Nick Rhinehart is a Ph.D. Candidate in The Robotics Institute at Carnegie Mellon University with Kris Kitani. His research focuses on understanding, forecasting, and controlling the behavior of intelligent agents through computer vision and machine learning, and he is particularly interested in systems that learn to reason about the future. He recently worked as a Visiting Researcher at UC Berkeley with Sergey Levine, and in the past, researched at N.E.C. Labs and the Uber Advanced Technology Group. His research on First-Person Forecasting received the Marr Prize (Best Paper) Honorable Mention Award at ICCV 2017. Nick previously co-organized the Workshop on Inverse Reinforcement Learning for Computer Vision at CVPR 2018.

Ilya Kostrikov is a third-year PhD student at New York University supervised by Rob Fergus and Joan Bruna. He has broad research interests. In the past he was working on deep learning for non-Euclidean data such as surfaces. Currently he is working on methods for exploration and imitation learning for games and robotics

Justin Fu a third-year Ph.D. student working with Sergey Levine at UC Berkeley. His research lies at the intersection of machine learning and robotics. He is mainly interested in combining deep learning with reinforcement learning in order to solve complex decision making problems. He is also interested in imitation learning and how we can easily specify objectives for reinforcement learning agents.

Siddharth Reddy is a second-year computer science Ph.D. student at the Berkeley Artificial Intelligence Research Lab co-advised by Sergey Levine and Anca Dragan. He is broadly interested in machine learning, robotics, and cognitive science. His research combines human and machine intelligence to solve sequential decision-making problems that neither can on their own. His past work includes deep reinforcement learning algorithms for shared autonomy and imitation learning, and methods for inferring beliefs about dynamics from behavior

Sergey Levine is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. In his research, he focuses on the intersection between control and machine learning, with the aim of developing algorithms and techniques that can endow machines with the ability to autonomously acquire the skills for executing complex tasks. In particular, he is interested in how learning can be used to acquire complex behavioral skills, in order to endow machines with greater autonomy and intelligence. Sergey Levine has previously organized four NIPS workshops and one ICML workshop, two workshops at RSS, one at ECCV, a AAAI Spring Symposium, and has served as program chair for ICLR and CoRL.

Chelsea Finn is a research scientist at Google Brain and a post-doctoral scholar at UC Berkeley. She will join the faculty in the Department of Computer Science at Stanford University in Fall 2019. She is broadly interested in how learning algorithms can enable machines to acquire greater and more general notions of intelligence, allowing them to autonomously learn a variety of complex sensorimotor skills in real-world settings. This includes learning deep representations for representing complex skills from raw sensory inputs, enabling machines to learn on their own, without human supervision, and allowing systems to build upon what they've learned previously to acquire new capabilities with small amounts of experience. Chelsea has previously co-organized a workshop on Deep Learning for Action and Interaction at NeurIPS 2016 and is co-organizing a workshop on Structure and Priors in Reinforcement Learning at ICLR 2019.

He He is an applied scientist at Amazon Web Services, Palo Alto. Starting Fall 2019, she will be joining NYU CS as an assistant professor. She is broadly interested in machine learning and natural language processing. Her research focuses on building intelligent agents that work in a changing environment and interact with people, with an emphasis on language-related problems. Specific applications include dependency parsing, simultaneous machine interpretation, and goal-oriented dialogue. He previously co-organized the Workshop on Representation Learning for NLP at ACl 2018, as well as the Widening NLP (WiNLP) workshop co-located with ACL 2017.