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When AI meets Spatio-Temporal Data: Concepts, Methodologies, and Applications
Speaker: Dr. Yuxuan LIANG National University of Singapore Title: "When AI meets Spatio-Temporal Data: Concepts, Methodologies, and Applications" Date: Wednesday, 1 February 2023 Time: 10:00am - 11:00am HKT Zoom link: https://hkust.zoom.us/j/465698645?pwd=aVRaNWs2RHNFcXpnWGlkR05wTTk3UT09 Meeting ID: 465 698 645 Passcode: 20222023 Abstract: With the rapid advances in new-generation information technologies such as the Internet of Things, 5G, and mobile Internet, Spatio-Temporal (ST) data are growing explosively. In contrast to image, text, and voice data, ST data often present unique spatio-temporal characteristics, including spatial distance and hierarchy, as well as temporal closeness, periodicity, and trend. Spatio-Temporal AI is a proprietary AI technology for ST data, where AI meets conventional city-related fields, like transportation, civil engineering, environment, and economy, in the context of urban spaces. This talk first introduces the concept of Spatio-Temporal AI, discussing its general framework and key challenges from the perspective of computer science. Secondly, we classify the applications of spatio-temporal AI into four categories, consisting of modeling ST trajectories, ST grid data, ST graphs, and ST series. We also present representative scenarios in each category. Thirdly, we delineate our recent progress in the methodologies of the above four categories. Finally, we outlook on the future of spatio-temporal AI, suggesting a few research topics that are somehow missing in the community. ******************** Biography: Yuxuan Liang is a Research Fellow at School of Computing, National University of Singapore (NUS). He is currently working on the research, development, and innovation of spatio-temporal data mining and AI, with a broad range of applications in smart cities. Prior to that, he completed his PhD study at NUS. He published over 30 peer-reviewed papers in refereed journals and conferences, such as KDD, WWW, NeurIPS, and TKDE. Those papers have been cited over 1,800 times (Google Scholar H-Index: 20). He was recognized as 1 out of 10 most innovative and impactful PhD students focusing on data science in Singapore by Singapore Data Science Consortium (SDSC).