Habibul Ahsan
Visiting Professor of Epidemiology
Research Interests
Professor Ahsan’s research interests include applying AI and machine learning to large-scale genomic, environmental, and clinical datasets to improve disease prediction and prevention. His work explores deep learning and causal inference to uncover complex gene-environment-lifestyle interactions that drive disease risk and treatment outcomes. By leveraging multimodal data from diverse population cohorts, he aims to develop scalable, equitable, and real-world AI-driven tools for precision medicine. His research also emphasizes integrating computational methods with clinical and population health strategies to accelerate translation into healthcare systems globally. Email
In addition to his MBZUAI affiliation, Professor Ahsan serves as the Louis Block Distinguished Service Professor of Epidemiology, Family Medicine, and Human Genetics at the University of Chicago, where he is also the Dean for Population and Precision Health and Director of the Institute for Population and Precision Health (IPPH). Before joining UChicago, he was a tenured faculty member at Columbia University, where he helped establish pioneering research programs in molecular and genomic epidemiology.
Internationally recognized for integrating genomic, environmental, and clinical data to understand and prevent chronic diseases, Professor Ahsan now leads efforts in AI-driven precision health. His work leverages machine learning to model complex interactions among genetics, environment, and lifestyle. He directs major initiatives, including the Illinois Precision Medicine Consortium for the NIH All of Us Program, the long-running HEALS cohort, and the COMPASS multi-ethnic cohort, generating multimodal datasets ideal for AI applications.
Professor Ahsan has published more than 450 papers, secured more than $100 million in research funding, and mentored numerous scientists across computational and biomedical sciences. By bridging population-scale data science with genomic and clinical epidemiology, he is shaping the future of AI-enabled precision health, ensuring innovations translate into scalable and equitable real-world healthcare solutions worldwide.
- Postdoctoral Fellow in Epidemiology, Columbia University
- Master of Medical Science (MMedSc) in Epidemiology from the University of Western Australia
- Postgraduate Diploma (PGDip) in Public Health from the University of Western Australia
- MBBS (MD) in Medicine from the Institute for Postgraduate Medicine & Research, Dhaka University
- ASCO Annual Meeting Planning Committee 2015 - 2017
- NIH/NIEHS - National Advisory Environmental Health Sciences Council 2015 - 2019
- National Academy of Sciences Scientific Panel on Toxicological Assessment 2012 - 2016
- Associate Editor, Genetic Epidemiology 2006 - 2011
- Ellis S, Song S, Reiman D, et al. AI-assisted Diagnosis of Nonmelanoma Skin Cancer in Resource-Limited Settings. Cancer Epidemiol Biomarkers Prev. 2025;34(7):1080-1088. doi:10.1158/1055-9965.EPI-25-0132. PMID: 40287980
- Suzuki K, Hatzikotoulas K, Southam L, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024;627:347-357. doi:10.1038/s41586-024-07019-6. PMID: 38374256
- Oliva M, Demanelis K, Lu Y, et al. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet. 2023;55:112-122. doi:10.1038/s41588-022-01248-z. PMID: 36510025
- Mahajan A, Spracklen CN, Zhang W, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet. 2022;54:560-572. doi:10.1038/s41588-022-01058-3. PMID: 35551307
- Demanelis K, Jasmine F, Chen LS, et al. Determinants of telomere length across human tissues. Science. 2020;369(6509):eaaz6876. doi:10.1126/science.aaz6876. PMID: 32913074
- Islam MR, Islam H, Siddiqua SM, et al. Understanding Cancer Risk Among Bangladeshi Women: An Explainable Machine Learning Approach to Socio-Reproductive Factors Using Tertiary Hospital Data. Healthcare (Basel). 2025;13(12):1432. doi:10.3390/healthcare13121432. PMID: 40565458
- Võsa U, Claringbould A, Westra HJ, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53:1300-1310. doi:10.1038/s41588-021-00913-z. PMID: 34475573
- Cui YH, Yang S, Wei J, et al. Autophagy of the m6A mRNA demethylase FTO is impaired by low-level arsenic exposure to promote tumorigenesis. Nat Commun. 2021;12:2183. doi:10.1038/s41467-021-22469-6. PMID: 33846348
- Sadoff J, Gray G, Vandebosch A, et al. Final Analysis of Efficacy and Safety of Single-Dose Ad26.COV2.S. N Engl J Med. 2022;386:847-860. doi:10.1056/NEJMoa2117608. PMID: 35139271
- Sadoff J, Gray G, Vandebosch A, et al. Safety and Efficacy of Single-Dose Ad26.COV2.S Vaccine against Covid-19. N Engl J Med. 2021;384:2187-2201. doi:10.1056/NEJMoa2101544. PMID: 33882225
- Karlsson Linnér R, Biroli P, Kong E, et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals. Nat Genet. 2019;51:245-257. doi:10.1038/s41588-018-0309-3. PMID: 30643258
- Porcu E, Rüeger S, Lepik K, et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun. 2019;10:3300. doi:10.1038/s41467-019-10936-0. PMID: 31341166