Tianhao Wang (王天浩)

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3234 Matthews Lane
La Jolla, CA 92093
tianhaowang@ucsd.edu

I am an Assistant Professor in the Halıcıoğlu Data Science Institute at University of California, San Diego. I am broadly interested in various aspects of machine learning, optimization, and statistics.

Prior to UCSD, I was a Research Assistant Professor in the Toyota Technological Institute at Chicago from 2024 to 2025. Before that, 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.

CV


Recent papers(*: equal contribution)

  1. Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics
    Binghua Li*, Mengzhe Li*, Denny Wu, and Tianhao Wang
    In Advances in Neural Information Processing Systems (NeurIPS), 2026
    Presented at ICML 2026 Workshop on High-dimensional Learning Dynamics (HiLD)
    arXiv
  2. Towards Understanding Momentum Acceleration in River-Valley Loss Landscape
    Miao Lu, Zeyu Bian, Kaiyue Wen, Beining Wu, Siyu Chen,  Tianhao Wang and 1 more author
    In Advances in Neural Information Processing Systems (NeurIPS), 2026
    arXiv
  3. INFUSER: Influence-Guided Self-Evolution Improves Reasoning
    Siyu Chen, Miao Lu, Beining Wu, Heejune Sheen, Fengzhuo Zhang,  Shuangning Li and 4 more authors
    In Advances in Neural Information Processing Systems (NeurIPS), 2026
    arXiv
  4. On Universality of Non-Separable Approximate Message Passing Algorithms
    Max Lovig, Tianhao Wang, and Zhou Fan
    Annals of Statistics (to appear), 2026
    arXiv
  5. Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks
    Xinhe Wang, Jin Huang, Xingjian Zhang, Tianhao Wang, and Jiaqi W. Ma
    In Annual Meeting of the Association for Computational Linguistics (ACL), 2026
    arXiv Link
  6. Through the Judge’s Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters
    Xingjian Zhang, Tianhong Gao, Suliang Jin, Tianhao Wang, Teng Ye,  Eytan Adar and 1 more author
    Transactions on Machine Learning Research, 2026
    arXiv
  7. A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descent
    Shuo Xie, Tianhao Wang, Beining Wu, and Zhiyuan Li
    In International Conference on Learning Representations (ICLR), 2026
    arXiv
  8. Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders
    Siyu Chen, Heejune Sheen, Xuyuan Xiong, Tianhao Wang, and Zhuoran Yang
    In International Conference on Learning Representations (ICLR), 2026
    arXiv
  9. Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalance
    Robin Yadav, Shuo Xie, Tianhao Wang, and Zhiyuan Li
    arXiv:2512.00763, 2025
    Oral presentation at NeurIPS 2025 Workshop on Optimization for Machine Learning (OPT 2025)
    arXiv Link
  10. Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation
    Mohamad Amin Mohamadi, Tianhao Wang, and Zhiyuan Li
    arXiv:2511.11500, 2025
    arXiv