D. E. Shaw Research Recruiting Talk
D. E. Shaw Research (DESRES) is a New York–based independent research laboratory that conducts scientific research in the field of computational biochemistry. Our group is currently focusing primarily on long molecular dynamics (MD) simulations involving proteins and other biological macromolecules of potential interest from both a scientific and a pharmaceutical perspective. An integral part of our effort consists of designing and developing custom hardware and software to carry out this research. We are also pursuing drug discovery programs, leveraging the insights enabled by our novel technologies, with an initial focus on drug targets that have proved resistant to traditional drug discovery methods. D. E. Shaw Research teams include computational chemists and biologists, computer scientists and applied mathematicians, and computer architects and engineers, all working collaboratively within a tightly coupled interdisciplinary research environment under the leadership of our chief scientist David Shaw.
About the Speakers
Xinyi Guo
Xinyi Guo is involved in algorithm design and software development for computational chemistry applications. She holds a B.A. in physics and mathematics from Pomona College and a Ph.D. in astrophysics with a secondary field in computational sciences and engineering from Harvard University. For her dissertation research on fundamental plasma physics, she discovered mechanisms for particle heating and acceleration in collisionless shocks using state-of-the-art ab initio plasma simulations. Xinyi has also conducted research on gravitational waves at the California Institute of Technology.
John Cherian
John Cherian is involved in the development of improved force fields for biomolecular simulation. He received a B.S. with distinction in mathematical and computational science and an M.S. in statistics from Stanford University. As an undergraduate, John served as a research assistant in Stanford's Department of Statistics, where he analyzed the randomization time of the "overhand shuffle". He also performed research in Stanford's Holmes Lab, where he conducted a longitudinal analysis of microbiome data and then developed and implemented a novel model to handle non-Gaussian count data.