Kaizheng Wang
Assistant Professor of Industrial Engineering and Operations Research
Kaizheng Wang works at the intersection of machine learning, statistics and optimization. He develops and studies scalable algorithms for analyzing massive data that are unstructured, incomplete, and heterogeneous.
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.
Research Areas
- Data Analytics
- Decision Analysis
- Optimization
- Risk Management
- Stochastic Modeling and Simulation
- Business Analytics
- Large-scale Optimization
Additional information
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Professional Experience
- Assistant Professor of Industrial Engineering and Operations Research, Columbia University, 2020–
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Honors & Awards
- Tweedie New Researcher Award, Institute of Mathematical Statistics, 2026
- Frontiers of Science Award in Mathematics, International Congress of Basic Science, 2024
- Best Paper Prize, SIAM Activity Group on Imaging Science, 2024
- Second Place Award, INFORMS Data Mining Challenge, 2023
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Education
- PhD, Operations Research, Princeton University
- BS, Mathematics, Peking University