Zhaonan Qu

Zhaonan Qu is a Research Fellow at Columbia University's Data Science Institute, working on econometrics, optimization, and machine learning. His research develops methods for causal inference, network analysis, and efficient statistical estimation.

Key contributions include optimal preconditioning techniques, robust instrumental variables estimation, and scalable algorithms for large-scale choice modeling. Recent work connects matrix balancing with discrete choice theory and develops network inference methods using iterative proportional fitting.

Qu holds a PhD in Economics from Stanford University, where he was advised by Guido Imbens and Yinyu Ye. Current projects address distributionally robust optimization and computational challenges in high-dimensional econometrics.