Md Joshem Uddin

Welcome to My Research Page

Hello! I’m a Ph.D. candidate in Mathematics at the University of Texas at Dallas, specializing in topological machine learning on graphs. My research focuses on enhancing graph neural networks (GNNs) through topological data analysis (TDA), with applications in power grid networks. I develop methods to improve graph representation learning for tasks like node classification, link prediction, and graph classification.

Research Interests

• Topological Data Analysis: Using TDA to create robust graph embeddings that improve GNN performance.
• Graph Neural Networks: Developing models like Wise-GNN and ClassContrast to incorporate topological insights.
• Power Grid Networks: Enhancing resiliency and cyberattack detection, funded by NSF and Simons Foundation.
• Temporal Graph Representation: Improving graph representation learning for Dynamic graph focusing on power grid.

Recent Highlights

• SIAM TX-LA Section Conference: Presented on WISE-GNN and organized a mini-symposium on topological and geometric approaches in data science and Meachine Learning.
• Publications and Research Funding: I worked on research projects supported by NSF and Simons Foundation.

Explore my work and publications, and feel free to reach out for collaborations, research discussions, or speaking engagements via the contact page.