I'm an Assistant Professor in the Department of Statistics at The Ohio State University. In May 2009, I completed my Ph.D. in Statistics at the University of California, Berkeley with Prof. Bin Yu. From 2009 until 2012, I worked with Prof. Rob Kass as a postdoctoral researcher in the Department of Statistics at Carnegie Mellon University.


My research interest is primarily in the area of high-dimensional data analysis and statistical inference. I work on both theoretical and applied aspects, and I'm often motivated by concrete problems arising in the analysis of neuroscientific data — from neural spike trains (electrophysiological recordings) to neuroimaging (such as fMRI). In my past life (before entering graduate school), 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.

Here are some of my recent papers:

  1. Lei, J. and Vu, V.Q. (2015). Sparsistency and Agnostic Inference in Sparse PCA. Annals of Statistics, Vol. 43(1): 299–322. [pdf]
  2. Vu, V.Q., Cho, J., Lei, J., and Rohe, K. (2013). Fantope Projection and Selection: A near-optimal convex relaxation of Sparse PCA. Advances in Neural Information Processing Systems (NIPS) 26.
  3. Vu, V.Q. and Lei, J. (2013). Minimax Sparse Principal Subspace Estimation in High Dimensions. Annals of Statistics, Vol. 41(6): 2905–2947. [pdf]
A complete list of my papers is here. I also develop software and some of it is available on my Github page.