Title: Spatio-temporal event modeling through deep kernel-based point processes

Date: November 25th, 2024
Time: 9AM – 10:30AM EST
Location: Groseclose 403
Zoom: https://gatech.zoom.us/j/6011976544?pwd=SG9jUExKOHlUcENWMFFSUzEzWlUwUT09

Zheng Dong
Machine Learning PhD Student
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee
1. Dr. Yao Xie
2. Dr. Pascal Van Hentenryck
3. Dr. Gian-Gabriel Garcia
4. Dr. B. Aditya Prakash
5. Dr. Jorge Mateu

Abstract
As the data volume and complexity in modern applications continue to grow, there is an increasing need in parallel for advanced point process models that can effectively capture intricate event dependencies and dynamics. This thesis focuses on advancing point process modeling by developing deep influence kernels for spatio-temporal event data. Combining statistical modeling principles with the expressive power of deep learning, the proposed methods effectively capture complex event dependencies, improve model estimation efficiency, and enhance interpretability. The thesis also demonstrates the practicality of deep kernel-based point processes in various real-world applications, such as in modeling COVID-19 transmission dynamics and urban crime events. Our contributions in these works can extend to a broader range of methodological and real-world applications, meanwhile inspiring future research in the rapidly evolving area of spatio-temporal event modeling and neural point processes.