WELCOME TO RL Virtual Seminar
Our RL Virtual Seminar is the perfect place for your reinforcement learning and research.
einforcement Learning (RL) learns how the agents should take actions when interacting with the environment to obtain the highest reward. Due to many successful applications in robotics, games, precision health, e-commerce and ride-sharing industries, Reinforcement Learning (RL) has gained great popularity among various scientific fields.
The goal of this working group is to join forces of researchers from different academic fields and industry, to explore cutting edge algorithms, applications and theory, including and not limited to deep RL, model-based RL, multi-agent RL, inverse RL and policy evaluation methods. Through the high-quality speech from top-ranked speakers in the area of reinforcement learning, our attendees are able to learn about the advanced methods for application of reinforcement learning.
Our Core Values
We aim to join together researchers from all over the world, invoke discussion and learn remotely.
Our Organizers
Rui Song is faculty member of the Department of Statistics at North Carolina State University. Her current research interests include Machine Learning, Causal Inference, Precision Health, Financial Econometrics. Her research has been continuously supported as sole principle investigator by National Science Foundation (NSF). She received the prestigious NSF Faculty Early Career Development (CAREER) Award in 2016. She has published over 70 papers in top tier journals of statistics and ML conferences including Annals of Statistics, Biometrika, Journal of the American Statistical Association and Journal of the Royal Statistical Society: Series B., KDD, ICML and AAAI.
Hongtu Zhu is a tenured professor of biostatistics at University of North Carolina at Chapel Hill and DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing. He was Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center. He chaired Departments of Statistical Cognitive Team and Feature Engineering with AI scientists and engineers on the development of innovative solutions for the world’s large ride-hailing platform at DiDi Chuxing. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He has been an elected Fellow of American Statistical Association and Institute of Mathematical Statistics since 2011. He received an established investigator award from Cancer Prevention Research Institute of Texas in 2016 and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He has published more than 260 papers in top journals including Nature, Nature Genetics, PNAS, AOS, and JRSSB, as well as 40 conference papers in top conferences including NeurIPS, AAAI, KDD, ICDM, MICCAI, and IPMI. He has served/is serving an editorial board member of premier international journals including Statistica Sinica, JRSSB, Annals of Statistics, and Journal of American Statistical Association.
Tony Qin is Principal Research Scientist and Director of the reinforcement learning group at DiDi AI Labs, working on core problems in ridesharing marketplace optimization. Prior to DiDi, he was a research scientist in supply chain and inventory optimization at Walmart Global E-commerce. Tony received his Ph.D. in Operations Research from Columbia University. His research interests span optimization and machine learning, with a particular focus in reinforcement learning and its applications in operational optimization, digital marketing, and smart transportation. He has published in top-tier conferences and journals in machine learning and optimization and served as Program Committee of NeurIPS, ICML, AAAI, IJCAI, KDD, and a referee of top journals including PAMI and JMLR. He and his team received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He is a co-organizer of the 2020 Virtual Conference on Reinforcement Learning for Real Life and KDD Cup 2020 RL Track. He co-organized tutorials on reinforcement learning for transportation at AAAI, KDD, IJCAI, and ICAPS in 2019.
Jieping Ye is a Professor at the University of Michigan, Ann Arbor. He was the VP of the DiDi Chuxing and director of the AI Labs. He is currently the VP of Beike (Beike, also called ke.com, is the leading integrated online and offline platform for housing transactions and services in China.). He is an internationally recognized expert in machine learning, data mining, artificial intelligence, and big data analytics. He is an elected Fellow of IEEE. His research interests include data mining and machine learning with applications in transportation and biomedicine. He has served as a Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, AAAI, ICDM, and SDM. He serves as an Associate Editor of Data Mining and Knowledge Discovery and IEEE Transactions on Knowledge and Data Engineering and served as an AE of IEEE PAMI. He won the NSF CAREER Award in 2010 and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.
Michael R. Kosorok, Ph.D., the W.R. Kenan, Jr. Distinguished Professor of Biostatistics and Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill, received his PhD in Biostatistics from the University of Washington in 1991. He is an internationally known biostatistician and a prominent expert in data science, machine learning and precision medicine. He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Sciences. He has published over 170 peer-reviewed articles, written a major text on the theoretical foundations of empirical processes and semiparametric inferences (Kosorok, 2008, Springer), and co-edited (with Erica E.M. Moodie, 2016, ASA-SIAM) a research monograph on dynamic treatment regimens and precision medicine.