报告题目：Spatial, Spatio-temporal, Graph and Sequential Data Analytics with Deep Learning
报告人：龙程，南洋理工大学
报告时间：12月16日（周一）11:20-12:10
报告地点：N3-332
摘要:
Spatial, spatio-temporal, graph and sequential data is ubiquitous and has many applications.For example, in the emerging application of urban computing, it involves (1) spatial data such as points-of-interest, road networks, and point clouds; (2) spatio-temporal data such asvehicles’ trajectories, users’ mobility records, and road traffics; (3) graph data such as online social networks and bipartite graphs between taxis and passengers; and (4) sequential datasuch as readings of traffic, weather and pollution sensors. In this talk, I would present some of my recent work on using deep learning based methods for various problems involvingspatial, spatio-temporal, graph and sequential data, e.g., reinforcement learning for adaptive batch formation, trajectory group representation learning and prediction, mobility and time prediction, and auto map generation, etc.

报告人简介:
LONG Cheng is currently an Assistant Professor at the School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU). From 2016 to 2018, he worked as a lecturer (Asst Professor) at Queen’s University Belfast, UK. He got the PhD degree from the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology (HKUST) in 2015. His research interests are broadly in data management, data mining, and machine learning on spatial, spatio-temporal, graph and sequential data. His research has been recognized with one “Best Research Award” provided by ACM-Hong Kong, one “Fulbright-RGC Research Award” provided by Research Grant Council (Hong Kong), two “PG Paper Contest Awards” provided by IEEE-HK, and one “Overseas Research Award” provided by HKUST. He has served as a Program Committee member/referee for several top data management and data mining conferences/journals (TODS, VLDBJ, TKDE, ICDM, CIKM, etc.). He is member of ACM and IEEE.