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Poster and Coffee Break 1
Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova

Sat Dec 14 09:30 AM -- 10:30 AM (PST) @
in The Optimization Foundations of Reinforcement Learning »

Author Information

Aaron Sidford (Stanford)
Aditya Mahajan (McGill University)
Alejandro Ribeiro (University of Pennsylvania)
Alex Lewandowski (University of Alberta)
Ali H Sayed (Ecole Polytechnique Fédérale de Lausanne)

A. H. Sayed is Dean of Engineering at EPFL, Switzerland, and principal investigator of the Adaptive Systems Laboratory. He has served as distinguished professor and chairman of electrical engineering at UCLA. An author/co-author of over 530 scholarly publications and six books, his research involves several areas including adaptation and learning theories, data and network sciences, statistical inference, and distributed optimization. He is recognized as a Highly Cited Researcher by Thomson Reuters and Clarivate Analytics, and is a member of the US National Academy of Engineering. He is serving as President of the IEEE Signal Processing Society.

Ambuj Tewari (University of Michigan)
Angelika Steger (ETH Zurich)
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.

Asier Mujika (ETH Zurich)
Hilbert J Kappen (Radboud University)
Bolei Zhou (CUHK)
Byron Boots (Georgia Tech / Google Brain)
Chelsea Finn (Stanford University)
Chen-Yu Wei (University of Southern California)
Chi Jin (UC Berkeley)
Ching-An Cheng (Georgia Tech)
Christina Yu (Cornell University)
Clement Gehring (Massachusetts Institute of Technology)
Craig Boutilier (Google)
Dahua Lin (The Chinese University of Hong Kong)
Daniel McNamee (University College London)
Daniel Russo (Columbia University)
David Brandfonbrener (New York University)
Denny Zhou (Google)
Devesh Jha (MERL)
Diego Romeres (Mitsubishi Electric Research Laboratories)

I am a Principal Research Scientist at MERL, mainly working in machine learning applied to robotics. Main research areas are robotic manipulation, probabilistic models, reinforcement learning.

Doina Precup (McGill University / Mila / DeepMind Montreal)
Dominik Thalmeier (Radboud University)
Eduard Gorbunov (Moscow Institute of Physics and Technology)
Elad Hazan (Princeton University)
Elena Smirnova (Criteo)
Elvis Dohmatob (Criteo)
Emma Brunskill (Stanford University)
Enrique Munoz de Cote (Prowler.io)
Ethan Waldie (University of Toronto & Palantir Technologies)
Florian Meier (ETH Zurich)
Florian Schaefer (Caltech)
Ge Liu (MIT)
Gergely Neu (Universitat Pompeu Fabra)
Haim Kaplan (TAU, GOOGLE)
Hao Sun (CUHK)
Hengshuai Yao (Huawei Technologies)

I studied reinforcement learning at Reinforcement Learning and Artificial Intelligence (RLAI) lab from 2008 to 2014 in a Ph.D program at Department of Computing Science, University of Alberta. My thesis is on model-based reinforcement learning with linear function approximation. During my Ph.D studies, I worked with Csaba Szepesvari, Rich Sutton, Dale Schuurmans, and Davood Rafiei on reinforcement learning theory, algorithms and web applications. I joined NCSoft game studio in San Francisco in 2016 working on reinforcement learning in games. I moved back to Canada and joined Huawei in 2017.

Jalaj Bhandari (Columbia University)

I am a PhD student in the IEOR Department at Columbia University, currently working on design and analysis of Reinforcement Learning (RL) algorithms with Prof. Garud Iyengar and Prof. Daniel Russo. In the past, I have done research work with Prof. John P. Cunningham on Bayesian Machine learning, specifically in designing computationally efficient Markov Chain Monte Carlo (MCMC) algorithms for posterior sampling. I broadly interested in working at the intersection of Optimization and Machine Learning.

James A Preiss (University of Southern California)
Jayakumar Subramanian (McGill University)
Jiajin Li (The Chinese University of Hong Kong)
Jieping Ye (University of Michigan)
Jimmy Smith (Stanford)
Joan Bas Serrano (Universitat Pompeu Fabra)
Joan Bruna (NYU)
John Langford (Microsoft Research New York)
Jonathan Lee (UC Berkeley)
Jose A. Arjona-Medina (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC))
Karan Singh (Princeton University)
Yuping Luo (Princeton University)
Zafarali Ahmed (McGill University)
Zaiwei Chen (Georgia Institute of Technology)
Zhaoran Wang (Northwestern University)
Zhizhong Li (The Chinese University of Hong Kong)
Zhuoran Yang (Princeton University)
Ziping Xu (University of Michigan)

My name is Ziping Xu. I am a fifth-year Ph.D. student in Statistics at the University of Michigan. My research interests are on sample efficient reinforcement learning and transfer learning, multitask learning. I am looking for research-orientated full-time job starting Fall 2023

Ziyang Tang (UT Austin)
Yi Mao (Microsoft)
David Brandfonbrener (New York University)
Shirli Di-Castro (Technion)
Riashat Islam (MILA/McGill)
Zuyue Fu (Northwestern University)
Abhishek Naik (University of Alberta)
Saurabh Kumar (Stanford University)
Benjamin Petit (Stanford University)
Angeliki Kamoutsi (ETH Zurich)
Simone Totaro (Universitat Pompeu Fabra)
Arvind Raghunathan (MERL)

Arvind's research focuses on algorithms for optimization of large-scale nonlinear and mixed integer nonlinear programs with applications in power grid, transportation systems and model-based control of processes. He previously worked at the United Technologies Research Center for 7 years developing optimization algorithms for aerospace, elevator, energy systems and security businesses.

Rui Wu (Google)
Donghwan Lee (KAIST)
Dongsheng Ding (University of Southern California)
Alec Koppel (U.S. Army Research Laboratory)
Hao Sun (Peng Cheng Laboratory)
Christian Tjandraatmadja (Google)
Mahdi Karami (University of Alberta)
Jincheng Mei (University of Alberta)
Chenjun Xiao (University of Alberta)
Junfeng Wen (University of Alberta)
Zichen Zhang (University of Alberta)
Ross Goroshin (Google Brain)
Mohammad Pezeshki (Mila)
Jiaqi Zhai (Google)
Philip Amortila (University of Illinois at Urbana-Champaign)
Shuo Huang (Georgia Institute of Technology)
Mariya Vasileva (University of Illinois at Urbana-Champaign)
El houcine Bergou (KAUST-INRA)
Adel Ahmadyan (Google)
Haoran Sun (Georgia Institute of Technology)
Sheng Zhang (Georgia Institute of Technology)

I am currently a final-year PhD student in Machine Learning Program at Georgia Tech. I am fortunate to be advised by Prof. Justin Romberg and Prof. Ashwin Pananjady. Before coming to Georgia Tech, I graduated with an MS in Applied Mathematics from Columbia University and a BS in Mathematics and Applied Mathematics from Wuhan University. My research mainly focuses on reinforcement learning (RL) and distributed optimization. The overall goal of my research is to enhance the theoretical understanding of RL, and to design efficient algorithms for large-scale problems arise from machine-learning and decision-making applications. Specifically, I have studied the statistical efficiency (sample complexity) of RL algorithms, and designed an accelerated method for distributed stochastic optimization problems. In addition, during my previous research internships, I have developed an AI program for a popular Chinese poker game using self-play deep RL, proposed a matrix factorization framework for high-dimensional demand forecasting with missing values, and designed deep convolutional neural networks for automated image segmentation of neurons.

Lukas Gruber (Johannes Kepler University)
Yuanhao Wang (Tsinghua University)
Tetiana Parshakova (Stanford U)

I am now pursuing a Ph.D. in Computational & Mathematical Engineering at Stanford University. I received a Bachelors in the Department of Industrial Design at KAIST. Then, I obtained a Master’s degree at KAIST School of Electrical Engineering.

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