Bio

Agnideep “Agni” Aich is a Postdoctoral Scholar in the HEAL Lab within the Department of Emergency Medicine at the Stanford University School of Medicine under the mentorship of Dr. Christian Rose. His research focuses on the development and application of statistical machine learning methods for analyzing complex, high-dimensional data, with applications in healthcare and biomedical sciences.

Prior to joining Stanford, Agni completed his PhD in Mathematics, with a concentration in Statistical Machine Learning, at the University of Louisiana at Lafayette. His dissertation, Learning from Extremes: A Copula-Based Feature Selection Framework for Machine Learning-Driven Risk Prediction, developed a supervised filter for feature selection using copula-based upper-tail dependence to rank predictors by their joint extremal behavior with the outcome, with applications to clinical and public health risk prediction.

Honors & Awards

  • J.D. Testerman Award (Outstanding PhD Student), Department of Mathematics, University of Louisiana at Lafayette (Spring 2025)
  • Rhodes Outstanding Teaching Assistant Award (Outstanding Teaching Performance), Department of Mathematics, University of Louisiana at Lafayette (Fall 2022)

Boards, Advisory Committees, Professional Organizations

  • Member, American Statistical Association (ASA) (2025 - Present)
  • Member, Institute of Mathematical Statistics (IMS) (2025 - Present)
  • Member, American Mathematical Society (AMS) (2025 - Present)
  • Member, Society for Industrial and Applied Mathematics (SIAM) (2025 - Present)

Professional Education

  • PhD, University of Louisiana at Lafayette, Statistical Machine Learning (2026)
  • MS, University of Louisiana at Lafayette, Mathematics (2021)
  • MS, University of Calcutta, Statistics (2018)
  • BS (with Hons.), University of Calcutta, Statistics (2016)

Stanford Advisors

Contact

  • Academic
    agnideep@stanford.edu
    University - Scholar Department: Emergency Medicine - Adult Clinical/Academic Position: Postdoctoral Scholar

Additional Info

Links

Current Research and Scholarly Interests

Agni's research develops statistical machine learning methods for analyzing complex, high-dimensional clinical, biomedical, and population health data. His work centers on predictive modeling, AI in healthcare, supervised feature selection, and dependence-aware methods, including copula-based approaches. At the HEAL Lab, his current focus is on analyzing clinical workflows and AI implementation in healthcare systems, with an emphasis on practical, interpretable, and human-centered outcomes.

Lab Affiliations