Bio. Shuheng Zhou is a Full Professor of Statistics at the University of California, Riverside, with core expertise in high-dimensional statistics, machine learning theory and algorithms. Her research focuses on graphical models, complex and incomplete matrix and tensor data, errors-in-variables, clustering, privacy, approximation and randomized algorithms, and network and combinatorial optimization. She actively applies these rigorous theoretical frameworks to pressing challenges in computational biology, neuroscience, genomics, and spatio-temporal modeling.

Dr. Zhou received her bachelor’s degree from Tsinghua University in Beijing, China, and her Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2006. Prior to her tenured appointment at UC Riverside, she served on the faculty at the University of Michigan (2010–2017) and held postdoctoral fellowships at ETH Zürich (2008–2010) and Carnegie Mellon University (2006–2008). Her awards and honors include the Elizabeth Crosby Research Award at the University of Michigan, as well as a featured article on differential privacy, co-authored with Professor Larry Wasserman, published in the Journal of the American Statistical Association.