Subhankar Bhadra

Subhankar Bhadra is a Postdoctoral Researcher at Pennsylvania State University, working under Dr. Michael Schweinberger. He holds a Ph.D. from North Carolina State University and a Master's from the Indian Statistical Institute, Kolkata. His research focuses on statistical inference for random graphs, causal inference in interference settings, and health data analytics leveraging NLP techniques.

His work bridges network science and causal inference, addressing challenges in community detection, spillover effects, and scalable algorithms for large networks. Notable contributions include a unified framework for blockmodel analysis and methodologies for causal effects under treatment/outcome spillover. His papers are under review at high-impact journals like the Journal of the American Statistical Association.

Subhankar has taught STAT 415/MATH 415 (Introduction to Mathematical Statistics) and actively contributes to advancing statistical methodologies for network data. He has no listed academic awards but demonstrates significant scholarly output through collaborative research projects.