LORIN CRAWFORD is the RGSS Assistant Professor of Biostatistics and a core faculty member of the Center for Computational Molecular Biology at Brown University. Prior to joining Brown, he received his PhD in 2017 from the Department of Statistical Science at Duke University.

The central theme of Professor Crawford’s research program is to provide computational tools that improve our understanding of the relationship between genotypic and phenotypic variation. His lab consists of a diverse group of scientists working to address problems in domains such as quantitative genetics, clinical imaging, and molecular dynamics.

Professor Crawford’s most recent work has earned him a place on Forbes 30 Under 30 list, The Root 100 Most Influential African Americans list, and recognition as an Alfred P. Sloan Research Fellow and a David & Lucile Packard Foundation Fellowship for Science and Engineering.

Overview

The goal of this talk is to broadly explore how the future of data science will be shaped by the scale and scope of interdisciplinary teams and collaborations. Our research group combines theoretical tools from seemingly disparate fields to generate practical solutions for problems in biology and health. Here, I will highlight two of our recent projects where we use machine learning to understand how nonadditive genetic variation affects the architecture of complex human traits and use topological data analysis to discover biophysical signatures of protein ensembles in molecular dynamics.