About Me

htlin@amazon.com and hzl435@psu.edu

I am a Postdoctoral Scientist at Amazon. I received my Ph.D. in Statistics from The Pennsylvania State University , working with Matthew Reimherr , and my B.S. in Statistics from University of Science and Technology of China.

My current work at Amazon focuses on agentic workflows, LLM/VLM agentic RL, and multimodal anomaly detection and diagnosis. Previously, my Ph.D. work centered on robust transfer learning, functional data learning, and differential privacy via kernel methods.

Curriculum Vitae  

Google Scholar

Publications and Preprints

(* Equal Contribution)

1. Discussion on ``INTACT: A method for integration of longitudinal physical activity data from multiple sources’’
   Haotian Lin and Matthew Reimherr
   Biometrics (to appear), Inivited discussion

2. SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
   Zelin He, Boran Han, Xiyuan Zhang, Shuai Zhang, Haotian Lin, Qi Zhu, Haoyang Fang, Danielle C. Maddix, Abdul Fatir Ansari, Akash Chandrayan, Abhinav Pradhan, Bernie Wang, Matthew Reimherr
   International Conference on Artificial Intelligence and Statistics (AISTATS), 2026
   [arXiv:2602.19455] [bibtex]

   Copy to ClipboardClose

   @article{he2026sentsr,
     title={SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning},
     author={He, Zelin and Han, Boran and Zhang, Xiyuan and Zhang, Shuai and Lin, Haotian and Zhu, Qi and Fang, Haoyang and Maddix, Danielle C and Ansari, Abdul Fatir and Chandrayan, Akash and others},
     journal={arXiv preprint arXiv:2602.19455},
     year={2026}
   }

3. Co-Regularization Enhances Knowledge Transfer in High Dimensions
   Shuo-Shuo Liu*, Haotian Lin*, Matthew Reimherr, and Runze Li
   Neural Information Processing Systems (NeurIPS), 2025
   [Paper] [bibtex]

   Copy to ClipboardClose

   @article{liu2026co,
     title={Co-Regularization Enhances Knowledge Transfer in High Dimensions},
     author={Liu, Shuo Shuo and Lin, Haotian and Reimherr, Matthew and Li, Runze},
     journal={Advances in Neural Information Processing Systems},
     volume={38},
     pages={86176--86214},
     year={2026}
   }

4. Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression
   Haotian Lin and Matthew Reimherr
   (Submitted)
   [arXiv:2501.10870] [bibtex]

   Copy to ClipboardClose

   @article{lin2025model,
     title={Model-robust and adaptive-optimal transfer learning for tackling concept shifts in nonparametric regression},
     author={Lin, Haotian and Reimherr, Matthew},
     journal={arXiv preprint arXiv:2501.10870},
     year={2025}
   }

5. Pure Differential Privacy for Functional Summaries with a Laplace-like Process
   Haotian Lin and Matthew Reimherr
   Journal of Machine Learning Research (JMLR), 2024
   [Paper] [arXiv:2309.00125] [bibtex]

   Copy to ClipboardClose

   @article{lin2024pure,
     title={Pure differential privacy for functional summaries with a laplace-like process},
     author={Lin, Haotian and Reimherr, Matthew},
     journal={Journal of Machine Learning Research},
     volume={25},
     number={305},
     pages={1--50},
     year={2024}
   }

6. Smoothness Adaptive Hypothesis Transfer Learning
   Haotian Lin and Matthew Reimherr
   International Conference on Machine Learning (ICML), 2024
   [Paper] [arXiv:2402.14966] [bibtex]

   Copy to ClipboardClose

   @inproceedings{lin2024smoothness,
     title={Smoothness Adaptive Hypothesis Transfer Learning},
     author={Lin, Haotian and Reimherr, Matthew},
     booktitle={International Conference on Machine Learning},
     pages={30286--30316},
     year={2024},
     organization={PMLR}
   }

7. On Hypothesis Transfer Learning in Functional Linear Models
   Haotian Lin and Matthew Reimherr
   International Conference on Machine Learning (ICML), 2024
   [Paper] [arXiv:2206.04277] [Code] [bibtex]

   Copy to ClipboardClose

   @inproceedings{lin2024hypothesis,
     title={On Hypothesis Transfer Learning of Functional Linear Models},
     author={Lin, Haotian and Reimherr, Matthew},
     booktitle={International Conference on Machine Learning},
     pages={30252--30285},
     year={2024},
     organization={PMLR}
   }