Speakers

Ryan Adams is professor of computer science in the Department of Computer Science at Princeton University. He was formerly assistant professor of computer science in the School of Engineering and Applied Sciences at Harvard University, where he led the Intelligent Probabilistic Systems group, which focuses on probabilistic approaches to building such algorithms, working at the interface of computer science, statistics and computational neuroscience. In broad terms, he is interested in understanding the computation that lies beneath intelligence and developing artificial systems that can discover complex structure in data. Adams completed his Ph.D. in physics under David MacKay at the University of Cambridge, where he was a Gates Cambridge Scholar and a member of St. John’s College. His doctoral work won the honorable mention for the Savage Award from the International Society for Bayesian Analysis. Before coming to Harvard, Adams spent two years as a junior research fellow at the University of Toronto as a part of the Canadian Institute for Advanced Research. Adams has won awards for his work at several major international conferences such as the International Conference on Machine Learning, the International Conference on Artificial Intelligence and Statistics, and the Conference on Uncertainty in Artificial Intelligence. Adams is also the recipient of a Defense Advanced Research Projects Agency Young Faculty Award.

Anne Collins is an Assistant Professor in the department of Psychology and a member of the The Helen Wills Neuroscience Institute at UC Berkeley. Previously, she was a post-doctoral research associate at Brown University, in the Laboratory of Neural Computation and Cognition (LNCC), with Prof. Michael Frank. She studied math and science at Ecole Polytechnique (Palaiseau, France) as an undergrad, then got her PhD in Cognitive Science from UPMC (Paris, France), with Prof. Etienne Koechlin, at the laboratory for cognitive neuroscience (ENS, INSERM). Her research focuses on human learning, decision-making and executive functions; computational modeling at multiple levels (cognitive and neuroscience); and behavioral, EEG, drug and genes studies in healthy or patient populations.

Nicolas Heess is a research scientist at Google DeepMind. He has published on topics of reinforcement learning, unsupervised learning, probabilistic models, and inference. His current research focuses on applications of these at the intersection of perception and control with a special interest in structured representations of behavior. Before joining DeepMind he was a postdoctoral researcher at the Gatsby Unit (UCL) working with Yee Whye Teh and David Silver. He did his PhD under the supervision of Chris Williams at the University of Edinburgh and also paid several extended visits to Microsoft Research (Cambridge, UK) where he worked with John Winn.

Leslie Pack Kaelbling is Professor of Computer Science and Engineering at MIT. She has previously held positions at Brown University, the Artificial Intelligence Center of SRI International, and at Teleos Research. Prof. Kaelbling has done substantial research on designing situated agents, mobile robotics, reinforcement learning, and decision-theoretic planning. In 2000, she founded the Journal of Machine Learning Research, a high-quality journal that is both freely available electronically as well as published in archival form; she currently serves as editor-in-chief. She is an NSF Presidential Faculty Fellow, a former member of the AAAI Executive Council, the 1997 recipient of the IJCAI Computers and Thought Award, a trustee of IJCAII and a fellow of the AAAI. She received an A. B. in Philosophy in 1983 and a Ph. D. in Computer Science in 1990, both from Stanford University.

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. His work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business.

Shie Mannor is a professor of Electrical Engineering at the Technion, Israel Institute of Technology. Shie graduated from the Technion with a PhD in Electrical Engineering in 2002. He was a Fulbright postdoctoral scholar at LIDS at MIT from 2002 to 2004. He was at the Department of Electrical and Computer Engineering at McGill University from July 2004 until August 2010, where he held the Canada Research Chair in Machine Learning. Shie has been with the Department of Electrical Engineering at the Technion since 2008 where he is currently a professor. His research interests include machine learning, planning and control, and networks. Shie has published over 70 journal papers and over 130 conference papers and holds 8 patents. He is an associate editor of Operations Research and of Mathematics of Operations Research and an action editor of the Journal of Machine Learning Research (JMLR). His research awards include several best paper awards, the Henri Taub Prize for Academic Excellence, an ERC Starting Grant, an HP Faculty Award and a Horev Fellowship.

Dale Schuurmans is a Professor of Computing Science and Canada Research Chair in Machine Learning at the University of Alberta. He received his PhD in Computer Science from the University of Toronto and MSc and BSc degrees in Computing Science and Mathematics from the University of Alberta. He has previously been an Associate Professor at the University of Waterloo, a Postdoctoral Fellow at the University of Pennsylvania, a Researcher at the NEC Research Institute, and a Research Associate at the National Research Council Canada. Prof. Schuurmans' research interests include machine learning, optimization and search. He is author of over eighty publications in these areas, and has received outstanding paper awards at the International Joint Conference on Artificial Intelligence (IJCAI) and the National Conference on Artificial Intelligence (AAAI).

Emo Todorov obtained his PhD in Cognitive Neuroscience from MIT in 1998. Since then he has worked as Postdoctoral Felow in the Gatsby Computational Neuroscience Unit at UCL, Research Scientist in Biomedical Engineering at USC, Assistant Professor in Cognitive Science at UCSD, and is now Associate Professor in Applied Mathematics and Computer Science & Engineering at UW. He is generally interested in intelligent control of complex systems, in both engineering and biology. His current focus is autonomous robot control through high-performance numerical optimization. He is also the founder of Roboti LLC and developer of the MuJoCo physics simulator.