I am a postdoc fellow at the Simons Institute at UC Berkeley hosted by Peter Bartlett and Bin Yu. I am a part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning.
I obtained my Ph.D. in Computer Science at Johns Hopkins University, advised by Vladimir Braverman, and B.S. and M.S. at Peking University. I was selected as a 2023 Rising Star in Data Science by UChicago and UCSD.
Email | CV | arXiv | Google Scholar
My research focuses on bridging the gap between theory and practice in machine learning by developing efficient algorithms to solve real-world problems and providing a deep understanding of the underlying theoretical principles.
I work on the theory and algorithms of deep learning, and related topics in algorithms, machine learning, optimization, and statistical learning theory.
Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
arXiv 2025
Minimax Optimal Convergence of Gradient
Descent in Logistic Regression via Large and Adaptive Stepsizes
ICML 2025 | poster
Large Stepsize Gradient Descent for
Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
COLT 2024 |
poster
Implicit Bias of Gradient Descent for
Logistic Regression at the Edge of Stability
NeurIPS 2023 (spotlight) |
poster
Benefits of Early Stopping in Gradient
Descent for Overparameterized Logistic Regression
ICML 2025 |
poster
Finite-Sample Analysis of Learning
High-Dimensional Single ReLU Neuron
ICML 2023 |
poster
Last Iterate Risk Bounds of SGD with
Decaying Stepsize for Overparameterized Linear Regression
ICML 2022 (long presentation) |
poster
The Benefits of Implicit Regularization from SGD in Least Squares Problems
NeurIPS 2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
COLT 2021 (journal version in JMLR 2023)
Scaling Laws in Linear Regression:
Compute, Parameters, and Data
NeurIPS 2024
How Many Pretraining Tasks Are Needed
for
In-Context Learning of Linear Regression?
ICLR 2024 (spotlight) |
poster
The Power and Limitation of Pretraining-Finetuning for Linear Regression under
Covariate Shift
NeurIPS 2022 |
poster
Organizer
Conference Reviewer
ICML (2020 - 2025),
NeurIPS (2020 - 2025),
ICLR (2021 - 2025),
AISTATS (2021 - 2023),
UAI (2023)
Conference Program Committee Member
AAAI (2021 - 2023)
Journal Reviewer
JMLR,
TPAMI,
TMLR,
SIMODS,
JAIR,
Applied Probability Journals,
IEEE Transactions on Information
Theory,