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. The methodologies have wide applications in signal processing, network analysis, recommendation systems, distributed computing, etc. He is also interested in uncertainty quantification and robustness certification for complex systems.
A main focus of Wang’s research is efficient extraction of key structures from high-dimensional data, which greatly reduces complexity and enhances interpretability. This includes dimensionality reduction, representation learning, clustering, ranking and other weakly supervised machine learning problems where labeled data are scarce and difficult to obtain. He leverages cutting-edge tools in optimization, statistics, and related fields to design principled approaches that faithfully output high-quality solutions.
Before coming to Columbia University, Wang received his PhD in Operations Research and Financial Engineering from Princeton University in 2020 and his BS in Mathematics from Peking University in 2015.