Keynotes

Keynote Speakers

	Jie Bao Director of Data Management Division of JD iCity
Title: Urban Computing: Building Inteligent Cities with Big Data and AI
Abstract: Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data in cities to tackle urban challenges, e.g. air pollution, energy consumption and traffic congestion. Urban computing connects sensing technologies, data management and AI models, as well as visualization methods, to create win-win-win solutions that improve urban environment, human life quality and city operation systems. This talk presents the vision of urban computing in JD group, introducing the urban big data platform (JD Urban Spatio-Temporal Data Engine, aka JUST, http://just.urban-computing.com) and a general design for intelligent cities. A series of deployed applications with collaboration of JD logistics are also presented in this talk. Please review more information through the website: http://icity.jd.com/.
Bio: Dr. Jie Bao, currently, leads the data management department at JD Intelligent City Business Unit, where he is in charge of the development of JD Urban Spatio-Temporal data engine (aka JUST, http://just.urban-computing.com), as well as all the data-driven products in the business unit. His main research interests include: Urban Computing, Spatio-temporal Data Management/Mining, and Distributed Computing Platforms. He got his Ph.D. degree from the Department of Computer Science and Engineering at University of Minnesota at Twin Cities in 2014. Before joining JD, he was a researcher in MSRA from 2014- 2017. He was a research intern in IBM T.J Watson Lab in 2013, and Microsoft Research, Asia in 2011. He has published over 40 research papers in refereed journals and conferences (e.g., SIGKDD, ICDE, VLDB, SIGMOD, AAAI and SIGSPATIAL).
	Yang Liu Principal Researcher in the AI Department of WeBank, China.
Title: Towards Building Federated Learning Powered Applications for Urban Management
Abstract: In this talk, we discuss basic concepts of federated learning and its applications for computer vision and crowdsourcing tasks in urban management. We will also discuss privacy and efficiency challenges of such systems, and proposal new techniques to address these challenges.
Bio: Dr Yang Liu is a Principal Researcher in the AI Department of WeBank, China. Her research interests include machine learning, federated learning, transfer learning, multi-agent systems, statistical mechanics, and applications of these technologies in the financial industry. She received her PhD from Princeton University in 2012 and her Bachelor's degree from Tsinghua University in 2007. She holds multiple patents. Her research has been published in leading scientific conferences and journals including Nature with over 1000 citations. She co-authored the book "Federated Learning" - the first monograph on the topic of federated learning. Her research work has been recognized with multiple awards, such as CCF Technology Award, Shenzhen Fintech Special Award, AAAI Innovation Award and IJCAI Innovation Award.
	Tony Qin Principal Research Scientist and Director of the reinforcement learning group at DiDi AI Labs
Title: Deep Reinforcement Learning in a Ride-sharing Marketplace
Abstract: With the rising prevalence of smart mobile phones in our daily life, online ride-hailing platforms have emerged as a viable solution to provide more timely and personalized transportation service, led by such companies as DiDi, Uber, and Lyft. These platforms also allow idle vehicle vacancy to be more effectively utilized to meet the growing need of on-demand transportation, by connecting potential mobility requests to eligible drivers. In this talk, we will describe our train of research on ride-hailing marketplace optimization at DiDi, in particular, order dispatching and vehicle repositioning. We will show the development of the spatiotemporal contextual value network and how it is used in order dispatching policy generation and decision-time planning in vehicle repositioning.
Bio: 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 and were selected for the NeurIPS 2018 Best Demo Awards. Tony holds more than 10 US patents in intelligent transportation and E-commerce systems.