Ram Samudrala

Biography

Ram Samudrala is Professor and Chief of Division of Bioinformatics, State University of New York, Buffalo researching multi-scale modeling of atomic, molecular, cellular and physiological systems with an emphasis on protein and proteome structure, function, interaction, design and evolution. His work has led to more than 115 publications in journals such as Science, Nature, PLoS Biology, the Proceedings of the National Academy of Sciences and the Journal of the American Medical Association. He has joined the University of Washington Faculty in 2001 (where he remains as an Affi liate Professor) after completing his Doctoral research with John Moult at the Center for Advanced Research in Biotechnology in 1997 and his Post-doctoral research with Michael Levitt (2013 Nobel in Chemistry) at Stanford University in 2000, which resulted in him making some of the best predictions at the fi rst three community-wide assessment of protein structure prediction (CASP) experiments. Ram Samudrala is Professor and Chief of Division of Bioinformatics, State University of New York, Buffalo researching multi-scale modeling of atomic, molecular, cellular and physiological systems with an emphasis on protein and proteome structure, function, interaction, design and evolution. His work has led to more than 115 publications in journals such as Science, Nature, PLoS Biology, the Proceedings of the National Academy of Sciences and the Journal of the American Medical Association. He has joined the University of Washington Faculty in 2001 (where he remains as an Affi liate Professor) after completing his Doctoral research with John Moult at the Center for Advanced Research in Biotechnology in 1997 and his Post-doctoral research with Michael Levitt (2013 Nobel in Chemistry) at Stanford University in 2000, which resulted in him making some of the best predictions at the fi rst three community-wide assessment of protein structure prediction (CASP) experiments.

Research Interest

Multi-scale modeling of atomic, molecular, cellular and physiological systems with an emphasis on protein and proteome structure, function, interaction, design and evolution.