I am Gordon McKay Professor of Electrical Engineering and of Applied Mathematics at the Harvard John A. Paulson School of Engineering and Applied Sciences.
My research interests include theoretical and algorithmic aspects of high-dimensional signal and information processing. In the past, I have also worked on imaging, multidimensional sampling theory, multiscale geometrical representations, and image processing.
News
- (09/26/20) NeurIPS paper: High-dimensional perceptrons: Approaching Bayes error with convex optimization
- (09/17/20) New paper: Universality Laws for High-Dimensional Learning with Random Features
- (08/28/20) New paper: A Precise Performance Analysis of Learning with Random Features
- (06/16/20) New paper: The limiting Poisson law of massive MIMO detection
- (06/02/20) ICML paper: The role of regularization in classification of high-dimensional Gaussian mixture
- (09/04/19) NeurIPS paper: A solvable high-dimensional model of GAN
- (05/01/19) ICML paper: Approximate survey propagation for high-dimensional estimation
- (03/28/19) New paper: Asymptotics and optimal designs of SLOPE
- (11/08/18) New paper: Optimal spectral method for high-dimensional signal estimation
- (10/19/18) Elected to the Big Data Special Interest Group (SIG) of the IEEE Signal Processing Society
- (09/26/18) New paper: Nonconvex optimization meets low-rank matrix factorization
- (05/27/18) Phase retrieval via polytope optimization: Geometry, phase transitions, and new algorithms
- (05/08/18) Subspace estimation from incomplete observations: a high dimensional analysis
- (05/02/18) Paper to appear at the Proceedings of the IEEE (Streaming PCA and subspace tracking: the missing data case)
- (04/16/18) ICASSP tutorial on nonconvex methods for high-dimensional statistical estimation (slides available online)
- (12/11/17) Best Student Paper Award (First Prize) at IEEE CAMSAP
- (12/06/17) Understanding the Dynamics of Online Learning Algorithms via Scaling and Mean-Field Limits
- (09/12/17) The Scaling Limit of High-Dimensional ICA (NIPS spotlight)
- (08/15/17) Fundamental Limits of PhaseMax for Phase Retrieval: A Replica Analysis
- (04/03/17) Exact high-dimensional analysis of subspace learning from highly incomplete information