معرفی
Qi Lei is an Assistant Professor of Mathematics and Data Science at New York University's Courant Institute of Mathematical Sciences and Center for Data Science. By courtesy, they also hold an Assistant Professor position in Computer Science. Dr. Lei is a member of the CILVR lab and Math and Data groups, and serves as a Google DeepMind Faculty.
Dr. Lei's research focuses on machine learning, deep learning, and optimization, with particular interest in developing sample- and computationally efficient algorithms for fundamental machine learning problems. Their recent work spans several key areas:
- Data and Model Pruning
- Data Reconstruction Attack and Defense
- Theoretical Foundations of Pre-trained Models
Analysis of Dr. Lei's recent publications reveals a strong focus on theoretical foundations of machine learning, particularly around data efficiency, model robustness, and privacy. Their work bridges theoretical guarantees with practical applications, demonstrating expertise in both the mathematical underpinnings of learning algorithms and their real-world implementation. A notable trend is the increasing focus on privacy-preserving machine learning and defenses against data reconstruction attacks.
Dr. Lei is actively involved in academic service, having organized the minisymposium "Efficient Computation and Learning with Randomized Sampling and Pruning" at SIAM MDS 2024 and delivered invited talks at prestigious venues including IMS@NUS, ICSDS, Harvard Statistics, and workshops on Data-driven PDE-based inverse problems and Large Language Models.
As an educator and mentor, Dr. Lei welcomes self-motivated students to collaborate on research projects. They advise prospective Ph.D. applicants to apply through Courant Mathematics or the Center for Data Science programs at NYU, where they are actively involved in supervising graduate research.
