You are cordially invited to attend my thesis defense on April 3rd.

Title: Learning with Graph Structured Data

Date: April 3, 2023

Time: 1:30 - 3:00 pm ET

Location: Price Gilbert Library- 4222

Zoom Link: https://gatech.zoom.us/j/5339746700

Student Name: Rahul Singh

Machine Learning PhD Student

School of Aerospace Engineering
Georgia Institute of Technology

Committee

1.  Prof. Yongxin Chen (Advisor, School of Aerospace Engineering)

2.  Prof. Yao Xie (School of Industrial and Systems Engineering)

3.  Prof. Srijan Kumar (College of Computing)

4.  Prof. Eva Dyer (Department of Biomedical Engineering)

5.  Prof. Lipeng Ning (Harvard Medical School)

Abstract

Graphs provide a natural way to represent information in structured form. When the entities in a graph are random variables, it gives rise to probabilistic graphical models (PGMs). Traditional methods in PGMs are concerned with the structured data generated with known individual's association. These methods are not applicable in the setting when the data is generated by a large population of individuals with unknown individual's association. First part of the thesis is concerned with inference and learning from aggregate data generated by a large population of individuals each following a certain PGM. The second part of the thesis address the problem of representation learning over signed graphs. We propose spectral signed graph neural network (GNN) designs for learning node embeddings for signed graphs. Furthermore, we introduce signed Magnetic Laplacian for spectral analysis of directed signed graphs and use it to propose new spectral GNN designs applicable to directed signed graphs.