Tianhao Wang (王天浩)

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6045 South Kenwood Ave
Chicago, IL 60637
tianhao.wang@ttic.edu
tianhaowang@ucsd.edu

I am a Research Assistant Professor in the Toyota Technological Institute at Chicago. I am broadly interested in various aspects of machine learning, optimization, and statistics.

Prior to TTIC, I received my Ph.D. from the Department of Statistics and Data Science at Yale University, where I was fortunate to be advised by Zhou Fan. I obtained my Bachelor’s degree in mathematics with a dual degree in computer science at the University of Science and Technology of China.

In 2025, I will join the Halıcıoğlu Data Science Institute at UC San Diego as an Assistant Professor.

CV


Recent papers(*: equal contribution)

  1. Structured Preconditioners in Adaptive Optimization: A Unified Analysis
    Shuo Xie, Tianhao Wang, Sashank Reddi, Sanjiv Kumar, and Zhiyuan Li
    In International Conference on Machine Learning (ICML), 2025
    arXiv
  2. Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model
    Siyu Chen*, Beining Wu*, Miao Lu, Zhuoran Yang, and Tianhao Wang
    In International Conference on Learning Representations (ICLR), 2025  (Oral)
    Presented at NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning
    Link
  3. How well can Transformers emulate in-context Newton’s method?
    Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, and Jason D. Lee
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
    Presented at ICLR 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning
    arXiv
  4. Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
    Siyu Chen, Heejune Sheen, Tianhao Wang, and Zhuoran Yang
    In Advances in Neural Information Processing Systems (NeurIPS), 2024
    Presented at ICML 2024 Workshop on Theoretical Foundations of Foundation Models
    arXiv Link
  5. Implicit regularization of gradient flow on one-layer softmax attention
    Heejune Sheen, Siyu Chen, Tianhao Wang, and Harrison H. Zhou
    arXiv:2403.08699, 2024
    Presented at ICLR 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning
    arXiv
  6. Approximate Message Passing for orthogonally invariant ensembles: Multivariate non-linearities and spectral initialization
    Xinyi Zhong*, Tianhao Wang*, and Zhou Fan
    Information and Inference: A Journal of the IMA, 2024
    arXiv Link
  7. Universality of Approximate Message Passing algorithms and tensor networks
    Tianhao Wang, Xinyi Zhong, and Zhou Fan
    Annals of Applied Probability, 2024
    arXiv Link
  8. Training dynamics of multi-head softmax attention for in-context learning: emergence, convergence, and optimality
    Siyu Chen, Heejune Sheen, Tianhao Wang, and Zhuoran Yang
    Conference on Learning Theory (COLT), 2024
    Presented at ICLR 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning
    arXiv
  9. Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM
    Zhou Fan, Roy R. Lederman, Yi Sun, Tianhao Wang, and Sheng Xu
    Annals of Statistics, 2024
    arXiv Link
  10. The Marginal Value of Momentum for Small Learning Rate SGD
    Runzhe Wang, Sadhika Malladi, Tianhao Wang, Kaifeng Lyu, and Zhiyuan Li
    In International Conference on Learning Representations (ICLR), 2024
    arXiv Link