Hi, welcome to my homepage! I am Wenlong Ji, a third-year Ph.D. student at the Department of Statistics at Stanford University, advised by Prof. Lihua Lei. Previously, I received my bachelor’s degree from the School of Mathematical Sciences at Peking University. During my undergraduate study, I am fortunate to work with Prof. Bin Dong, Prof. Weijie Su, Prof. Linjun Zhang, and Prof. James Zou.

My research interest broadly lies in statistics, machine learning, and economics, focusing on establishing the theoretical foundation and developing statistical tools for real-world problems. If you are interested in any of my research or want to collaborate with me, please feel free to reach out!

Research Papers

(* indicates equal contribution)

1. Wenlong Ji*, Weizhe Yuan*, Emily J. Getzen, Kyunghyun Cho, Michael I. Jordan, Song Mei, Jason E Weston, Weijie Su, Jing Xu and Linjun Zhang. An Overview of Large Language Models for Statisticians. Submitted. link
2. Wenlong Ji*, Lihua Lei*, and Tijana Zrnic*. Predictions as surrogates: Revisiting surrogate outcomes in the age of AI. Submitted. link
3. Ian Covert*, Wenlong Ji*, Tatsunori Hashimoto, James Zou. Scaling Laws for the Value of Individual Data Points in Machine Learning. (ICML 2024).
4. Wenlong Ji*, Lihua Lei*, Asher Spector*. Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects. arXiv preprint arXiv:2310.08115, 2023. Submitted. link
5. Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang. Understanding multimodal contrastive learning and incorporating unpaired data. (AISTATS 2023). link
6. Yiping Lu, Wenlong Ji, Zachary Izzo, Lexing Ying. Importance Tempering: Group Robustness for Overparameterized Models. arXiv preprint arXiv:2209.08745, 2022. Under review. link
7. Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang. The Power of Contrast for Feature Learning: A Theoretical Analysis. (JMLR 2023). link
8. Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie Su. An Unconstrained Layer Peeled Perspective on Neural Collapse. (ICLR 2022). link