About me

I am currently a postdoctoral researcher of the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University. My research focuses on the intersection of computational topology, geometry, machine learning, and artificial intelligence.

I earned my Ph.D. in Computer Science from Purdue University, where my doctoral research centered on topological data analysis and graph representations. My work explores innovative approaches to data representation and analysis, to uncover hidden patterns and structures in complex datasets.

Through my research, I aim to develop novel algorithms and techniques that leverage the power of topology and geometry to enhance machine learning and AI models. By bridging the gap between these disciplines, I strive to create more efficient, interpretable, and robust models for a wide range of applications.

My CV. My google scholar. I work on the following projects at the moment.

• Explainable Graph Neural Networks
• Differentiable Topological Representations;
• Non-Euclidean Representations;
• Cooperative learning in Social Settings;