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Poster Session 1
Kyle H Ambert · Brandon Araki · Xiya Cao · Sungjoon Choi · Hao(Jackson) Cui · Jonas Degrave · Yaqi Duan · Mattie Fellows · Carlos Florensa · Karan Goel · Aditya Gopalan · Ming-Xu Huang · Jonathan Hunt · Cyril Ibrahim · Brian Ichter · Maximilian Igl · Zheng Tracy Ke · Igor Kiselev · Anuj Mahajan · Arash Mehrjou · Karl Pertsch · Alexandre Piche · Nicholas Rhinehart · Thomas Ringstrom · Reazul Hasan Russel · Oleh Rybkin · Ion Stoica · Sharad Vikram · Angelina Wang · Ting-Han Wei · Abigail H Wen · I-Chen Wu · Zhengwei Wu · Linhai Xie · Dinghan Shen

Author Information

Kyle H Ambert (Intel)

Kyle is Senior Data scientist at Intel Nervana, where he uses machine learning and deep learning methods to solve real-world analytical problems. He has a B.A. in Biological Psychology from Wheaton College, and a Ph.D. in Bioinformatics from Oregon Health & Science University, where his research focused on scalable machine learning-based methods for unstructured data curation and the application of artificial intelligence in the Neurosciences. At Intel Nervana, his team creates deep learning solution prototypes and researches optimization strategies for deep learning networks for text analytics, natural language processing, and image recognition.

Brandon Araki (Massachusetts Institute of Technology)
Xiya Cao (Columbia University)
Sungjoon Choi (Disney Research)
Hao(Jackson) Cui (Tufts University)
Jonas Degrave (Deepmind)
Yaqi Duan (Princeton University)
Mattie Fellows (University of Oxford)
Carlos Florensa (UC Berkeley)
Karan Goel (Stanford University)
Aditya Gopalan (Indian Institute of Science)
Ming-Xu Huang (National Chiao Tung University)
Jonathan Hunt (DeepMind)
Cyril Ibrahim (Element AI)
Brian Ichter (Google Brain)
Maximilian Igl (University of Oxford)
Zheng Tracy Ke (Harvard University)
Igor Kiselev (University of Waterloo)
Anuj Mahajan (Phd student, University of Oxford)
Arash Mehrjou (Max Planck Institute)
Karl Pertsch (University of Southern California)
Alexandre Piche (Mila)
Nicholas Rhinehart (Carnegie Mellon University)

Nick Rhinehart is a Postdoctoral Scholar in the Electrical Engineering and Computer Science Department at the University of California, Berkeley, where he works with Sergey Levine. His work focuses on fundamental and applied research in machine learning and computer vision for behavioral forecasting and control in complex environments, with an emphasis on imitation learning, reinforcement learning, and deep learning methods. Applications of his work include autonomous vehicles and first-person video. He received a Ph.D. in Robotics from Carnegie Mellon University with Kris Kitani, and B.S. and B.A. degrees in Engineering and Computer Science from Swarthmore College. Nick's work has been honored with a Best Paper Award at the ICML 2019 Workshop on AI for Autonomous Driving and a Best Paper Honorable Mention Award at ICCV 2017. His work has been published at a variety of top-tier venues in machine learning, computer vision, and robotics, including AAMAS, CoRL, CVPR, ECCV, ICCV, ICLR, ICML, ICRA, NeurIPS, and PAMI. Nick co-organized the workshop on Machine Learning in Autonomous Driving at NeurIPS 2019, the workshop on Imitation, Intent, and Interaction at ICML 2019, and the Tutorial on Inverse RL for Computer Vision at CVPR 2018.

Thomas Ringstrom (University of Minnesota)
Reazul Hasan Russel (University of New Hampshire)

I'm a PhD student at the computer science department at University of New Hampshire. I am interested about applying Reinforcement Learning into real world problems with safety and robustness guarantees.

Oleh Rybkin (University of Pennsylvania)

I am a Ph.D. student in the GRASP laboratory at the University of Pennsylvania, where I work on computer vision and deep learning with Kostas Daniilidis. Previously, I received my bachelor's degree from Czech Technical University in Prague, where I was advised by Tomas Pajdla. I have spent two summers at INRIA and TiTech, with Josef Sivic and Akihiko Torii respectively. I am working in artificial intelligence, computer vision, and robotics. More specifically, my main interest is machine understanding of intuitive physics for real-world robotic manipulation. My latest work has been on motion understanding via video prediction. During my bachelor's, I also worked on camera geometry for structure from motion.

Ion Stoica (UC Berkeley)
Sharad Vikram (UCSD)
Angelina Wang (UC Berkeley)
Ting-Han Wei (National Chiao Tung University)
Abigail H Wen (Intel Corporation)

Abigail Hing Wen is legal lead for Intel Capital’s artificial intelligence investments and strategic transactions focusing on emerging AI technologies and the greater AI ecosystem. She partners closely with investors and has worked with more than a hundred Silicon Valley startups over their life cycle, from incorporation to IPO or acquisition. Exemplary transactions include Intel’s $4.1B investment in ASML and $740M in Cloudera. She serves as Intel’s board observer for Two Bit Circus, a VR entertainment company based in LA. Other projects include training of Intel Capital’s board directors, the $125M Diversity Fund for underrepresented tech entrepreneurs and Intel’s Ed Tech accelerator and iLabs. Prior to joining Intel in 2012, Abigail advised clients on Wall Street and in DC with the corporate group of Sullivan & Cromwell LLP, clerked on the US Court of Appeals for the DC Circuit and worked on tech and innovation policy for the Senate Judiciary Committee. She holds a JD from Columbia and BA in government/international relations from Harvard. She has written a novel on AI exploring ethical questions and technology and speaks on AI and investing at conferences.

I-Chen Wu (National Chiao Tung University)

I-Chen Wu is with the Department of Computer Science, at National Chiao Tung University. He received his B.S. in Electronic Engineering from National Taiwan University (NTU), M.S. in Computer Science from NTU, and Ph.D. in Computer Science from Carnegie-Mellon University, in 1982, 1984 and 1993, respectively. He serves in the editorial board of the IEEE Transactions on Computational Intelligence and AI in Games and ICGA Journal. He also serves as the vice president of International Computer Games Association, and served as the president of the Taiwanese Association for Artificial Intelligence in 2015-2016. His research interests include computer games, deep learning, and reinforcement learning. Dr. Wu introduced the new game, Connect6, a kind of six-in-a-row game. Since then, Connect6 has become a tournament item in Computer Olympiad. He led a team developing various game playing programs, winning over 50 gold medals in international tournaments, including Computer Olympiad. He wrote over 100 papers, and served as chairs and committee in over 30 academic conferences and organizations, including the conference chair of IEEE CIG conference 2015.

Zhengwei Wu (Baylor College of Medicine, Rice University)
Linhai Xie (University of Oxford)
Dinghan Shen (Duke University)

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