The Manrai Lab is a team of machine learning scientists, clinicians, and biomedical data scientists working to improve medical decision making by developing computational approaches that incorporate rich and deep representations of clinical state and an individual's identity into care. Active projects include:

  1. Improving genetic variant classification and quantifying risk ("penetrance") in clinical genomics, with a focus on inherited heart disease (e.g. Manrai et al. NEJM 2016)
  2. Measuring "normal" variation for blood laboratory biomarkers across populations with a focus on creatinine and kidney disease (e.g. Manrai et al. JAMA 2018)/li>
  3. Developing semi-supervised learning approaches with applications including medical imaging and text (e.g. Melas-Kyriazi & Manrai 2020)/li>
  4. Modeling reproducibility in integrative biomedical studies using meta-science ("science of science") approaches (e.g. Manrai et al. AJE 2019)/li>

The group's research 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.


Arjun (Raj) Manrai is an Assistant Professor at Harvard Medical School and Faculty Member in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital. Manrai received an A.B. in Physics with Highest Honors from Harvard and earned his Ph.D. in Bioinformatics and Integrative Genomics from the Harvard-MIT Division of Health Sciences and Technology.