•   When: Wednesday, November 09, 2016 from 01:30 PM to 02:30 PM
•   Speakers: Dr. Liang Zhao
•   Location: Nguyen Engineering, Room 4201
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Abstract:

Nowadays, social media has become an important social sensor for significant social issues such as influenza epidemics, financial risks, and public sentiment. Spatiotemporal social events detection, forecasting, and modeling in social media are important and promising problems. These open problems still suffer from a series of challenges including: 1) Dynamics of social media streams, 2) Heterogeneity of spatial and social network, and 3) Noisy and sparse nature of social media content. In this talk, I will describe new models that can address these challenges and effectively capture the underlying predictive patterns for social events. The performance of these models will be demonstrated on important applications of detection and forecasting for significant societal events, such as civil unrest and disease outbreaks. Finally, some of my future research works will also be discussed.

Bio:

Liang Zhao is an assistant professor at Information Science and Technology Department at George Mason University. He got his PhD degree from Computer Science Department at Virginia Tech. His research interests include data mining and machine learning, with particular emphasis on sparse learning, text mining, and social network modeling. He has led the papers in prestigious conferences and journals such as ACM SIGKDD, IEEE ICDM, SIAM Data Mining, PLoS One, and ACM TSAS, and served as the reviewer for leading conferences and journals such as ACM SIGKDD, ACM TKDD, IEEE ICDM, SIAM Data Mining, ACM TIST, ACM SIGSPATIAL, and Geoinformatica. He also owns two US IP discloses on social media mining.