資管系博士班Seminar演講公告

講題：Interpretable Spatiotemporal Learning to Forecast Human-Societal Activity
時間：2019/05/02 (四) 14:00 ~ 16:00
地點：國立中山大學管理學院管CM 3028-1會議室(3F)
講者：Yu-Ru Lin - Associate Professor at the School of Computing and Information, University of Pittsburgh

演講摘要：
The relationships between individual behavior and broader-scale societal structures have been central to a range of human phenomena, from job and migration decisions to political participation to the experience of a specific health-related event. Recent progress in predictive analytics along with big data offers a powerful way to explore those relationships but often gives rise to the "black box" problem with little insight into what goes on in the algorithmically learned relationships. In this talk, I will present a spatiotemporal learning approach that leverages a deep learning framework with relevant social theories to help examine the relationship between human-societal activity and their social and geographical contexts, with applications including predicting political protest and opioid overdose events. Our approach is not only capable of forecasting the occurrence of future events, but also provides theory-relevant interpretations -- it allows for interpreting what features, from which places, have significant contributions on the forecasting model, as well as how they make those contributions. I will discuss the results and implications of our recent studies based on the proposed approach.

講者簡介：
Yu-Ru Lin is an Associate Professor at the School of Computing and Information, University of Pittsburgh. She researches on the interaction of Computational Social Science, Data Mining, and Visualization. She specializes in using social network and text data along with statistical learning tools and social theories to study phenomena spanning politics and policy, opinion dynamics, anomalous behaviors, and other crucially important complex patterns concerning collective attention and actions, as well as human and social dynamics in response to societal risks. Additional information can be found at: http://www.yurulin.com/.

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