- Ph.D., Electrical Engineering, Stanford University, 2015
- Ph.D. minor, Management Science and Engineering, Stanford University, 2015
- M.S., Statistics, Stanford University, 2013
- M.S., Electrical and Computer Engineering, UT Austin, 2010
- B.S., Microelectronics, Tsinghua University, 2008
Assistant Professor of Electrical and Computer Engineering
Associated Faculty in Computer Science
Associated Faculty in Program in Applied and Computational Mathematics (PACM)
Yuxin Chen received a Ph.D. in Electrical Engineering from Stanford University in 2015, an M.S. in Statistics from Stanford University in 2013, an 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 high-dimensional estimation, machine learning, convex and nonconvex optimization, information theory, statistics, statistical signal processing, network science, and their applications in medical imaging and computational biology.
- 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.
- 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.
- 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.
- 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.
- 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.
Honors and Awards:
- AFOSR Young Investigator Program (YIP) Award, 2019