Crunch, Data Conference, October 16-18, 2019 Budapest
Matt Might

Matt Might

Director at Hugh Kaul Precision Medicine Institute


Matt Might has been the Director of the Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham (UAB) since 2017. At UAB, Matt is the Hugh Kaul Kaul Endowed Chair of Personalized Medicine, a Professor of Internal Medicine and a Professor of Computer Science. At UAB, Matt's research focuses on precision prevention, diagnosis and therapeutics across rare disease, cancer and common/chronic conditions. A principal theme in his research is the use of computer and data science to enhance clinical and academic medicine.

From 2016 to 2018, Matt was a Strategist in the Executive Office of the President in The White House. At The White House, Matt worked primarily on President Obama's Precision Medicine Initiative with both the NIH and the Department of Veterans Affairs. And, in 2015, Matt joined the faculty of the Department of Biomedical Informatics at the Harvard Medical School. At Harvard, Matt's research focuses on rare disease discovery and diagnosis, and on the development of personalized therapeutics for rare disease.

Matt is co-founder and Chief Scientific Officer of, a non-profit dedicated to finding treatments for NGLY1 deficiency, and he was a co-founder and Scientific Advisor to Pairnomix, a start-up which identifies potential patient-specific therapies for rare disorders -- and genetic epilepsies in particular. Q State Biosciences acquired Pairnomix in October 2018 and Matt remains a Scientific Advisor.

Matt tweets from @mattmight and blogs at


The algorithm for precision medicine

data science
precision medicine

Precision medicine promises to deliver the right treatment to the right patient at the right time. The open question is how it will do so.

The answer is data. Precision medicine is data-driven medicine. It uses data -- frequently genetic data -- to prevent, diagnose and treat disease at its root cause and in the context of an individual patient.

This talk covers the development of an algorithm for conducting precision medicine, and it casts the creation of this algorithm through the lens of a personal story: of discovering that my child was the first case of a new, ultra-rare genetic disorder. From this story, I will generalize to a process that scales precision medicine to all disease, or rather, to all patients.