The primary focus of my research is on high-dimensional statistics and statistical machine learning. High-dimensional data problems are pervasive in contemporary applications and present an array of challenges including theoretical foundations, methodological development, and computational limits. My research delves into all three facets, but I am particularly keen on investigating the interface between statistical and computational theory. In addition to these foundational issues, I engage in cross-disciplinary collaborations and am often motivated by concrete problems arising in the analysis of neuroscientific data - such as  neuroimaging (such as fMRI). In my pre-academic life, I was a software engineer in silicon valley for over six years, so I'm very keen on opportunities for statistical theory and computation to come together to solve interesting scientific and engineering problems.

Vincent (Vince) Vu joined the statistics faculty in 2012. He is currently an associate editor for the reviews section of the Journal of the American Statistical Association and The American Statistician. His research has been funded by the NSF, and he received the Best Paper Award at the 2012 International Conference on Artificial Intelligence and Statistics (AISTATS).