Atul Butte | Director of Institute Computational Health Sciences and Professor
Institute of Computational Health Sciences University of California San Francisco

Atul Butte, Director of Institute Computational Health Sciences and Professor, Institute of Computational Health Sciences University of California San Francisco

Atul Butte, MD, PhD is the inaugural Director of the Institute of Computational Health Sciences ( at the University of California, San Francisco (UCSF), and a Distinguished Professor of Pediatrics.  Dr. Butte is also the Executive Director for Clinical Informatics across the six University of California Medical Schools and Medical Centers.  Dr. Butte trained in Computer Science at Brown University, worked as a software engineer at Apple and Microsoft, received his MD at Brown University, trained in Pediatrics and Pediatric Endocrinology at Children's Hospital Boston, then received his PhD from Harvard Medical School and MIT.   Dr. Butte has authored over 200 publications, with research repeatedly featured in the New York Times, Wall Street Journal, and Wired Magazine.  Dr. Butte was elected into the National Academy of Medicine in 2015, and in 2013, he was recognized by the White House as an Open Science Champion of Change for promoting science through publicly available data.  Dr. Butte is also a founder of three investor-backed data-driven companies: Personalis, providing medical genome sequencing services, Carmenta (acquired by Progenity), discovering diagnostics for pregnancy complications, and NuMedii, finding new uses for drugs through open molecular data. Dr. Butte is a principal investigator of three major programs: the California Initiative to Advance Precision Medicine; ImmPort, the clinical and molecular data repository for the National Institute of Allergy and Infectious Diseases; and the California Precision Medicine Consortium, helping recruit tens of thousands of participants into President Obama's Precision Medicine Initiative.


BioData West: Day 1 @ 08:30

Bringing big data and genomics to unlock cures

  • What is the current state of the field and how have new techniques in data analytics allowed us to probe deeper into data sets to gain understanding of rare disease morphologies?
  • How do we connect millions of disease specific data points using novel statistical machine learning techniques to develop new therapies? 
  • The time is now - how does UCSF leverage the power of supercomputing infrastructure, once reserved for astrophysicists, to develop new pathways to cures? 

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