Jiaming Yang (杨佳明) Ph.D. Candidate in Computer Science University of Michigan, Ann Arbor Email: jiamyang [at] umich [dot] edu [Google Scholar]   [LinkedIn]   [CV]

About Me

I am a fourth-year Ph.D. student in Computer Science at the University of Michigan, where I am advised by Prof. Michał Dereziński. Prior to that, I received my bachelor's degree from the School of Mathematical Sciences at Peking University.

My current research focuses on randomized numerical linear algebra (RandNLA), with applications to large-scale optimization and machine learning. More broadly, I am interested in machine learning, statistics, and optimization, with a focus on their theoretical foundations.

Outside of research, I enjoy rock climbing (both indoor and outdoor), photography, and playing poker.

Research Interests

• Randomized Numerical Linear Algebra (RandNLA)

• Machine Learning Theory

• Large-Scale Optimization

Publications

α-β indicates alphabetical author order.

• Randomized Kaczmarz Methods with Beyond-Krylov Convergence
  (α-β) M. Dereziński, D. Needell, E. Rebrova, J. Yang
  SIAM Journal on Matrix Analysis and Applications, 2025
  [arXiv]
• Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
  P. Rathore, Z. Frangella, J. Yang, M. Dereziński, M. Udell
  Submitted, 2025
  [arXiv]
• Faster Linear Systems and Matrix Norm Approximation via Multi-Level Sketched Preconditioning
  (α-β) M. Dereziński, C. Musco, J. Yang
  Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2025
  [arXiv]
• Solving Dense Linear Systems Faster than via Preconditioning
  (α-β) M. Dereziński, J. Yang
  Proceedings of the 56th Annual ACM Symposium on Theory of Computing (STOC), 2024
  [arXiv]
• HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
  Y. Yang, J. Yang, W. Hu, M. Dereziński
  Mathematics of Modern Machine Learning (M3L, NeurIPS), 2024
  [arXiv]
• Federated Adversarial Learning: A Framework with Convergence Analysis
  (α-β) X. Li, Z. Song, J. Yang
  International Conference on Machine Learning (ICML), 2023
  [arXiv]
• Pixelated Butterfly: Simple and Efficient Sparse Training for Neural Network Models
  T. Dao, B. Chen, K. Liang, J. Yang, Z. Song, A. Rudra, C. Ré
  International Conference on Learning Representations (ICLR), 2022
  [arXiv]

Teaching

• EECS 376: Foundations of Computer Science, University of Michigan
  Graduate Student Instructor (GSI), Winter 2026

Service

Conference Reviewer: NeurIPS (2024, 2026), ICML (2025, 2026), AISTATS (2025, 2026)

Workshop Reviewer: M3L (2024), MOSS (2025)