John D. Chodera - vita

Assistant Member, Computational and Systems Biology Program, Sloan Kettering Institute 2012– Assistant Member, Memorial Sloan Kettering Cancer Center 2012– Assistant Professor, Physiology, Biophysics, and Systems Biology Program, 2013– Weill Cornell Graduate School of Medical Sciences Independent Distinguished Postdoctoral Fellow, California Institute for Quantitative Biosciences (QB3), 2008–2012 University of California, Berkeley Postdoctoral researcher, Department of Chemistry, Stanford University 2006–2008 With Vijay S. Pande (founder, Folding@Home distributed computing project) PH.D. in Biophysics, University of California, San Francisco 1999–2006 Dissertation: Master equation models of macromolecular dynamics from atomistic simulation. Committee: Ken A. Dill (thesis advisor), Matthew P. Jacobson, Vijay S. Pande B.S. in Biology, California Institute of Technology 1995–1999 Undergraduate research with Paul H. Patterson (experimental molecular neurobiology) and Jerry E. Solomon (computational chemistry) Fellowships and awards Louis V. Gerstner Young Investigator Award 2013–2016 Google Exacycle for External Faculty 2013–2014 QB3-Berkeley Distinguished Postdoctoral Fellowship, University of California, Berkeley 2008–2012 IBM Predoctoral Fellowship 2005–2006 Frank M. Goyan Award for outstanding work in physical chemistry, University of California, San Francisco 2005 Howard Hughes Medical Institute Predoctoral Fellowship 2000–2005 Research overview My research focuses on redesigning the way we develop small molecules for chemical biology and drug discovery and bringing rigorous atomistic modeling into the high-throughput biology and genomics era. By combining novel algorithmic advances to achieve orders-of-magnitude efficiency gains with powerful but inexpensive GPU hardware and distributed computing technologies, I am developing a new generation of tools for predicting small molecule binding affinities, designing small molecules with desired properties, quantifying drug sensitivity or resistance of clinical mutations, and understanding the detailed structural mechanisms underlying oncogenic mutations. As a core member of the Folding@home Consortium, my lab harnesses the computing power of hundreds of thousands of volunteers around the world to study functional implications of mutations and new opportunities for therapeutic design against cancer targets. Using a unique automated biophysical laboratory, we collect new experimental data targeted to advance the quantitative accuracy of our methodologies, and gather new insight into drug susceptibility and resistance in kinases and other cancer targets. My work makes extensive use of scalable Bayesian statistical inference methods and information theoretic principles for designing experiments and quantifying error.