Professor, University of Alberta, Canada
Csaba Szepesvari (PhD’99) is currently a Professor at the Department of Computing Science of the University of Alberta and a Principal Investigator of the Alberta Innovates Center for Machine Learning, before which he was a senior researcher of the Computer and Automation Research Institute of the Hungarian Academy of sciences and held various industrial positions. The coauthor of a book on nonlinear approximate adaptive controllers and the author of a short book on Reinforcement Learning, he published about 150 journal and conference papers. He is best known for the UCT algorithm, which led to a leap in the performance of planning and search algorithms in many domains, in particular in computer go. He is an Action Editor of the Journal of Machine Learning Research and the Machine Learning Journal. His research interests include reinforcement learning, statistical learning theory and online learning.
Research Fellow, Imperial College London, UK
Marc Deisenroth joined the Department of Computing of Imperial College London as a Research Fellow in Statistical Machine Learning in September 2013. From December 2011 to August 2013 he was a Group Leader at TU Darmstadt (Germany) where he is still adjunct researcher at TU Darmstadt. From February 2010 to December 2011, he was a full-time Research Associate at the University of Washington (Seattle, USA). He completed his PhD at the Karlsruhe Institute of Technology (Germany) in 2009. He conducted his PhD research at the Max Planck Institute for Biological Cybernetics (2006-2007) and at the University of Cambridge (2007-2009). His interest centers around modern Bayesian machine learning and its application to autonomous control and robotic systems. He was Program Chair of the European Workshop on Reinforcement Learning in 2012 and was Workshops Chair of the Robotics: Science & Systems Conference in 2013. He co-authored a book on Policy Search for Robotics (NOW Publishers, 2013).
Post-Doctoral Researcher, University of California at Berkeley, USA
Sergey Levine is a postdoctoral researcher working with Professor Pieter Abbeel at the University of California at Berkeley. He previously completed his PhD with Professor Vladlen Koltun at Stanford University. His research areas include robotics, reinforcement learning and optimal control, machine learning, and computer graphics. His work includes the development of new algorithms for learning motor skills, methods for learning behaviors from human demonstration, and applications in robotics and computer graphics, ranging from robotic manipulation to animation of martial arts and conversational hand gestures.
Pedro A. Ortega
Post-Doctoral Researcher, University of Pennsylvania, USA
Pedro A. Ortega is postdoctoral researcher at the GRASP Robotics Lab, University of Pennsylvania, working with Prof. Daniel D. Lee. His research focuses on the mathematical foundations of artificial intelligence, machine learning and cybernetics. His work includes information-theoretic and statistical mechanical approaches to adaptive control, leading to contributions in bounded rationality models and recasting adaptive control as a causal inference problem. He obtained his PhD in Engineering from the University of Cambridge (Prof. Zoubin Ghahramani), and he has been a post-doctoral fellow at the Department of Engineering in Cambridge (Prof. Simon Godsill), at the Max Planck Institute for Biological Cybernetics/Intelligent Systems (Daniel A. Braun) and at the Hebrew University in Jerusalem (Prof. Naftali Tishby).