About MeI am a postdoctoral researcher in the Department of Statistics and the School of Engineering and Applied Sciences at Harvard University. I am fortunate to work with Prof. Subhabrata Sen and Prof. Yue M. Lu. My research interests lie in the mathematical foundations of data science, high-dimensional statistics, information theory, signal processing, and applied probability. I am particularly interested in understanding phase transitions, universality phenomena, and computational-statistical trade-offs in modern high-dimensional inference problems. Previously, I was a Ph.D. student at the Statistics Department at Columbia University, where I was fortunate to be advised by Prof. Arian Maleki and Prof. Daniel Hsu. You can find more information about me in my CV. Email: Office:
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Publications |
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Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing Matrices.
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Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity.
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Universality of Approximate Message Passing with Semi-Random Matrices.
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Universality of Linearized Message Passing for Phase Retrieval with Structured Sensing Matrices.
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Statistical Query Lower Bounds for Tensor PCA.
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Spectral Method for Phase Retrieval: an Expectation Propagation Perspective.
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Information Theoretic Limits for Phase Retrieval with Subsampled Haar Sensing Matrices.
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Analysis of Spectral Methods for Phase Retrieval with Random Orthogonal Matrices.
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Attribute-efficient learning of monomials over highly-correlated variables.
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Learning single-index models in Gaussian space.
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Teaching |
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Currently, I am a Teaching Fellow for Mathematics of High-dimensional Information Processing and Learning (AM 254), taught by Prof. Yue M. Lu. Previously, I served as a TA for the following courses at Columbia:
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