Shuheng Zhou is a full Professor of Statistics at the University of California, Riverside. Her area of expertise is high dimensional statistics, machine learning theory and algorithms. She studies graphical models, complex and incomplete matrix and tensor data, errors-in-variables, clustering, privacy, approximation and randomized algorithms, and network and combinatorial optimization, with applications in computational biology, neuroscience, genomics, and spatio-temporal modeling.

Shuheng Zhou received her bachelor’s degree from Tsinghua University, Beijing, China and her PhD in Electrical and Computer Engineering at Carnegie Mellon University in 2006. Prior to her tenured appointment at the University of California, Riverside, she was a faculty at the University of Michigan (2010--2017), a postdoctoral fellow at ETH Zürich (2008--2010) and at Carnegie Mellon University (2006--2008).  Her awards and honors include a featured article coauthored with Professor Larry Wasserman that appeared in the Journal of the American Statistical Association on differential privacy and the Elizabeth Crosby Research Award at U.M.