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, highdimensional statistics, information theory, signal processing, and applied probability. I am particularly interested in understanding phase transitions, universality phenomena, and computationalstatistical tradeoffs in modern highdimensional 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:


Publications 
Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing Matrices.

StatisticalComputational Tradeoffs in Tensor PCA and Related Problems via Communication Complexity.

Universality of Approximate Message Passing with SemiRandom Matrices.

Universality of Linearized Message Passing for Phase Retrieval with Structured Sensing Matrices.

Statistical Query Lower Bounds for Tensor PCA.

Spectral Method for Phase Retrieval: an Expectation Propagation Perspective.

Information Theoretic Limits for Phase Retrieval with Subsampled Haar Sensing Matrices.

Analysis of Spectral Methods for Phase Retrieval with Random Orthogonal Matrices.

Attributeefficient learning of monomials over highlycorrelated variables.

Learning singleindex models in Gaussian space.


Teaching 
In Fall 2023, I was a TA for Mathematics of Highdimensional Information Processing and Learning (AM 254), taught by Prof. Yue M. Lu. Previously, I served as a TA for the following courses at Columbia:
