- 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
Visiting Research Scholar
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, Y. Chi, J. Fan, C. Ma, “Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval,” Mathematical Programming, vol. 176, no. 1-2, pp. 5-37, July 2019.
- Y. Chen, J. Fan, C. Ma, Y. Yan, "Inference and Uncertainty Quantification for Noisy Matrix Completion," Proceedings of the National Academy of Sciences (PNAS), vol. 116, no. 46, pp. 22931–22937, Nov. 2019.
- 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,” Foundations of Computational Mathematics, vol. 20, no. 3, pp. 451-632, June 2020.
- Y. Chen, Y. Chi, J. Fan, C. Ma, "Spectral Methods for Data Science: A Statistical Perspective," Foundations and Trends in Machine Learning, vol. 14, no. 5, pp. 566–806, 2021.
Honors and Awards:
- Alfred P. Sloan Research Fellowship, 2022
- Princeton SEAS Junior Faculty Award, 2021
- Princeton Graduate Mentoring Award, 2020
- ICCM Best Paper Award (Gold Medal), 2020
- ARO Young Investigator Program (YIP) Award, 2020
- Finalist for the Best Paper Prize for Young Researchers in Continuous Optimization, 2019
- AFOSR Young Investigator Program (YIP) Award, 2019