Computational and Statistical Convergence for Graph Estimation: Just Relax

The general theme of my research in recent years is spatio-temporal modeling and sparse recovery with high dimensional data under measurement error. In this talk, I will discuss several computational and statistical convergence results on graph and sparse vector recovery problems. Our analysis reveals interesting connections between computational and statistical efficiency and the concentration of measure phenomenon in random matrix theory. Our methods are applicable to many application domains such as neuroscience, geoscience and spatio-temporal modeling, genomics, and network data analysis. I will present theory, simulation and data examples. Part of this talk is based on joint work with Mark Rudelson.
Shuheng Zhou is currently an assistant Professor in the Department of Statistics, with a courtesy appointment with the Department of Electrical Engineering and Computer Sciences at the University of Michigan, Ann Arbor. She received her Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, Pennsylvania. Her research interests include statistical machine learning with complex, incomplete and high dimensional data, (non)convex optimization, privacy, approximation and randomized algorithms, and network and combinatorial optimization.