Computing Systems for Data-Driven Science
Foundations of Data Science
Daniel Bienstock is the Liu Family Professor of Industrial Engineering and Operations Research and Professor of Applied Physics and Applied Mathematics at Columbia Engineering, where he has been since 1989. Before joining Columbia, he was a researcher at the Combinatorics and Optimization Group at Bell Communications Research (1986-1989), and an assistant professor at GSIA, Carnegie Mellon University (1985-1986). Bienstock focuses on optimization, with a special interest in discrete and nonconvex optimization, from the standpoint of theory and high-performance implementation. A recent focus of his work has been on modeling cascading failures of power grids and the social impact of epidemics. His book, Potential Function Methods for Approximately Solving Linear Programming Problems, Theory and Practice was published by Springer in 2002. Bienstock, a recipient of the Presidential Young Investigator Award in 1990, was a plenary speaker at the 2005 SIAM Conference on Optimization and a semi-plenary speaker at the 2006 International Symposium on Mathematical Programming. He received his Ph.D. in operations research from MIT in 1985.