MSU CSE Colloquium Series 2018-2019: Dr. Yanjie Fu


Collective Representation Learning in Spatial and Temporal Data Environments: Techniques and Applications

Dr. Yanjie Fu
Department of Computer Science
Missouri University of Science and Technology

Friday, Nov 30, 2018
11 AM - 12 PM
EB 3105

Abstract:
The pervasiveness of mobile, sensing, and IoT technologies have accumulated large-scale spatial temporal behaviorial data of individual users and systems in real time and at different locations from mobile devices and App services. Such socio-spatio-temporal data have unprecedented and unique complexity. For instance, they are mostly spatially-autocorrelated, temporally-dependent, dynamically-networked, cross-domain, and semantically-rich. As a result, it is difficult to make sense of spatiotemporal data. In this talk, we first introduce why integrating representation learning with spatiotemporal contexts can help. We then focus on (1) spatial representation learning; (2) spatiotemporal representation learning; (3) their applications to automated region profiling for urban planning and driving behavior analysis for transportation safety. Finally, we conclude the talk and present our future work.

Biography:
Dr. Yanjie Fu is an assistant professor in the Department of Computer Science at the Missouri University of Science and Technology (University of Missouri-Rolla), where he has been since 2016. He received his Ph.D. degree from Rutgers, the State University of New Jersey in 2016, the B.E. degree from University of Science and Technology of China in 2008, and the M.E. degree from Chinese Academy of Sciences in 2011. He has research experience in industry research labs, such as Microsoft Research Asia and IBM Thomas J. Watson Research Center. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, ACM TKDD, IEEE TMC, ACM TIST, ACM SIGKDD, AAAI, IJCAI. Dr. Fu's general interests are data mining and big data analytics, especially (1) how analytical approaches alleviate information overload, heterogeneity, and asymmetry and (2) what role modeling regulations play in exploring the correlations among big data. His recent research focuses on applying spatiotemporal social data mining, deep learning, collective learning, and automated data science on big data problems including smart cities, geographic analysis, wireless intelligence, user and system behavior analysis, recommender systems.

Host:
Dr. Jiliang Tang