Seminar: Privacy-Aware Predictive Modeling of Human Event Data

 

  

Mingxuan Sun

Assistant Professor, LSU Division of Computer Science and Engineering

Friday September 11, 2020

3:00 pm

Location: online-only

Abstract

Machine learning that leverages individuals' event data can improve the prediction accuracy of future events, but introduces high risks to each individual's privacy. Nowadays, large volumes of human event data, such as online TV-viewing records, disaster rescue requests, and electronic records of hospital admissions, are becoming increasingly available in a wide variety of applications including healthcare analytics, smart cities, and social network analysis. Predictive modeling of those collective event sequences is beneficial for improving event response efficiency and promoting nationwide economic development. However, by optimizing for the unitary goal of accuracy, machine learning algorithms trained on historic event data may amplify privacy risks.

In this talk, we will introduce a series of novel models and algorithms to analyze human events to balance between prediction accuracy and privacy. Specifically, we investigate novel point processes and deep learning methods to improve event prediction accuracy. We will also introduce interpretable algorithms to explain how user information is used in event prediction, which improves human understanding and trust of predictive modeling. In the end, we will discuss scenarios where certain private information can be inferred from user inputs and investigate privacy-preserving approaches for event prediction.

Bio

Mingxuan Sun is an Assistant Professor in the Division of Computer Science and Engineering at Louisiana State University. She received her Ph.D. degree in Computer Science from the Georgia Institute of Technology, Atlanta, GA in 2012. Before joining LSU, she was a Senior Scientist with Pandora Media, Inc., Oakland, CA. Her research interests include machine learning, data mining, and information retrieval. She is also interested in machine learning and AI applications in social informatics, information security, and wireless communications. She has published research papers in leading journals and conferences including PAMI, JMLR, NIPS, AAAI, KDD, ICDM, etc. She is a recipient of NSF CAREER Award in 2020.