Zhaonan Qu

Welcome to my personal website!

I recently received my PhD in January 2024 in the Department of Economics at Stanford University, where my advisor was Professor Guido Imbens. I was also mentored by Professor Alfred Galichon, Professor Han Hong, Professor Johan Ugander, and Professor  Yinyu Ye.

My research interests include econometrics, causal inference, optimization, data science, and particularly the interactions between them in theoretical, empirical, and policy questions.

Here is my CV.

Select Works (* denotes co-first authors)

Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting with Serina Chang*, Frederic Koehler*, Jure Leskovec, and Johan Ugander. 41st International Conference on Machine Learning (ICML)  (2024).

On Sinkhorn's Algorithm and Choice Modeling with Alfred Galichon and Johan Ugander. Under Review (2023).

Computationally Efficient Estimation of Large Probit Models with Patrick Ding, Guido Imbens, and Yinyu Ye. Under Revision (2023).

Optimal Diagonal Preconditioning with Wenzhi Gao*, Oliver Hinder, Yinyu Ye, and Zhengyuan Zhou. Operations Research (2024).

A Unified Linear Speedup Analysis of Stochastic FedAvg and Nesterov Accelerated FedAvg with Kaixiang Lin*, Zhaojian Li, Jiayu Zhou, and Zhengyuan Zhou. Journal of Artificial Intelligence Research 78: 1143-1200 (2023). 

Ensemble Methods for Causal Effects in Panel Data Settings with Susan Athey, Mohsen Bayati, and Guido Imbens. American Economic Association Papers and Proceedings 109: 65-70 (2019).