Abhishek Chakrabortty is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on advanced statistical methodologies, including concentration inequalities, empirical processes, and debiasing techniques. He specializes in semi-supervised learning, causal inference, and high-dimensional statistical analysis with applications in biomedical informatics and electronic medical records. Key research interests include developing robust frameworks for handling missing data, model misspecification, and non-parametric regression. His work emphasizes practical applications such as phenome-wide association studies and improving treatment effect estimation in complex datasets. His recent publications highlight contributions to semi-supervised inference methods, doubly robust causal estimation, and moving beyond classical sub-Gaussian assumptions in high-dimensional problems. This reflects a strong focus on bridging theoretical advancements with real-world data challenges. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research areas suggest active involvement in mentoring graduate researchers.
