Organizers

Ben Eysenbach* is a Student Researcher at Google Brain and a PhD student at CMU. He is advised by Sergey Levine and Ruslan Salakhutdinov. He is broadly interested in designing algorithms that enable robots to learn more safety and with less human supervision. Before his PhD, Ben was a Resident doing robotics research at Google Brain. He received a BS in Math from MIT, where he also did research in computer vision with Antonio Torralba and Carl Vondrick. He has presented work on ICML and ICLR, and co-organized the first Exploration in Reinforcement Learning workshop at ICML 2018. [Website] [Publications]

Surya Bhupatiraju* is a research engineer at DeepMind, where he works broadly on better, more efficient reinforcement learning algorithms. Previously, he was a Resident at Google Brain, where we worked with George Tucker, Shane Gu, and Sergey Levine. He received a BS in computer science from MIT, where he spent time working on reinforcement learning with Josh Tenenbaum and Tejas Kulkarni and spent time working at MSR on neural program synthesis with Pushmeet Kohli, Abdelrahman Mohamed and Rishabh Singh. He has presented work at ICML and ICLR, and co-organized the first Exploration in Reinforcement Learning workshop at ICML 2018. [Google Scholar]

Shixiang (Shane) Gu is a Research Scientist at Google Brain, where he works on problems in deep learning, reinforcement learning, robotics, and probabilistic machine learning. His recent research focuses on sample-efficient RL methods that could scale to solve difficult continuous control problems in the real-world, which have been covered by Google Research Blogpost and MIT Technology Review. Shane completed PhD in Machine Learning at the University of Cambridge and the Max Planck Institute for Intelligent Systems in Tübingen, where he was co-supervised by Richard E. Turner, Zoubin Ghahramani, and Bernhard Schölkopf. He holds B.ASc. in Engineering Science from the University of Toronto, where he did his thesis with Geoffrey Hinton in distributed training of neural networks using evolutionary algorithms. [Website] [Google Scholar]

Harri Edwards is a researcher at OpenAI and a PhD student at the University of Edinburgh advised by Amos Storkey. His focus is on learning with minimal or no supervision in various contexts, most recently the emergence of complex behaviors in agents through the development of intrinsic motivators such as curiosity. [Google Scholar]

Martha White is an assistant professor of Computing Science at the University of Alberta. Previously she was an assistant professor in the School of Informatics and Computing at Indiana University in Bloomington, and received her PhD in Computing Science from the University of Alberta in 2015. Her primary research goal is to develop algorithms for autonomous agents learning from streams of data. She focuses on developing practical algorithms for reinforcement learning and representation learning. [Website] [Publications]

Pierre-Yves Oudeyer is a research director at Inria and head of the FLOWERS lab at Inria and Ensta-ParisTech since 2008. Before, he has been a permanent researcher at Sony Computer Science Laboratory for 8 years (1999-2007). He studies developmental autonomous learning and the self-organization of behavioural and cognitive structures, at the frontiers of AI, machine learning, neuroscience, developmental psychology and educational technologies. In particular, he studies exploration in large open-ended spaces, with a focus on autonomous goal setting, intrinsically motivated learning, and how this can automate curriculum learning. With his team, he pioneered curiosity-driven learning algorithms working in real world robots (used in Sony Aibo robots), and showed how the same algorithms can be used to personalize sequences of learning activities in educational technologies deployed at large in schools. He developed theoretical frameworks to understand better human curiosity, and contributed to build an international interdisciplinary research community on human curiosity. He also studied how machines and humans can invent, learn and evolve speech communication systems. He is laureate of the Inria-National Academy of Science young researcher prize in computer sciences, of an ERC Starting Grant, and of the Lifetime Achievement Award of the Evolutionary Linguistics association. Beyond academic publications and several books, he is co-author of 11 international patents. His team created the first open-source 3D printed humanoid robot for reproducible science and education (Poppy project), and now widely used in schools and artistic projects, as well as 2 startup companies. He is also working actively for the diffusion of science towards the general public, through the writing of popular science articles and participation to radio and TV programs as well as science exhibitions. [Website] [Google Scholar]

Kenneth O. Stanley is Charles Millican Professor of Computer Science at the University of Central Florida and director there of the Evolutionary Complexity Research Group. He was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he is now also a senior research science manager and head of Core AI research. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, and open-ended evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, and Galactic Arms Race. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002 - 2012 from the International Society for Artificial Life. He is a coauthor of the popular science book, "Why Greatness Cannot Be Planned: The Myth of the Objective" (published by Springer), and has spoken widely on its subject. [Website] [Google Scholar]

Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University where she leads the AI for Human Impact group. Her work focuses on reinforcement learning in high stakes scenarios-- how can an agent learn from experience to make good decisions when experience is costly or risky, such as in educational software, healthcare decision making, robotics or people-facing applications. She was previously on faculty at Carnegie Mellon University. She is the recipient of a multiple early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research) and her group has received several best research paper nominations (CHI, EDMx2) and awards (UAI, RLDM). Recent experience organizing meetings includes: Co-PC for RLDM 2017 and a UAI 2018 Workshop. [Website] [Publications]