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. I plan to admit 2-3 Ph.D. students starting Fall 2023. 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.

News and Announcement

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

04/2022 Invited talk on Theoretical foundations of Pre-trained Models at AlgML seminar in Princeton

04/2022 Invited talk at Dartmouth ACMS

03/2022 New papers out:

02/2022 Invited talk at Adversarial Approaches in Machine Learning workshop at Simons Institute

Selected Papers

(full publication list)

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

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”, to appear at NeurIPS 2021

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