Yuxin Chen. B316 Engineering Quad, Princeton, NJ 08544, United States Homepage:

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1 Yuxin Chen B316 Engineering Quad, Princeton, NJ 08544, United States Homepage: Appointments 02/2017 Present 06/2017 Present 08/2017 Present Assistant Professor, Electrical Engineering, Princeton University Associated Faculty, Computer Science, Princeton University Associated Faculty, Center for Statistics and Machine Learning, Princeton University Education Stanford University Statistics (Postdoc) 01/ /2017 Advisor: Prof. Emmanuel J. Candès Electrical Engineering (Ph. D.) 06/ /2015 Advisor: Prof. Andrea J. Goldsmith Thesis: Subsampling in Information Theory and Data Processing Statistics (Master of Science) 04/ /2013 Management Science and Engineering (Ph.D. Minor) 06/ /2015 University of Texas at Austin Electrical and Computer Engineering (Master of Science) 08/ /2010 Advisor: Prof. Jeffrey G. Andrews Tsinghua University (Bachelor of Engeneering) Microelectronics 08/ /2008 Electronic Engineering 08/ /2006 Graduated with High Distinction Research Interests Convex and nonconvex optimization, high-dimensional statistics, machine learning, information theory, statistical signal processing, network science, and their applications to medical imaging and computational biology Journal Articles J1. C. Ma and K. Wang, Y. Chi, Y. Chen, Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly in Phase Retrieval, Matrix Completion, and Blind Deconvolution, J2. Y. Chen, J. Fan, C. Ma and K. Wang, Spectral Method and Regularized MLE Are Both Optimal for Top-K Ranking, J3. P. Sur, Y. Chen, and E. J. Candes, The Likelihiood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square, J4. Y. Chen, and E. J. Candes, The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences, accepted to Communications on Pure and Applied Mathematics, J5. 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, no. 5, pp , May 2017.

2 J6. T. Zhang, Y. Chen, S. Bao, M. Alley, J. M. Pauly, B. Hargreaves, S. S. Vasanawala, Resolving phase ambiguity in dual-echo Dixon imaging using a projected power method, Magnetic Resonance in Medicine, vol. 77, no. 5, pp , May J7. Y. Chen, A. J. Goldsmith and Y. C. Eldar, Minimax Capacity Loss under Sub-Nyquist Universal Sampling, IEEE Transactions on Information Theory, vol. 63, no. 6, pp , June J8. Y. Chen, C. Suh and A. J. Goldsmith, Information Recovery from Pairwise Measurements, IEEE Transactions on Information Theory, vol. 62, no. 10, pp , Oct J9. Y. Chen, Y. Chi and A. J. Goldsmith, Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming, IEEE Transactions on Information Theory, vol. 61, no. 7, pp , July J10. Y. Chi, Y. Chen, Compressive Two-Dimensional Harmonic Retrieval via Atomic Norm Minimization, IEEE Transactions on Signal Processing, vol. 63, no. 4, pp , Feb J11. T. Zhang, J. Y. Cheng, Y. Chen, D. G. Nishimura, J. M. Pauly, and S. S. Vasanawala, Robust Self- Navigated Body MRI Using Dense Coil Arrays, Magnetic Resonance in Medicine, vol. 76, no. 1, pp , J12. Y. Chen, A. J. Goldsmith and Y. C. Eldar, Backing off from Infinity: Performance Bounds via Concentration of Spectral Measure for Random MIMO Channels, IEEE Transactions on Information Theory, vol. 61, no. 1, pp , January J13. Y. Chen, and Y. Chi, Robust Spectral Compressed Sensing via Structured Matrix Completion, IEEE Transactions on Information Theory, vol. 60, no. 10, pp , Oct J14. Y. Chen, A. J. Goldsmith and Y. C. Eldar, Channel Capacity under Sub-Nyquist Nonuniform Sampling, IEEE Transactions on Information Theory, vol. 60, no. 8, pp , Aug J15. Y. Chen, Y. C. Eldar and A. J. Goldsmith, Shannon Meets Nyquist: Capacity of Sampled Gaussian Channels, IEEE Transactions on Information Theory, vol. 59, no. 8, pp , Aug J16. Y. Chen, S. Shakkottai and J. G. Andrews, On the Role of Mobility for Multimessage Gossip, IEEE Transactions on Information Theory, vol. 59, no. 6, pp , June J17. Y. Chen and J. G. Andrews, An Upper Bound on Multi-hop Transmission Capacity with Dynamic Routing Selection, IEEE Transactions on Information Theory, vol. 58, no. 6, pp , June Conference Papers C1. Y. Chen, G. Kamath, C. Suh, and D. Tse, Community Recovery in Graphs with Locality, International Conference on Machine Learning (ICML), pp , New York, June C2. Y. Chen, E. J. Candes, Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems, Advances in Neural Information Processing Systems (NIPS), Montreal, Dec (oral, acceptance rate 0.8%). C3. Y. Chen, C. Suh, Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons, International Conference on Machine Learning (ICML), pp , Lille, July 2015 (finalist for the Bell Labs Prize). C4. Y. Chen, C. Suh and A. J. Goldsmith, Information Recovery from Pairwise Measurements: A Shannon- Theoretic Approach, International Symposium on Information Theory (ISIT), pp , Hongkong, June C5. Y. Chen, L. Guibas and Q. Huang, Near-Optimal Joint Object Matching via Convex Relaxation, International Conference on Machine Learning (ICML), pp , Beijing, June C6. Q. Huang, Y. Chen, and L. Guibas, Scalable Semidefinite Relaxation for Maximum A Posterior Estimation, International Conference on Machine Learning (ICML), pp , Beijing, June C7. Y. Chen, and A. J. Goldsmith, Information Recovery from Pairwise Measurements, International Symposium on Information Theory (ISIT), pp , Honolulu, Hawaii, July C8. Y. Chen, Y. Chi, and A. J. Goldsmith, Robust and Universal Covariance Estimation from Quadratic Measurements via Convex Programming, International Symposium on Information Theory (ISIT), pp , Honolulu, Hawaii, July 2014.

3 C9. Y. Chen, Y. Chi and A. J. Goldsmith, Estimation of Simultaneously Structured Covariance Matrices from Quadratic Measurements, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp , Florence, Italy, May C10. Y. Chen, Y. C. Eldar and A. J. Goldsmith, An Algorithm for Exact Super-resolution and Phase Retrieval, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp , Florence, Italy, May C11. Y. Chen, and Y. Chi, Compressive Harmonic Retrieval via Matrix Completion, Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lausanne, Switzerland, July 2013 (highlighted talk, finalist of Best Paper Award). C12. Y. Chen, A. J. Goldsmith, and Y. C. Eldar, Minimax Universal Sampling for Compound Multiband Channels, IEEE International Symposium on Information Theory (ISIT), pp , Istanbul, Turkey, July C13. Y. Chen, and Y. Chi, Spectral Compressed Sensing via Structured Matrix Completion, International Conference on Machine Learning (ICML), pp , Atlanta, Georgia, June 2013 (plenary oral, acceptance rate 12%). C14. Y. Chen, Y. C. Eldar, and A. J. Goldsmith, Channel Capacity under General Nonuniform Sampling, IEEE International Symposium on Information Theory (ISIT), pp , Cambridge, MA, July C15. Y. Chen, Y. C. Eldar and A. J. Goldsmith, Shannon Meets Nyquist: Capacity Limits of Sampled Analog Channels, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp , Prague, Czech Republic, May C16. Y. Chen, S. Shakkottai and J. G. Andrews, Sharing Multiple Messages over Mobile Networks, IEEE Infocom, pp , Shanghai, China, April 2011 (full length, acceptance rate 15%). C17. Y. Chen and S. Sanghavi, A General Framework for High-dimensional Estimation in the Presence of Incoherence, Allerton Conference on Communication, Control, and Computing, pp , Monticello, IL, Sep C18. Y. Chen and J. G. Andrews, An Upper Bound on Multi-hop Transmission Capacity with Dynamic Route Selection, IEEE Symposium on Information Theory (ISIT), pp , Austin, TX, June Invited Talks T1. Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval, Oberwolfach Workshop on Applied Harmonic Analysis and Data Processing, Oberwolfach, Mar T2. Implicit Regularization in Nonconvex Statistical Estimation, Information Theory and Its Applications (ITA) Workshop, San Diego, Feb T3. Implicit Regularization in Nonconvex Statistical Estimation, Data Science Seminar series, Institute for Mathematics and its Applications (IMA), Jan T4. Implicit Regularization in Nonconvex Statistical Estimation, International Conference on Data Science, Shanghai, Dec T5. Implicit Regularization in Nonconvex Statistical Estimation, Signal Processing and Communications Seminar Series, University of Delaware, Dec T6. Implicit Regularization in Nonconvex Statistical Estimation, Simons Institute Workshop on Optimization, Statistics and Uncertainty, Berkeley, Nov T7. Implicit Regularization in Nonconvex Statistical Estimation, 51th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Oct T8. Implicit Regularization in Nonconvex Statistical Optimization, Statistics Seminar, Columbia University, Oct T9. Spectral Method and Regularized MLE Are Both Optimal for Top-K Ranking, Joint Statistical Meetings, Baltimore, August T10. The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences, Meeting of the International Linear Algebra Society, Ames, July 2017.

4 T11. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems, ShanghaiTech Symposium on Information Science and Technology (SSIST), Shanghai, July T12. The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences, SIAM Conference on Optimization, Vancouver, May T13. The Projected Power Method: A Nonconvex Algorithm for Discrete Problems, Electrical Engineering Seminar Series, Harvard University, Apr T14. The Effectiveness of Nonconvex Optimization in Two Problems, Statistics Seminar, NYU Stern School of Business, Mar T15. The Effectiveness of Nonconvex Optimization in Two Problems, IDeAS Seminar, Princeton University, Mar T16. The Projected Power Method: A Nonconvex Algorithm for Joint Alignment from Pairwise Differences, Information Theory and Applications Workshop, San Diego, Feb T17. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems, 50th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Nov T18. An Efficient Algorithm for Joint Alignment from Pairwise Differences, CMO-BIRS Workshop: Applied Harmonic Analysis, Massive Data Sets, Machine Learning, and Signal Processing, Oaxaca, Oct T19. An Efficient Algorithm for Joint Alignment from Pairwise Differences, 54th Annual Allerton Conference on Communication, Control, and Computing, Monticello, Sep T20. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems, World Congress in Probability and Statistics, Toronto, July T21. Modern Optimization Meets Physics: Recent Progress on Phase Retrieval, International Matheon Conference on Compressed Sensing and its Applications (CSA), Berlin, Dec T22. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems, Workshop on Sensing and Analysis of High-Dimensional Data (SAHD), Duke University, July T23. Near-Optimal Joint Object Matching via Convex Relaxation, IDeAS Seminar, Princeton University, Apr T24. Near-Optimal Joint Object Matching via Convex Relaxation, Information Initiative at Duke (iid) Seminar, Duke University, Apr T25. Near-Optimal Joint Object Matching via Convex Relaxation, Center for Signal and Information Processing (CSIP) Seminar, Georgia Tech, Mar Patents P1. Tao Zhang, Yuxin Chen, John M Pauly, Shreyas Vasanawala, Robust dual echo Dixon imaging with flexible echo times, US Provisional (licensed to Siemens Healthcare and GE Healthcare). P2. Tao Zhang, John M Pauly, Yuxin Chen, Joseph Cheng, and Shreyas Vasanawala, Robust Self-Navigating MRI Using Large Coil arrays, US 14/596,959, 2015 (licensed to GE, Siemens, and Philips). Teaching ELE538B (Large-Scale Optimization for Data Science), Princeton University, Spring 2018 ORF 570 / ELE 578 (Special Topics in Statistical Optimization and Reinforcement Learning, co-taught with Mengdi Wang), Spring 2018 ELE382 (Statistical Signal Processing), Princeton University, Fall 2017 ELE538B (Sparsity, Structure, and Inference), Princeton University, Spring 2017 Honors and Awards Princeton SEAS Innovation Award 2018 ELE538B (Sparsity, Structure, and Inference) is included in the Princeton Engineering Commendation List for Outstanding Teaching 2017

5 Finalist for the Bell Labs Prize 2015 Finalist of of Best Paper Award, SPARS 2013 Graduated with High Distinction, Tsinghua Univ Superior Excellence Award (top 3), Beijing Undergraduate Physics Competition 2005 Professional Service S1. Co-organizer of the workshop Bridging Mathematical Optimization, Information Theory, and Data Science at Princeton Center for Theoretical Science, May 2018.