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. After graduating from UC Davis, she went to Stanford University as a postdoc, then became Professor of Mathematics at Claremont McKenna College. Starting from 2017, Deanna has been Professor of Mathematics at UCLA. Deanna won numerous awards for her work, including Sloan Research Fellowship, NSF CARREER Award, IEEE Best Young Author Paper Award, and 2016 IMA Prize in Mathematics and its Applications.
Ph.D. in Statistics, University of Michigan, 2012-2016. Can was co-advised by Professor Elizaveta Levina in the department of Statistics and me. Can's thesis was on the analysis of challenging, sparse complex networks. After graduating from University of Michigan, Can became Professor of Statistics at UC Davis.
Ph.D. in Mathematics, University of Michigan, 2014-2018 (expected). Elizaveta (Liza) is working in random matrix theory. She is studying the invertibility and boundedness of random matrices in the challenging regime, where very little is assumed about the distribution of the entries. Elizaveta is going to UCLA for a postdoctoral position.
Ph.D. in Mathematics, University of Michigan, 2015-2018 (expected). Yan Shuo is working in mathematical data science. He is advancing methods to detect meaningful low-dimensional structures in high-dimensional data. Yan Shuo is going to UC Berkeley for a postdoctoral position.
Ph.D. in Mathematics, University of California, Irvine, 2014-2020 (expected). Jennifer is co-advised with Prof. Hongkai Zhao. She is developing a theory of "random matrices of objects", where objects can be numbers, vectors, images, texts, etc. The motivation comes from data science problems, in which one needs to integrate heterogeneous data from various sources.
Yaniv was an NSF Postdoctoral Fellow and Hildebrandt Assistant Professor in Mathematics at University of Michigan during 2011-2014. Yaniv and I developed the first tractable algorithms for single-bit Compressed Sensing. We then extended this work to logistic regression and non-linear Lasso. Yaniv is now Professor of Mathematics at University of British Columbia.
Beatrice is 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.
M.S. in Financial Mathematics, University of Michigan, 2006-2009. Yuting worked with me on a few problems geometric functional analysis. After graduating from University of Michigan, she continued to Risk Management Solutions.
M.S. in Applied and Interdisciplinary Mathematics, University of Michigan, 2014-2015. Joe worked with me on non-linear inverse regression. After graduating from UM, 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 the journal Linear Algebra and Applications.
Matthew and Xinyan were my REU students in 2014, co-advised by Dr. Yaniv Plan and me. We developed a model for blood sugar levels in individuals with Type 1 Diabetes.