University of Texas at Austin
Qi Lei is a PhD candidate of Oden Institute for Computational Engineering & Sciences at UT Austin, where she is also a member of the Center for Big Data Analytics and the Wireless Networking & Communications Group. Currently she is visiting Institute for Advanced Studies (IAS) for the Theoretical Machine Learning Program. Before that, she was a research fellow at Simons Institute for the Foundations of Deep Learning Program. Her main research interests are in machine learning, deep learning and optimization. She received her B.S. degree from Zhejiang University, and master degree from UT Austin. Qi has received several awards, including four years of the National Initiative for Modeling and Simulation Graduate Research Fellowship, and Simons-Berkeley Research Fellowship for 2019 summer. She also owns several patents.
Currently there is a significant effort in both academia and industry for scalable machine learning, which also aligns with my interests. I believe most promising outcomes of data science involve a large amount of data, especially of very high dimensions. Therefore I care about developing novel and scalable algorithms that provably exploit the underlying problem structure such as sparsity or low-rank properties for big data. For deep learning models, I specifically also care about stabilizing their training process via designing better network architecture, as well as improving and understanding its adversarial robustness. I’m recently more interested in understanding the dynamics of minimax problems with applications of training deep generative models and adversarial training.