Challenges in Analyzing Two-sided Markets and Its Application on Ridesourcing Platforms

When and Where

Friday, December 06, 2019 3:30 pm to 4:30 pm
Room 409
Stewart Building
149 College Street, Toronto, ON M5T 1P5


Hongtu Zhu, University of North Carolina


In this talk, we will introduce a general analytical framework for large scale data obtained from two-sided markets, especially ride-sourcing 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 platform, 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, we receive the Daniel Wagner Prize for Excellent in Operations Research Practice in 2019. All the research accomplishments presented in this talk are joint work by a group of researchers at Didi Chuxing and our international collaborators.

Please register for the event.

About Hongtu Zhu

Dr. Zhu joined DiDi in 2018 from his position of Endowed Bao-Shan Jing Professorship in Diagnostic Imaging and 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 got 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 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 Daniel Wagner Prize for Excellent in Operations Research Practice with his colleagues at DiDi in 2020. He has published more than 250 papers in top journals including Nature, Nature Genetics, Nature Neuroscience, PNAS, AOS, and JRSSB, as well as 40 conference papers in top conferences including NIPS, AAAI, KDD, ICDM,MICCAI, and IPMI. He serves as a chair or area chair of top international conferences including - Information Processing in Medical Imaging, as well as an editorial board member of premier international journals including Statistica Sinica, JRSSB,  Annals of Statistics, and Journal of American Statistical Association.


149 College Street, Toronto, ON M5T 1P5