Michael Prentiss is a computational chemist and software-focused postdoctoral fellow with 13 years of experience applying Fortran, Perl, Python and C to scientific computing and molecular simulation. Based at UCSD, he combines theoretical and numerical optimization methods with all-atom and coarse-grain molecular dynamics and Monte Carlo approaches to probe protein folding kinetics and structure formation. His background includes a Visiting Fellowship at Cambridge using basin-hopping search with optimized force fields and consulting work modeling GPCRs with minimal MD models, illustrating an ability to bridge fundamental theory and practical modeling. Trained with a Ph.D. in Chemistry and dual undergraduate degrees in Bioengineering and Political Science, he brings both deep technical rigor and interdisciplinary perspective to complex biophysical problems. An avid Linux user and developer of stochastic-process and optimization code, he is comfortable implementing low-level, performance-sensitive algorithms that drive scientific insight.
13 years of coding experience
University of California, San Diego
B.A. B.S., BioEngineering, Political Science, B.A. B.S., BioEngineering, Political Science at University of Illinois Urbana-Champaign
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