Principal Investigator, Cornell University
Statistical scientist with broad research interests in AI and medicine, working on problems at the intersection of computational biology and machine learning.
Biography
Himel is a tenure-track Principal Investigator at Cornell University’s Department of Population Health Sciences, with joint appointments in the Division of Gastroenterology and Hepatology and the Department of Statistics and Data Science at Bowers College of Computing and Information Science.
His group at Cornell develops computational methods, software, and data products to generate and validate testable hypotheses that accelerate data-driven discovery. Much of his research has focused on reverse translational efforts aiming to integrate vastly different kinds of biological data by leveraging a combination of causal machine learning, systems biology, and omics data science techniques to enable target identification and biomarker discovery across a range of indications.
Himel is a Fellow of the American Statistical Association (FASA), an Elected Member of the International Statistical Institute (ISI), and the recipient of several early-career and research awards.
Mallick Lab
Recent Posts
Research Areas
- Bayesian Biostatistics
- Statistical Bioinformatics
- Multimodal AI
- Causal ML
- Uncertainty Quantification
Education
- Postdoctoral Fellowship in Computational Biology and Bioinformatics, 2019
Harvard University and Broad Institute - PhD in Biostatistics, 2015
University of Alabama at Birmingham - MSc in Statistics, 2009
Indian Institute of Technology Kanpur - BSc in Statistics, 2007
University of Calcutta