Yuxin Chen
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I am an assistant professor of Electrical Engineering, and an associated faculty member of Computer Science, Applied and Computational Mathematics, and the Center for Statistics and Machine Learning at Princeton University. Prior to joining Princeton in Spring 2017, I was a postdoctoral scholar in the Department of Statistics at Stanford University supervised by Prof. Emmanuel Candès. I completed my Ph.D. in Electrical Engineering at Stanford University in Fall 2014, under the supervision of Prof. Andrea Goldsmith. Research areas: mathematical foundations of data science, optimization, high-dimensional statistics, statistical learning, information theory, and their applications to medical imaging and computational biology. Contact: Email: yuxin dot chen at princeton dot edu |
Recent news
I am organizing a reading group on mathematical data science. Let me know if you are interested in attending and presenting.
Yuejie Chi and I gave a tutorial on ‘‘Taming Nonconvexity in Information Science’’ at ITW 2018.
I received the 2019 AFOSR Young Investigator Program (YIP) Award.
Mengdi Wang and I organized a workshop ‘‘Bridging Mathematical Optimization, Information Theory, and Data Science’’ at Princeton, May 2018.
Teaching
Fall 2019: ELE522: Large-Scale Optimization for Data Science
Fall 2018: ELE538: Mathematics of High-Dimensional Data (Princeton Engineering Commendation List for Outstanding Teaching)
Fall 2018: ELE382: Probabilistic Systems and Information Processing
Spring 2018: ELE538C: Large-Scale Optimization for Data Science (Princeton Engineering Commendation List for Outstanding Teaching)
Selected recent papers
Y. Chen, Y. Chi, J. Fan, C. Ma, Y. Yan, “Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization,” 2019. [paper][slides]
Y. Chen, C. Cheng, J. Fan, “Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices,” 2018. [paper][slides]
Y. Chi, Y. Lu, Y. Chen, “Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview,” accepted to IEEE Transactions on Signal Processing, 2018 (invited overview article). [paper][slides]
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. [paper][slides]
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,” 2017 (appeared in part in ICML 2018). [main text][supplement][full paper][slides]
Y. Chen, J. Fan, C. Ma, K. Wang, “Spectral Method and Regularized MLE Are Both Optimal for Top-K Ranking,” accepted to Annals of Statistics, 2017. [slides]
P. Sur, Y. Chen, E. J. Candes, “The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square,” accepted to Probability Theory and Related Fields, 2018. [slides][code]
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. [slides][code]
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 (appeared in part in NIPS 2015 (oral)). [website]