Keynote Speakers

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Opening Welcome Speech

Janil Puthucheary

Ministry of Communications & Information and Ministry of Health

Opening Address: Singapore National AI Strategy

Singapore launched our National AI Strategy in 2019, in recognition of the potential of AI to fundamentally transform the way we live, work and play. Our vision is to be a leader in developing and deploying scalable, impactful AI solutions, in key sectors of high value and relevance to our citizens and businesses by 2030. For the opening address, I will share more about one key enabler within our National AI Strategy, namely the presence of vibrant AI Research, Innovation, and Enterprise ecosystem in Singapore, and outline how the global community can partner with us on our journey to be a Smart Nation.

Bio: Dr Janil Puthucheary, 49, was elected Member of Parliament in 2011. He is currently Senior Minister of State, Ministry of Communications and Information, and Ministry of Health. He chairs OnePeople.sg, which works to promote racial harmony in Singapore. His political roles include Chair of Young PAP (the youth wing of the People's Action Party), and Whip.

On the Nature of Data Science

Jeffrey Ullman

Stanford University

On the Nature of Data Science

One can hear "Data Science" defined as a synonym for machine learning or as a branch of Statistics. I shall argue that it is far more than that; it is the natural migration of the technology of very large-scale data management to solve problems in scientific and commercial fields. To support my argument, I shall give a brief introduction to two algorithms that are important in data science but that are neither machine learning nor statistics: locality-sensitive hashing and counting distinct elements.

Bio: Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.

Data Science for Assembly Engineering

Sharon Glotzer

University of Michigan

Data Science for Assembly Engineering

Discovery and design of new materials able to self assemble from nanoscale building blocks are becoming increasingly enabled by large-scale molecular simulation. Aided by fast simulation codes leveraging powerful computer architectures, an unprecedented amount of data can be generated in the blink of an eye, shifting the effort and focus of the computational scientist from the simulation to the data. How do we manage so much data, and what do we do with it when we have it? In this talk, we discuss the applications of data science and data-driven thinking to molecular and materials simulation. Although we do so in the context of assembly engineering of soft matter, the tools and techniques discussed are general and applicable to a wide range of problems. We present applications of machine learning to automated, structure identification of complex colloidal crystals, high-throughput mapping of phase diagrams, the study of kinetic pathways between fluid and solid phases, and the discovery of previously elusive design rules and structure-property relationships.

Bio: Sharon C. Glotzer is the John W. Cahn Distinguished University Professor at the University of Michigan, Ann Arbor, the Stuart W. Churchill Collegiate Professor of Chemical Engineering, and the Anthony C. Lembke Department Chair of Chemical Engineering. She is also Professor of Materials Science and Engineering, Physics, Applied Physics, and Macromolecular Science and Engineering. Her research on computational assembly science and engineering aims toward predictive materials design of colloidal and soft matter: using computation, geometrical concepts, and statistical mechanics, her research group seeks to understand complex behavior emerging from simple rules and forces, and use that knowledge to design new classes of materials. Glotzer’s group also develops and disseminates powerful open-source software including the particle simulation toolkit, HOOMD-blue, which allows for fast molecular simulation of materials on graphics processors, the signac framework for data and workflow management, and several analysis and visualization tools. Glotzer received her B.S. in Physics from UCLA and her Phd in Physics from Boston University. She is a member of the National Academy of Sciences, the National Academy of Engineering and the American Academy of Arts and Sciences.

Safe Learning in Robotics

Claire Tomlin

University of California at Berkeley

Safe Learning in Robotics

In many applications of autonomy in robotics, guarantees that constraints are satisfied throughout the learning process are paramount. We present a controller synthesis technique based on the computation of reachable sets, using optimal control and game theory. Then, we present methods for combining reachability with learning-based methods, to enable performance improvement while maintaining safety and to move towards safe robot control with learned models of the dynamics and the environment. We will illustrate these “safe learning” methods on robotic platforms at Berkeley, including demonstrations of motion planning around people, and navigating in a priori unknown environments.

Bio: Claire Tomlin is a Professor of Electrical Engineering and Computer Sciences at the University of California at Berkeley, where she holds the Charles A. Desoer Chair in Engineering. Claire received her B.A.Sc. in EE from the University of Waterloo in 1992, her M.Sc. in EE from Imperial College, London, in 1993, and her PhD in EECS from Berkeley in 1998. She held the positions of Assistant, Associate, and Full Professor at Stanford from 1998-2007, and in 2005 joined Berkeley. Claire works in hybrid systems and control, and integrates machine learning methods with control theoretic methods in the field of safe learning. She works in the applications of air traffic and unmanned air vehicle systems. Claire is a MacArthur Foundation Fellow, an IEEE Fellow, and an AIMBE Fellow. She was awarded the Donald P. Eckman Award of the American Automatic Control Council in 2003, an Honorary Doctorate from KTH in 2016, and in 2017 she won the IEEE Transportation Technologies Award. In 2019, she was elected to the National Academy of Engineering and the American Academy of Arts and Sciences.

Automated Mechanism Design for Strategic Classification

Vincent Conitzer

Duke University

Automated Mechanism Design for Strategic Classification

AI is increasingly making decisions, not only for us, but also about us -- from whether we are invited for an interview, to whether we are proposed as a match for someone looking for a date, to whether we are released on bail. Often, we have some control over the information that is available to the algorithm; we can self-report some information, and other information we can choose to withhold. This creates a potential circularity: the classifier used, mapping submitted information to outcomes, depends on the (training) data that people provide, but the (test) data depend on the classifier, because people will reveal their information strategically to obtain a more favorable outcome. This setting is not adversarial, but it is also not fully cooperative. Mechanism design provides a framework for making good decisions based on strategically reported information, and it is commonly applied to the design of auctions and matching mechanisms. However, the setting above is unlike these common applications, because in it, preferences tend to be similar across agents, but agents are restricted in what they can report. This creates both new challenges and new opportunities. I will discuss both our theoretical work and our initial experiments. (This is joint work with Hanrui Zhang, Andrew Kephart, Yu Cheng, Anilesh Krishnaswamy, Haoming Li, and David Rein. Papers on this topics can be found at: https://users.cs.duke.edu/~conitzer/bytopic.html#automated%20mechanism%20design)

Bio: Vincent Conitzer is the Kimberly J. Jenkins Distinguished University Professor of New Technologies and Professor of Computer Science, Professor of Economics, and Professor of Philosophy at Duke University, as well as Head of Technical AI Engagement at the Institute for Ethics in AI, and Professor of Computer Science and Philosophy, at the University of Oxford. Much of his work has focused on AI and game theory. More recently, he has started to work on AI and ethics: how should we determine the objectives that AI systems pursue, when these objectives have complex effects on various stakeholders? He received his doctorate from Carnegie Mellon University in 2006. He has received the 2021 ACM/SIGAI Autonomous Agents Research Award, the Social Choice and Welfare Prize, a Presidential Early Career Award for Scientists and Engineers (PECASE), the IJCAI Computers and Thought Award, and an honorable mention for the ACM dissertation award.