Computer Science and Applied Mathematics

Jocelyn Sunseri

Jocelyn Sunseri is involved in the development of machine learning approaches for chemistry and biophysics. Jocelyn earned a B.S. in physics from the University of Pittsburgh, a B.A. in English from Carnegie Mellon University, and a Ph.D. from the Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology. As a graduate student working with David Ryan Koes, Jocelyn applied deep learning to solve problems in drug discovery. Specifically, she developed gnina, a C++/CUDA program that performs molecular docking and minimization with support for custom scoring functions, and libmolgrid, a C++ library with Python bindings that facilitates machine learning with molecular data. Outside the office, she enjoys playing the accordion and riding her bicycle.

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