Industrial Engineering and Operations Research
Kaizheng Wang works at the intersection of optimization, machine learning, and statistics. He develops and studies scalable algorithms for analyzing massive data that are unstructured, incomplete, and heterogeneous.
- Assistant Professor of Industrial Engineering and Operations Research, Columbia University, 2020–
- Harold W. Dodds Fellowship, Princeton University, 2019
- Abbe, E., Fan, J., Wang, K., & Zhong, Y. Entrywise eigenvector analysis of random matrices with low expected rank. The Annals of Statistics, 2020+.
- Ma, C., Wang, K., Chi, Y., & Chen, Y. Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval, matrix completion and blind deconvolution. Foundations of Computational Mathematics, 20, 451–632 (2020).
- Fan, J., Wang, D., Wang, K., & Zhu, Z. Distributed estimation of principal eigenspaces. The Annals of Statistics 47 (6): 3009-3031, 2019.
- Chen, Y., Fan, J., Ma, C., & Wang, K. Spectral method and regularized MLE are both optimal for Top-K ranking. The Annals of Statistics 47 (4): 2204-2235, 2019.