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 methods have wide applications in revenue management, signal processing, distributed computing, etc.
A main focus of Wang’s research is data integration for learning and decision-making. This is a methodology for solving new tasks based on limited direct information and rich auxiliary data from other sources. Their unknown relevance and reliability, distributed storage, and data privacy requirements pose significant challenges. Wang 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.
