PRATIK SHAH

Pratik Shah

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Pratik Shah is the Principal Investigator leading the Health 0.0 research lab at MIT that works on engineering, medical imaging, machine learning, and biological technologies for novel scientific discoveries that improve clinical and community health outcomes.

Key goals are: 1) Novel medical technologies for translational clinical and biomedical research and real-world benefit for patients and providers; 2) Augmenting artificial intelligence (AI), machine learning , medical imaging and neural network capabilities for personalized digital medicines; and 3) Empowering patients, physicians, researchers, and regulators for making informed and equitable healthcare decisions. Recent work from his lab has been published in Nature Digital Medicine, Cell press, Journal of American Medical Association, IEEE conferences, and Proceedings of National Academies of Science Engineering and Medicine workshops. Pratik serves on the grant reviewer board of Center for Scientific Review at the National Institutes of Health and foundations supporting patient-centric research. Past acknowledgements include the American Society for Microbiology’s Raymond W. Sarber National Award, a Harvard Medical School and Massachusetts General Hospitals ECOR Fund for Medical Discovery postdoctoral fellowship, coverage by leading national and international news media outlets. Pratik has been an invited discussion leader at Gordon Research Seminars; a speaker at American Association for the Advancement of Sciences, Cold Spring Harbor Laboratories, Gordon Research Conferences, The National Academies of Sciences, Engineering and Medicine, TED and IEEE bioengineering conferences; and a peer reviewer for leading scientific publications. Pratik has BS, MS, and PhD degrees in biological sciences and completed fellowship training at Massachusetts General Hospital, the Broad Institute of MIT and Harvard, and Harvard Medical School.

Read more about Pratik's ongoing research at MIT here.

Watch Pratik's TED talk on biomarker prediction from low-cost images using machine learning here.