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Poster Presentations
Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange

Sat Dec 14 03:15 PM -- 04:15 PM (PST) @

Author Information

Rahul Mehta (Palantir Technologies)
Andrew Lampinen (Stanford University)
Binghong Chen (Georgia Institute of Technology)
Sergio Pascual-Diaz (PROWLER.io)
Jordi Grau-Moya (PROWLER.io)
Aldo Faisal (Imperial College London)
Jonathan Tompson (Google Brain)
Yiren Lu (Google)
Khimya Khetarpal (Mila- McGill University)
Martin Klissarov (McGill University)
Pierre-Luc Bacon (Stanford University)
Doina Precup (McGill University / Mila / DeepMind Montreal)
Thanard Kurutach (University of California Berkeley)
Aviv Tamar (UC Berkeley)
Pieter Abbeel (UC Berkeley & covariant.ai)

Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter's work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.

Jinke He (Delft University of Technology)
Maximilian Igl (University of Oxford)
Shimon Whiteson (University of Oxford)
Wendelin Boehmer (University of Oxford)
Raphaël Marinier (Google)
Olivier Pietquin (Google Research Brain Team)
Karol Hausman (Google Brain)
Sergey Levine (UC Berkeley)
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 applications in other decision-making domains. 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

Chelsea Finn (Stanford)
Tianhe Yu (Stanford University)
Lisa Lee (Carnegie Mellon University)
Benjamin Eysenbach (Carnegie Mellon University)
Benjamin Eysenbach

Assistant professor at Princeton working on self-supervised reinforcement learning (scaling, algorithms, theory, and applications).

Emilio Parisotto (Carnegie Mellon University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
Ruslan Salakhutdinov (Carnegie Mellon University)
Hongyu Ren (Stanford University)
Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

Deepak Pathak (UC Berkeley)


Christopher Lu (UC Berkeley and Covariant.ai)
Trevor Darrell (UC Berkeley)
Alexei Efros (UC Berkeley)
Phillip Isola (Massachusetts Institute of Technology)
Feng Liu (University of Technology Sydney)
Bo Han (RIKEN)
Gang Niu (RIKEN)
Gang Niu

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.

Masashi Sugiyama (RIKEN / University of Tokyo)
Saurabh Kumar (Stanford University)
Janith Petangoda (Imperial College London)
Johan Ferret (Google Brain)
James McClelland (Stanford University and DeepMind)
Kara Liu (University of California, Berkeley)
Animesh Garg (University of Toronto, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

Robert Lange (Einstein Center for Neurosciences)

I have finished my undergraduate studies in economics at the University of Cologne. During that time I have worked as a student research assistant for Prof. Alex Ludwig (Goethe University Frankfurt) and Prof. Helge Braun (University of Cologne). The projects mainly focused on public policy evaluation and the intersection of retirement and unemployment insurance systems. I developed a fascination for data wrangling and the computational aspects of Economics. Since September I am part of the 2017 cohort of the Data Science Master's Program at the Barcelona Graduate School of Economics. I am fully convinced that the intersection between behavioral sciences and statistical learning is crucial in order to improve almost every aspect of our lives. Therefore, I am looking forward to pursuing a second master's degree in the are of cognitive sciences and artificial intelligence coming next fall. At the moment my interest focuses on computational statistics and machine learning, interdisciplinary applications such as cognitive sciences, biometrics and the philosophy of risk.

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