Vijay S. Pande is currently an Associate Professor in the Chemistry Department at Stanford University. Pande’s current research centers on the development and application of novel grid computing simulation techniques to address problems in chemical biology. In particular, he has pioneered novel distributed computing methodology to break fundamental barriers in the simulation of kinetics and thermodynamics of proteins and nucleic acids. As director of the Folding@Home and Genome@Home projects, Prof. Pande has directly simulated protein folding dynamics with quantitative comparisons through experiment, often considered a “holy grail” of computational biology. His current research also includes novel simulation methods for high precision drug binding affinity calculations, protein design, and synthetic bio-mimetic polymers. After graduating from Langley High School, Pande received a BA in Physics from Princeton University in 1992. There, he was first introduced to biophysical questions, especially in his undergraduate thesis research with Philip Anderson, a Nobel Laureate in physics. Three years later, he received his Ph.D. in physics from Massachusetts Institute of Technology (MIT), studying as a NSF Fellow under Profs. Toyoichi Tanaka and Alexander Grosberg. At MIT, Pande’s research centered on statistical mechanical models of protein folding and design, analyzing ways to design protein sequences to have the desired stability and folding properties. As a Miller Fellow working with Daniel Rokhsar at UC Berkeley, Pande extended this methodology to examine atomistic protein models. Pande has won numerous awards, including most recently being named to MIT’s TR100 (top young innovators in 2002), a Frederick E. Terman Fellowship (2002), a Henry and Camile Dufus Teacher-Scholar (2003), and the Irving Sigal Young Investigator of the Protein Society (2006). Pande holds the position of Associate Professor of Chemistry and (by courtesy) of Structural Biology. He currently teaches an undergraduate course on chemical thermodynamics and a graduate course on statistical mechanics.
