I spent part of my childhood in Kenya, where I got to see my father work as an ophthalmologist and my mother work as an epidemiologist, and what it could mean to engage with healthcare at the level of individual patients. When we moved to the US, I became enamored with computer science and how we think about data. Today, I’m driven to identify where and how the two disciplines I am most passionate about – machine learning and biomedicine – can come together to help shape novel therapeutics and bring us toward more adaptive and personalized patient care.
David Reshef is a computer scientist, physician, and a venture partner at GV, where he focuses on investing at the intersection of machine learning, healthcare, and life sciences.
David's career has focused on using machine learning to identify and characterize structure in high-dimensional data. His work has been published in high-impact journals including Science, Proceedings of the National Academy of Sciences, The Journal of Machine Learning Research, and The Annals of Applied Statistics, and he has focused on applications across a broad range of fields including computer-aided diagnostics, disease modeling, high-throughput screens, and immunotherapy. David has also spent time working on public health initiatives in India, Zambia, and Peru.
David holds an M.D. with honors from Harvard Medical School and a Ph.D. in computer science from MIT, where he was a Soros Fellow and a National Science Foundation Fellow. Prior to that, he studied statistics at the University of Oxford as a Marshall Scholar and computer science at MIT. His work was awarded a best thesis award from the Department of Electrical Engineering and Computer Science at MIT.
I spent part of my childhood in Kenya, where I got to see my father work as an ophthalmologist and my mother work as an epidemiologist, and what it could mean to engage with healthcare at the level of individual patients. When we moved to the US, I became enamored with computer science and how we think about data. Today, I’m driven to identify where and how the two disciplines I am most passionate about – machine learning and biomedicine – can come together to help shape novel therapeutics and bring us toward more adaptive and personalized patient care.