Ph.D. in Mathematics, University of California, Davis, 2005-2009. In her thesis, Deanna developed and analyzed the first strongly polynomial algorithms for compressed sensing, which she published in FoCM and ACHA. Deanna is a Professor of Mathematics at UCLA, the Executive Director of the Institute for Digital Research and Education, and Dunn Family Endowed Chair in Data Theory. She won numerous awards including Sloan Research Fellowship, NSF CARREER Award, and the IMA Prize in Mathematics and its Applications.

Ph.D. in Statistics, University of Michigan, 2012-2016. Can was co-advised by Professor Elizaveta Levina and me. Can's thesis was on community detection in networks, see this ICM survey and papers in Annals of Statistics and Random Structures and Algorithms. Can is an Assistant Professor of Statistics at UC Davis.

Ph.D. in Mathematics, University of Michigan, 2014-2018. Elizaveta (Liza) studied invertibility of heavy-tailed random matrices, and also how to improve a random matrix by modifying a small fraction of the entries; see these papers in Israel Journal of Mathematics, Advances in Mathematics, and Journal of Theoretical Probability. Elizaveta is an Assistant Professor at Princeton University.

Ph.D. in Mathematics, University of Michigan, 2015-2018. Yan Shuo studied how to recover low-dimensional structures from high-dimensional data. He advanced mathematical understanding of phase retrieval and non-gaussian component analysis. Yan Shuo is an Assistant Professor at the National University of Singapore.

Ph.D. in Mathematics, University of California, Irvine, 2014-2019. Jennifer was co-advised with Prof. Hongkai Zhao. She studied how much independence is needed in the classical limit laws of random matrix theory, and published her work in Random Matrices: Theory and Applications.

Ph.D. in Mathematics, University of California, Irvine, 2018-2023. Kat designed a new method to visualize high-dimensional data, and published it in Journal of Computational Science. She also worked on mathematical principles behind the popular tool of data visualization called t-SNE.

Yaniv was an NSF Postdoctoral Fellow and Hildebrandt Assistant Professor at the University of Michigan during 2011-2014. Yaniv and random hyperplane tessellations, developed the first tractable algorithms for the one-bit compressed sensing, and extended this work to logistic regression, non-linear Lasso and general non-linear measurement systems, non-gaussian measurements, and developed a geometric framework for high-dimensional estimation problems. Yaniv is now Associate Professor at the University of British Columbia.

Beatrice was a Postdoctoral Assistant Professor at University of Michigan during 2014-2017. She is an expert in convex geometry and geometric functional analysis. Beatrice co-authored a book in this area when she was still a graduate student. She is Assistant Professor at the University of Alberta.

Anna was a Visiting Assistant professor at UC Irvine during 2019-2022. She works on data visualization, iterative linear solvers, and sparse recovery. Anna is now Assistant Professor at UC Irvine.

March was a Visiting Assistant professor at UC Irvine during 2021-2022. Together Thomas Strohmer and myself, March has been building mathematical foundations for private synthetic data: see this, this, this, this, and this paper. March is now Assistant Professor at Michigan State University.

Yizhze is a Visiting Assistant professor at UC Irvine during 2022-2024. He specializes in random matrices, random graphs, and neural networks. Together Thomas Strohmer and myself, Yizhe has been building mathematical foundations for private synthetic data: see this COLT 2023 paper.

Yin-Ting was a Visiting Assistant professor at UC Irvine during 2022-2023. He studies high-dimensional probability and its applications to data analysis and asymptotic convex geometry.

M.S. in Applied and Interdisciplinary Mathematics, University of Michigan, 2014-2015. Joe worked with me on non-linear inverse regression. He continued to the Statistics Ph.D. program at UC Berkeley.

David was my REU student in the Summer 2011. He studied developed the high-dimensional version of the notion of median. He used the multivariate median to develop robust Principal Component Analysis for data.

Albert from Princeton University and Alex from University of Michigan were my REU students in the Summer 2012, co-advised with Dr. Yaniv Plan and me. We studied signal recovery from non-gaussian single-bit measurements. Our results were published in Linear Algebra and Applications. Albert graduated with a Ph.D. from UC Berkeley and is now a postdoctoral fellow at UW Madison. Alex graduated with a Ph.D. from Texas A&M.