You are cordially invited to my thesis defense on April 24th.

Title: Statistical Learning and Change Detection for Dynamic Networks

Date: April 24th 2023

Time: 11am-12pm EST

Location: Remote, meeting link: https://gatech.zoom.us/j/91247879302?pwd=cWx6cW5OMWFINWJFR2tqbm1Ldi9Idz09

Minghe Zhang

Machine Learning Ph.D. Candidate

School of Industrial and Systems Engineering (ISyE)
Georgia Institute of Technology

Committee

1 Dr. Yao Xie (Advisor, ISyE)

2 Dr. Pascal Van Hentenryck (ISyE)

3 Dr. Kamran Paynabar (ISyE)

4 Dr. Basak Kalkanci (Scheller College of Business)

5 Dr. Feng Qiu (Argonne National Laboratory)

Abstract

The field of graph analysis has recently garnered a significant amount of attention due to its importance in maximizing the utility of data. While most prior research in graph learning has primarily focused on static graphs with fixed nodes and edges, there are numerous real-world applications where the graph structure evolves dynamically, such as social networks, sales data, or transportation systems. In this thesis, we concentrate on a specific type of graph, called the Dynamic Graph, which aims to capture such graph dynamics. Our study commences with an exploration of discrete-time dynamic graphs and identifies structural changes based on a statistical model utilizing a spectral CUSUM method. Furthermore, we examine continuous-time dynamic graphs to model solar power in a practical solar system and forecast anomalous solar activities in real-time using a marked temporal point process model. Lastly, we investigate the financial impact of serious environmental violations committed by suppliers on purchasing firms by analyzing supply chain networks that link the suppliers with the buying firms.

Best wishes,