Yuqing Wang

ywan1050@jh.edu

CV·Google Scholar

I am a postdoc in the AMS department at JHU. Before that, I was a Research Fellow at the Simons Institute at UC Berkeley for the MPG program in fall 2024. I obtained my PhD in Mathematics at Georgia Institute of Technology, advised by Prof. Molei Tao.

I am currently on the job market.

Research

I am interested in developing the mathematical foundations of deep learning. My research centers on characterizing training dynamics, combining tools from optimization, dynamical systems, computational math, and analysis.

General theoretical tools for deep learning:

• Optimization:
  • Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime
• Generalization:
  • A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay with Ioannis G. Kevrekidis, Mikhail Belkin

Specific learning mechanisms, models, and data:

• Implicit biases of large learning rates:
  • Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult with Zhenghao Xu, Tuo Zhao, Molei Tao
  • Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect with Minshuo Chen, Tuo Zhao, Molei Tao
• Model designs:
  • Evaluating the design space of diffusion-based generative models with Ye He, Molei Tao
  • Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? — A Neural Tangent Kernel Perspective with Kaixuan Huang, Molei Tao, Tuo Zhao
• Data:
  • Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime with Shangding Gu

Preprints and Publications

Recent talks

• 01/2026: MPG reunion workshop, Simons Institute
• 01/2026: JMM
• 11/2025: SIAM NNP
• 07/2025: Optimization Meets Generative AI: Insights and New Designs, International Conference on Continuous Optimization (ICCOPT)
• 05/2025: Math Machine Learning seminar, MPI MiS + UCLA
• 05/2025: Dynamical Systems for Machine Learning (MS141), SIAM Conference on Applications of Dynamical Systems (DS25)
• 04/2025: CMX seminar, Caltech
• 04/2025: AMS postdoc seminar, JHU

Teaching

Instructor in AMS department, JHU:

• EN 553.432 Bayesian Statistics
  Fall 2025, Fall 2026
• EN 553.361 Introduction to Optimization I
  Spring 2025, Spring 2026

Teaching Assistant in School of Mathematics, Georgia Tech:

• MATH 2552 Differential Equation
  Fall 2018, Spring 2019, Fall 2019, Fall 2020, Spring 2021, Spring 2022, Spring 2023, Fall 2023
• MATH 2550 Introduction to Multivariable Calculus
  Summer 2019, Summer 2024
• MATH 2551 Multivariable Calculus
  Spring 2020