Neil Thompson is the Director of the FutureTech research project at MIT’s Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT’s Initiative on the Digital Economy. Previously, he was an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he co-directed the Experimental Innovation Lab (X-Lab), and a Visiting Professor at the Laboratory for Innovation Science at Harvard. He has advised businesses and government on the future of Moore’s Law, has been on National Academies panels on transformational technologies and scientific reliability, and is part of the Council on Competitiveness’ National Commission on Innovation & Competitiveness Frontiers. He has a PhD in Business and Public Policy from Berkeley, where he also did Masters degrees in Computer Science and Statistics. He also has a masters in Economics from the London School of Economics, and undergraduate degrees in Physics and International Development. Prior to academia, He worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.
Keynote Title: What will drive computer progress in the coming decades?
Abstract: Historically, computers have gotten better dramatically faster than other parts of the economy. But as Moore’s Law comes to an end, the most important source of computer progress is drying up. This talk will quantify how fast computers have been improving, and then consider the potential of upcoming technologies (quantum, specialized chips, etc.) for becoming the next big driver of computer progress.
University of Oxford
Heather Harrington obtained her PhD in 2010 from the Department of Mathematics at Imperial College London. She joined the Mathematical Institute at the University of Oxford in 2013 as a Hooke Research Fellow and EPSRC Postdoctoral Fellow and was affiliated with St Cross College and Keble College. Heather Harrington was promoted to Professor of Mathematics in 2020. She now has affiliations with St John’s College as a Research Fellow in Mathematics and the Sciences and the Wellcome Centre for Human Genetics an Associate Group Leader. Her research focuses on the problem of reconciling models and data by extracting information about the structure of models and the shape of data. She develops methods relying on techniques from computational algebraic geometry and topology to study complex biological systems. She is the Co-Director of the Centre for Topological Data Analysis. She has been recognised for her research with a Royal Society University Research Fellowship, London Mathematical Society Whitehead Prize, University of Cambridge Adams Prize, and a Philip Leverhulme Prize in Mathematics and Statistics.
Keynote Title: Computing for complex biological systems
U.S.’s Intelligence Advanced Research Projects Activity (IARPA)
Dr. Bill Harrod is a program manager at the U.S.’s Intelligence Advanced Research Projects Activity (IARPA). His technical focus areas include strategic computing, trusted microelectronics, nanoscale image reconstruction, and algorithms. He developed IARPA’s AGILE program, which aims to revolutionize computer architectures for strategically essential data challenges. The architectures being developed are driven by data-intensive graph analytics and artificial intelligence algorithms. Prior to joining IARPA, Dr. Harrod worked at the Department of Energy from 2011 to 2018, where he served as the Director of the Research Division in the Advanced Scientific Computing Research (ASCR) Office. From 2005 to 2010, Dr. Harrod was a Program Manager at the Defense Advanced Research Projects Agency (DARPA). At DARPA, Dr. Harrod led a series of groundbreaking exascale computing studies, which involved leading experts in industry and academia in investigating architectures, system software, resiliency, and applications. These pioneering studies led to a detailed projection of the technical challenges needed to advance supercomputing from the petascale to the exascale level. His earlier positions included Section Leader of the Math Software Group at Cray Research and Silicon Graphics, Inc and researcher at the University of Illinois. Dr Harrod earned a Ph.D. in mathematics from the University of Tennessee and a bachelor's degree in mathematics from Emory University.
Keynote Title: The Future of Data Centric Computing
Abstract: Today’s era of explosive data growth poses serious challenges for society in transforming massive, random, heterogeneous data streams and structures into useful knowledge, a necessity in every aspect of modern life, including national security, economic productivity, scientific discovery, medical breakthroughs, and social interactions. This burgeoning data, which is increasing exponentially not only in volume, but in velocity, variety, and complexity, already far outpaces the abilities of current computing systems to execute the complex data analytics needed to extract meaningful insights in a timely manner. The key problem with today’s computers is that they were designed to address yesterday’s compute-intensive problems rather than today’s data-intensive problems. Developing data centric computing systems requires a complete rethinking of computing architectures and technologies. The computations to be performed are determined by the data, and multiple applications might need simultaneous access to the same data. These are very different conditions than those characteristic of yesterday’s compute-intensive applications. IARPA’s new AGILE Program aims to provide data-analytic results in time for appropriate response, e.g., to predict impending adversarial events rather than forensically analyzing them after the fact. It will accomplish this goal by developing new system-level intelligent mechanisms for moving, accessing, and storing large, random, time-varying data streams and structures that allow for the scalable and efficient execution of dynamic graph analytic applications. This talk will provide a selection of the technical approaches that are being developed by the AGILE Performers.
London Institute for Mathematical Sciences
Prof. Yang-Hui He is a Fellow at the London Institute, Professor of Mathematics at City, University of London, Tutor in mathematics at Merton College, Oxford, and Chang-Jiang Chair of physics at Nankai University in China. He obtained his BA at Princeton, where he graduated summa cum laude and was awarded the Shenstone Prize and Kusaka Prize. He did his MA at Cambridge (Distinction, Tripos) and earned his PhD at MIT (president’s award). After a postdoc at the University of Pennsylvania, Yang joined Oxford University as the FitzJames Fellow and an STFC Advanced Fellow. He works at the interface of string theory, algebraic and combinatorial geometry, and machine learning.
Keynote Title: The AI Mathematician
Abstract: The search for the Theory of Everything has led to superstring theory, which then led physics, first to algebraic/differential geometry/topology, and then to computational geometry, and now to data science. With a concrete playground of the geometric landscape, accumulated by the collaboration of physicists, mathematicians and computer scientists over the last 4 decades, we show how the latest techniques in machine-learning can help explore problems of interest to theoretical physics and to pure mathematics. At the core of our programme is the question: how can AI help us with mathematics?