Sajal Chakroborty

Sajal Chakroborty is a Researcher in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI). He holds a Ph.D. and M.S. in Mathematics and Statistics, respectively, from Texas Tech University. His research focuses on interdisciplinary applications of mathematical modeling, statistical methods, and machine learning. Key areas include disease modeling (e.g., COVID-19 and Mpox), image processing, stochastic optimization, and functional analysis.

• Education:
  • Ph.D. in Mathematics, Texas Tech University
  • M.S. in Statistics, Texas Tech University

Chakroborty’s work bridges theory and practice, addressing challenges in public health through computational frameworks and advancing methodologies for small datasets and outlier detection. His recent publications emphasize hybrid deep learning for medical imaging, mathematical modeling of epidemics, and innovative tools for multivariate statistics. His contributions span both foundational mathematics and applied domains like transportation logistics and game theory.

Notable trends in his publications include leveraging machine learning for healthcare diagnostics, developing stochastic optimization techniques for uncertain systems, and integrating potential theory into statistical analysis. His research underscores the interplay between mathematical rigor and real-world problem-solving.