Princeton University

School of Engineering & Applied Science

Yuxin Chen

Assistant Professor of Electrical Engineering

Associated Faculty in Computer Science

Associated Faculty in Applied and Computational Mathematics


Room: B316 Engineering Quadrangle
Phone: 609-258-3996
Email: yuxin.chen@princeton.edu
Webpage: Yuxin Chen Group

Education

  • PhD, Electrical Engineering, Stanford University, 2015
  • PhD minor, Management Science and Engineering, Stanford University, 2015
  • MS, Statistics, Stanford University, 2013
  • MS, Electrical and Computer Engineering, UT Austin, 2010
  • BS, Microelectronics, Tsinghua University, 2008

Yuxin Chen received the Ph.D. degree in Electrical Engineering from Stanford University in 2015, the M.S. in Statistics from Stanford University in 2013, the M.S. in Electrical and Computer Engineering from the University of Texas at Austin in 2010, and the B.S. in Microelectronics from Tsinghua University in 2008. Before joining Princeton University, he was a postdoctoral scholar in the Department of Statistics at Stanford University from 2015 to 2017. His research interests include mathematics of data science, high-dimensional statistics, convex and nonconvex optimization, statistical learning, and information theory, and their applications to medical imaging and computational biology. 

Honors and Awards

  • AFOSR Young Investigator Program (YIP) Award, 2019

Selected Publications

  1. Y. Chen and E. J. Candes, “Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems,”Communications on Pure and Applied Mathematics, vol. 70, issue 5, pp. 822-883, May 2017.

  2. Y. Chen and E. J. Candes, “The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences,” Communications on Pure and Applied Mathematics, vol. 71, issue 8, pp. 1648-1714, August 2018.

  3. C. Ma, K. Wang, Y. Chi, Y. Chen, “Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution,” arXiv preprint arXiv:1711.10467, 2017. 

  4. Y. Chen, Y. Chi, J. Fan, C. Ma, “Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval,” accepted to Mathematical Programming, 2018.

  5. Y. Chen, J. Fan, C. Ma, K. Wang, “Spectral Method and Regularized MLE Are Both Optimal for Top-K Ranking,” Annals of Statistics, vol. 47, no. 4, pp. 2204-2235, August 2019. 

  6. Y. Chen and Y. Chi, "Robust Spectral Compressed Sensing via Structured Matrix Completion," IEEE Transactions on Information Theory, vol. 60, no. 10, pp. 6576 - 6601, Oct. 2014.

  7. Y. Chen, L. Guibas, and Q. Huang, "Near-Optimal Joint Object Matching via Convex Relaxation," International Conference on Machine Learning (ICML), June 2014.

  8. Y. Chen, Y. Chi, and A. J. Goldsmith, "Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming," IEEE Trans. on Info. Theory, vol. 61, no. 7, pp. 4034 - 4059, July 2015.