Sahand Negahban is currently an Assistant Professor in the Statistics Department at Yale University. Prior to that he worked with Prof. Devavrat Shah at MIT as a postdoc and Prof. Martin J. Wainwright at UC Berkeley as a graduate student.
The focus of Prof. Negahban's research is to develop theoretically sound methods, which are both computationally and statistically efficient, for extracting information from large datasets. A salient feature of his work has been to understand how hidden low-complexity struc- ture in large datasets can be used to develop computationally and statistically efficient methods for extracting meaningful information for high-dimensional estimation problems. His work borrows from and improves upon tools of statistical signal processing, machine learning, probability and convex optimization.