Stats Dept. presents "Challenges in analyzing two-sided markets and its application on ridesourcing platforms"

Challenges in analyzing two-sided markets and its application on ridesourcing platforms
Tuesday, April 14, 2020 - 4:30pm
Hongtu Zhu (UNC Chapel Hill)
Abstract / Description: 

In this talk, we will introduce a general analytical framework for large-scale data obtained from two-sided markets, especially ridesourcing platforms like DiDi. This framework integrates classical methods including Experiment Design, Causal Inference and Reinforcement Learning, with modern machine learning methods, such as Graph Convolutional Models, Deep Learning, Transfer Learning and Generative Adversarial Network. We aim to develop fast and efficient approaches to address five major challenges for ride-sharing platforms, ranging from demand-supply forecasting, demand-supply diagnosis, MDP-based policy optimization, A-B testing, to business operation simulation. Each challenge requires substantial methodological developments and inspires many researchers from both industry and academia to participate in this endeavor. Based on our preliminary results for the policy optimization challenge, in 2019 we received the INFORMS Daniel Wagner Prize for Excellence in Operations Research Practice. All the research accomplishments presented in this talk are joint work by a group of researchers at Didi Chuxing and our international collaborators.


Dr. Zhu is DiDi Fellow and Chief Scientist of Statistics since joining DiDi in 2018 from his position of Endowed Bao-Shan Jing Professorship in Diagnostic Imaging. He is also a tenured professor of biostatistics at MD Anderson Cancer Center and a tenured professor of biostatistics at University of North Carolina at Chapel Hill. Dr. Zhu is leading DiDi's statistical cognitive team with AI scientists and engineers on the development of innovative solutions for the world's large ride-hailing platform. Dr. Zhu earned his Ph.D. degree in statistics from the Chinese University of Hong Kong in 2000. 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 the American Statistical Association and the Institute of Mathematical Statistics since 2011. He received an established investigator award from the Cancer Prevention & Research Institute of Texas in 2016 and received the INFORMS Daniel Wagner Prize for Excellence in Operations Research Practice with his colleagues at DiDi in 2019. He has published more than 250 papers in top journals including Nature, Nature Genetics, Nature Neuroscience, PNAS, AOS, and JRSS-B, as well as 40 conference papers including NIPS, AAAI, KDD, ICDM, MICCAI, and IPMI. He has served/is serving as editorial board member of premier international journals including Statistica Sinica, JRSS-B, Annals of Statistics, and the Journal of the American Statistical Association.