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Qing Qu
1301 Beal AvenueAnn Arbor, MI 48109-2122

Prof. Qing Qu uses data and machine learning to optimize the world

Qu’s research has applications in imaging sciences, scientific discovery, healthcare, and more.

I have been recognized as one of the best reviewers at NeurIPS’19, and invited as a mentor to the first new in ML workshop at NeurIPS

One paper has been accepted at NeurIPS’19 as spotlight (top 3%)

Our paper, titled A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution, has been accepted at NeurIPS’19 as spotlight (top 3%). This is a joint work with Xiao Li and Zhihui Zhu.

Two papers have been accepted at ICLR’20, with one oral presentation (top 1.85%)

Our paper, titled Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning has been accepted at ICLR’20 as oral (top 1.85%). Our paper, titled Short-and-Sparse Deconvolution – A Geometric Approach has been accepted at ICLR’20 as poster (acceptance rate 26.5%).

A new review paper has been submitted to IEEE Signal Processing Magazine

The paper, titled Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications, reviews recent work on nonconvex optimization methods for finding the sparsest vectors in linear subspaces. This is a joint work with Zhihui Zhu, Xiao Li, Manolis Tsakiris, John Wright and Rene Vidal.

Invited to be a speaker and organizer for Efficient Tensor Representations for Learning and Computational Complexity

The workshop will be held from May 17 – 21, 2021 at the Institute for Pure and Applied Mathematics (IPAM) situated on the UCLA campus. This workshop is part of a semester long program on Tensor Methods and Emerging Applications to the Physical and Data Sciences.

New paper submission

The paper, titled Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization, is submitted. This is a joint work with Chong You, Zhihui Zhu, and Yi Ma.

A review paper on nonconvex optimization is released

The paper, titled From Symmetry to Geometry: Tractable Nonconvex Problems, reviews recent advances on nonconvex optimization from a geometric perspective and landscape studies. This is a joint work with Yuqian Zhang and John Wright.