Qi Lei (雷琦)

Qi Lei UT Austin 

Assistant Professor of Mathematics and Data Science, and, by courtesy,
Assistant Professor of Computer Science,
Member of CILVR lab,
Member of Math and Data,
Courant Institute of Mathematical Sciences and Center for Data Science,
New York University

Research Overview

My research interests are machine learning, deep learning, and optimization. Specifically, I am interested in developing sample- and computationally efficient algorithms for some fundamental machine learning problems.

(Curriculum Vitae, Github, Google Scholar)


I am actively looking for self-motivated and proactive students to work with. You are welcome to shoot me an email with your CV and short research plans/interests. (You may refer to this link to see whether our research interests match.)

For prospective students or interns who want to work with me in short term, please fill out this form so that we could find a suitable project for you.

For prospective post-doc applicants, I encourage you to apply for the positions of CDS Faculty Fellows, Courant Instructors, and Flatiron Research Fellows.

News and Announcement

04/2023 Paper accepted at ICML 2023:

04/2023 Won the NYU Research Catalyst Prize jointly with Zhengyuan Zhou

01/2023 Four papers accepted at AISTATS 2023:

01/2023 Invited talk at SLowDNN on reconstruction attack with guarantees

12/2022 Organized the workshop on meta-learning at NeurIPS 2022

09/2022 Presented the a unified view on reconstruction-based and similarity-based self-supervised learning in SIAM-MDS

09/2022 Started a new journey as an assistant professor of Courant Math/CDS at NYU!

05/2022 Presented my recent work on handling distribution shifts and won best poster award at New Advances in Statistics and Data Science

Selected Papers

(full publication list)

7. Zihan Wang, Jason Lee, Qi Lei. “Reconstructing Training Data from Model Gradient, Provably”, AISTATS 2023

6. Baihe Huang*, Kaixuan Huang*, Sham M. Kakade*, Jason D. Lee*, Qi Lei*, Runzhe Wang*, and Jiaqi Yang*. “Optimal Gradient-based Algorithms for Non-concave Bandit Optimization”, NeurIPS 2021

5. Jason D. Lee*, Qi Lei*, Nikunj Saunshi*, Jiacheng Zhuo*. “Predicting What You Already Know Helps: Provable Self-Supervised Learning”, NeurIPS 2021

4. Simon S. Du*, Wei Hu*, Sham M. Kakade*, Jason D. Lee*, Qi Lei*. “Few-Shot Learning via Learning the Representation, Provably”, The International Conference on Learning Representations (ICLR) 2021

3. Qi Lei, Jason D. Lee, Alexandros G. Dimakis, Constantinos Daskalakis. “SGD Learns One-Layer Networks in WGANs”, Proc. of International Conference of Machine Learning (ICML) 2020

2. Qi Lei*, Lingfei Wu*, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, Michael Witbrock. “Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification”, Systems and Machine Learning (sysML). 2019 (code, slides)

1. Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis, “Gradient Coding: Avoiding Stragglers in Distributed Learning”, Proc. of International Conference of Machine Learning (ICML), 2017 (code)