DMS Statistics and Data Science Seminar

Speaker: Dr. Yao Xie, Georgia Institute of Technology

ZOOM LINK: https://auburn.zoom.us/j/82501343299

Title: Statistical Inference for Spatio-Temporal Point Processes

Abstract: Discrete events are a sequence of observations consisting of event time, location, and possibly "marks" with additional event information. Such event data is ubiquitous in modern applications, such as social networks, seismic activities, police reports data, neuronal spike trains, and disease spread counts. We are particularly interested in capturing the complex dependence of the discrete events data, particularly estimating how nodes interact with each other, such as the triggering or inhibiting effects of the historical events on future events. This helps us recover network topology, perform causal inference, understand spatio-temporal dynamics, and make predictions. Motivated by popular Hawkes processes, we introduce a new general modeling approach for capturing spatio-temporal interaction, which enjoys computationally efficient model estimation procedures. We establish statistical guarantees by connecting to a modern convex optimization theory of solving variational inequality. The good performance of the proposed method is illustrated using several real-world data sets.

Bio: Yao Xie is an Associate Professor and Harold R. and Mary Anne Nash Early Career Professor at Georgia Institute of Technology in the H. Milton Stewart School of Industrial and Systems Engineering, and an Associate Director of the Machine Learning Center. She received her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University, M.Sc. in Electrical and Computer Engineering from the University of Florida, and B.Sc. in Electrical Engineering and Computer Science from the University of Science and Technology of China (USTC). She was a Research Scientist at Duke University. Her research areas are statistics (in particular sequential analysis and sequential change-point detection), machine learning, and signal processing, providing the theoretical foundation and developing computationally efficient and statistically powerful algorithms. She has worked on such problems in sensor networks, social networks, power systems, crime data analysis, and wireless communications. She received the National Science Foundation (NSF) CAREER Award in 2017. She is currently an Associate Editor for IEEE Transactions on Signal Processing, Sequential Analysis: Design Methods and Applications, and INFORMS Journal on Data Science, and serves on the Editorial Board of Journal of Machine Learning Research, Area Chair of NeurIPS 2021.