Arjun Manrai

Arjun (Raj) Manrai, PhD

Assistant Professor of Biomedical Informatics, Harvard Medical School
Deputy Editor, NEJM AI
10 Shattuck Street #304, Boston, MA, 02115

Arjun (Raj) Manrai, PhD is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School, where he leads a research lab that works broadly on applying machine learning and statistical modeling to improve medical decision-making. Raj is also a founding Deputy Editor of NEJM AI, the new artificial intelligence-focused journal from the publishers of the New England Journal of Medicine, and co-host of the NEJM AI Grand Rounds podcast.

Focus areas for Raj’s research group include the role of artificial intelligence in medical diagnosis, the clinical use of genomic data and blood laboratory biomarkers, inherited heart disease and kidney disease, decision making across populations, and reproducibility and safety challenges for medical artificial intelligence. His work has been published in the New England Journal of Medicine and JAMA, presented at the National Academy of Sciences, and featured in the New York Times, Wall Street Journal, and NPR.

Raj is also closely involved in the mentoring of students at Harvard College, having served for over a decade as a Resident Tutor and now member of the Senior Common Room of Leverett House. Students from the lab have won the Rhodes, PD Soros, and other awards to continue their training and research in machine learning and medicine.

Raj earned an AB in Physics from Harvard College followed by a PhD in Bioinformatics and Integrative Genomics from the Harvard-MIT Division of Health Sciences and Technology (HST).

He resides in the Boston area and outside work he can usually be found losing home dance competitions to his 2 young daughters.

DBMI Research Areas
DBMI Courses
What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health.
Authors: Passero K, Setia-Verma S, McAllister K, Manrai A, Patel C, Hall M.
Pac Symp Biocomput
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Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.
Authors: Krissaane I, De Niz C, Gutiérrez-Sacristán A, Korodi G, Ede N, Kumar R, Lyons J, Manrai A, Patel C, Kohane I, Avillach P.
J Am Med Inform Assoc
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Prediction of chronological and biological age from laboratory data.
Authors: Sagers L, Melas-Kyriazi L, Patel CJ, Manrai AK.
Aging (Albany NY)
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Challenges to the Reproducibility of Machine Learning Models in Health Care.
Authors: Beam AL, Manrai AK, Ghassemi M.
JAMA
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Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.
Authors: Manrai AK, Ioannidis JPA, Patel CJ.
Am J Epidemiol
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Author Correction: Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes.
Authors: Lakhani CM, Tierney BT, Manrai AK, Yang J, Visscher PM, Patel CJ.
Nat Genet
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Potential Excessive Testing at Scale: Biomarkers, Genomics, and Machine Learning.
Authors: Mandl KD, Manrai AK.
JAMA
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Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes.
Authors: Lakhani CM, Tierney BT, Manrai AK, Yang J, Visscher PM, Patel CJ.
Nat Genet
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Using Big Data to Determine Reference Values for Laboratory Tests-Reply.
Authors: Manrai AK, Patel CJ, Ioannidis JPA.
JAMA
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In the Era of Precision Medicine and Big Data, Who Is Normal?
Authors: Manrai AK, Patel CJ, Ioannidis JPA.
JAMA
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