Short BioQi Lei is an assistant professor of Mathematics and Data Science at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU. Previously she was an associate research scholar at the ECE department of Princeton University. She received her Ph.D. from Oden Institute for Computational Engineering & Sciences at UT Austin. She visited the Institute for Advanced Study (IAS)/Princeton for the Theoretical Machine Learning Program. Before that, she was a research fellow at Simons Institute for the Foundations of Deep Learning Program. Her research aims to develop sample- and computationally efficient machine learning algorithms and bridge the theoretical and empirical gap in machine learning. Qi has received several awards, including the Outstanding Dissertation Award, National Initiative for Modeling and Simulation Graduate Research Fellowship, Computing Innovative Fellowship, and Simons-Berkeley Research Fellowship. Research Interests:Modern Machine Learning (ML) models are transforming applications across various domains. Pushing the limits of their potential relies on training more complex models, using larger data sets, and persistent hyper-parameter tuning. This procedure requires sophisticated user experience, expensive equipment such as GPU machines, and extensive label annotations costs. These criteria leave machine learning exclusive to only specialized researchers and institutes. I aim to make machine learning more accessible to the general populace by developing efficient and easily trainable machine learning algorithms with low computational cost, fewer security concerns, and low requirement of labeled data. Over the past seven years, I have focused on bringing more theoretical ideas and principles to algorithm design towards efficient, robust, and few-shot machine learning algorithms. Curriculum Vitae:(Curriculum Vitae, Github, Google Scholar) EducationUnversity of Texas at Austin, Austin, TX Ph.D student in Institute for Computational Engineering and Sciences August 2014 - May 2020 Institute of Advanced Study, Princeton, NJ Visiting Graduate Student for the Special Year on Optimization, Statistics and Theoretical Machine Learning September 2019 - May 2020 Simons Institute, Berkeley, CA Research Fellow for the Foundations of Deep Learning Program May 2019 - August 2019 Zhejiang University, Zhejiang, China B.S. in Mathematics August 2010 - May 2014 Awards and Recognitions
ExperienceFacebook Visual Understanding Team, Menlo Park, CA Software Engineering Intern June 2018 - September 2018 Amazon/A9 Product Search Lab, Palo Alto, CA Software Development Intern, Search Technologies May 2017 - August 2017 Amazon Web Services (AWS) Deep Learning Team, Palo Alto, CA Applied Scientist Intern January 2017 - April 2017 IBM Thomas J. Watson Research Center, Yorktown Heights, NY Research Summer Intern May 2016 - October 2016 UCLA Biomath Department, Los Angeles, CA Visiting Student July 2013 - September 2013 Invited Talk‘‘Optimal Gradient-based Algorithms for Non-concave Bandit Optimization."
‘‘ Few-Shot Learning via Learning the Representation, Provably.’’
‘‘Predicting What You Already Know Helps: Provable Self-Supervised Learning.’’
‘‘Provable representation learning.’’
‘‘SGD Learns One-Layer Networks in WGANs.’’
‘‘Deep Generative models and Inverse Problems.’’
‘‘Similarity Preserving Representation Learning for Time Series Analysis.’’
‘‘Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification.’’
‘‘Recent Advances in Primal-Dual Coordinate Methods for ERM.’’
‘‘Coordinate Descent Methods for Matrix Factorization.’’
ServiceConference Reviewer: MLSys (19,20,Meta-reviewer’21, TPC’22), COLT (21,22), STOC (20), NeurIPS (16,17,18,19,20,21), ICML (18,19,20,21), ICLR (18,19,20,21), AISTATS (18,19,20,21), AAAI (20,21), ACML (19), and more Journal Reviewer: JSAIT(20), MOR (18,19,20), TNNLS (19,20), TKDE (19), ISIT (17,18), TIIS (17), IT (16,17), and more TeachingTheory of Deep Learning: Representation and Weakly Supervised Learning, Teaching Assistant, Fall 2020 Scalable Machine Learning, Teaching Assistant, Fall 2019 Mathematical Methods in Applied Engineering and Sciences, Instructer Intern, Spring 2016 |