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. I will be on the 2024-2025 job market.

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).

A Distributionally Robust Instrumental Variable Estimation Framework with Yongchan Kwon*. Under Revision (2023).

On Sinkhorn's Algorithm and Choice Modeling with Alfred Galichon and Johan Ugander. Major Revision at Operations Research (2023).

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

Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference with Ruoxuan Xiong*, Jizhou Liu, and Guido Imbens. Under Review (2021).

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).