The Speakers
Sergey Levine
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.
Dana Kulić
Professor Dana Kulić develops autonomous systems that can operate in concert with humans, using natural and intuitive interaction strategies while learning from user feedback to improve and individualise operation over long-term use. In collaboration with Professor Elizabeth Croft, she pioneered systems to quantify and control safety during HRI based on both robot and human perception. Working with Professor Yoshihiko Nakamura at the University of Tokyo, she developed one of the first systems to implement continuous learning from demonstration. The system was a first step towards robots that can learn from non-experts, as it did not require the demonstrator to segment or scaffold their demonstration.
Her research in rehabilitation technology enables highly accurate, non-invasive, measurement of human movement, which can be deployed in industrial settings for accurate measurement of operator movement. She serves as the Global Innovation Research Visiting Professor at the Tokyo University of Agriculture and Technology, and the August-Wilhelm Scheer Visiting Professor at the Technical University of Munich. Before coming to Monash,
Professor. Kulić established the Adaptive Systems Lab at the University of Waterloo, and collaborated with colleagues to establish Waterloo as one of Canada’s leading research centres in robotics. She is a co-Investigator of the Waterloo Robohub, the largest robotics experimental facility in Canada, and a co-Principal Investigator of the Natural Sciences and Engineering Research Council (NSERC) Canadian Robotics Network, Canada’s only federally funded network in robotics. She has led a number of large research projects and collaborations with industry and user groups, including a strategic project grant in collaborative assembly and multiple grants developing automation for rehabilitation.
Guy Hoffman
Dr. Guy Hoffman is Assistant Professor and the Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Prior to that he was Assistant Professor at IDC Herzliya and co-director of the IDC Media Innovation Lab.
Hoffman holds a Ph.D from MIT in the field of human-robot interaction. He heads the Human-Robot Collaboration and Companionship (HRC2) group, studying the algorithms, interaction schema, and designs enabling close interactions between people and personal robots in the workplace and at home.
Among others, Hoffman developed the world’s first human-robot joint theater performance, and the first real-time improvising human-robot Jazz duet. His research papers won several top academic awards, including Best Paper awards at HRI and robotics conferences in 2004, 2006, 2008, 2010, 2013, and 2015. In both 2010 and 2012, he was selected as one of Israel’s most promising researchers under forty. His TEDx talk is one of the most viewed online talks on robotics, watched more than 2.9 million times. Hoffman received his M.Sc. in Computer Science from Tel Aviv University as part of the Adi Lautman interdisciplinary excellence scholarship program
Dorsa Sadigh
Dorsa Sadigh is an assistant professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the intersection of robotics, learning, and control theory. Specifically, she is interested in developing algorithms for safe and adaptive human-robot interaction. Dorsa has received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and has received her bachelor’s degree in EECS from UC Berkeley in 2012. She is awarded the NSF CAREER award, the AFOSR Young Investigator Program Award, the IEEE TCCPS early career award, the Google Faculty Award, and the Amazon Faculty Research Award.
Michael L. Littman
Michael L. Littman is The Royce Family Professor of Teaching Excellence in Computer Science at Brown University, studying machine learning and decision making under uncertainty. He has earned multiple university-level awards for teaching and his research on reinforcement learning, probabilistic planning, and automated crossword-puzzle solving has been recognized with three best-paper awards and two influential paper awards. Littman is co-director of Brown's Humanity Centered Robotics Initiative and a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery. He is also a Fellow of the American Association for the Advancement of Science Leshner Leadership Institute for Public Engagement with Science, focusing on Artificial Intelligence.