Flinders University of South Australia and a PhD (1996) from Princeton University. His areas of research include harmonic analysis, number theory, and combinatorics.