Heterogeneity nourishes statistics; independence begets probability; uncertainty is eternal.

I am currently a Postdoctoral in Statistics at the Southern University of Science and Technology (SUSTech) and National University of Singapore (NUS), Department of Statistics and Data Science, supported by the SUSTech-NUS Joint Research Program, supervised by Prof. Bingyi Jing, Prof. Hongxin Wei, and Prof. Wang Zhou. I received my PhD in Statistics from Gregory and Paula Chow Institute for Studies in Economics, Xiamen University in 2024, where I was advised by Prof. Wei Zhong, with Prof. Xingbai Xu and Prof. Tuo Liu as co-advisors. During 2018-2020, I completed my academic master’s courses in Quantitative Economics at Wang Yanan Institute for Studies in Economics before transferring to the PhD program in Statistics. I was a visiting researcher at the Department of Statistics, National University of Singapore (NUS) from May to October 2023. I completed my Bachelor of Science in Mathematics (Peng’s Class: Base of Financial Mathematics&Financial Engineering) at Shandong University in 2018.

My research focuses on statistical machine learning theory, methods, and their applications on uncertainty quantification, particularly developing novel methodologies that bridge statistical theory with practical applications. I am also interested in interdisciplinary research at the intersection of machine learning methodologies with spatial statistics, econometrics, and biostatistics.

News

• 2025/05: Our paper “Parametric Scaling Law of Tuning Bias in Conformal Prediction” has been accepted to ICML 2025!
• 2024/07: Started postdoctoral position at the Department of Statistics and Data Science, Southern University of Science and Technology (SUSTech) under the SUSTech-NUS Joint Research Program.
• 2024/06: Graduated with a Ph.D. in Statistics from Gregory and Paula Chow Institute for Studies in Economics, Xiamen University.

Working Papers

• Zeng, H., Liu, K., Jing, B., & Wei, H. (2025). “Parametric Scaling Law of Tuning Bias in Conformal Prediction.” arXiv. https://doi.org/10.48550/arXiv.2502.03023

• Gao, H., Zhang, F., Zeng, H., Meng, D., Jing, B., & Wei, H. (2025). “Exploring Imbalanced Annotations for Effective In-Context Learning.” arXiv. https://doi.org/10.48550/arXiv.2502.04037

• Xi, H., Liu, K., Zeng, H., Sun, W., & Wei, H. (2025). “Robust Online Conformal Prediction under Uniform Label Noise.” arXiv. https://doi.org/10.48550/arXiv.2501.18363

• Zeng, H., Zhong, W., & Xu, X. (2024). “Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction.” arXiv. https://doi.org/10.48550/arXiv.2405.15600

Publications

Journal Articles

2025

• Liu, K., Sun, T., Zeng, H., Zhang, Y., Pun, C.-M., & Vong, C.-M. (2025). “Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification.” IEEE Transactions on Circuits and Systems for Video Technology. https://ieeexplore.ieee.org/abstract/document/10960721/

2024

• Zeng, H., Wan, C., Zhong, W., & Liu, T. (2024). “Robust Integrative Analysis via Quantile Regression with Homogeneity and Sparsity.” Journal of Statistical Planning and Inference, 234 (June): 106196. https://doi.org/10.1016/j.jspi.2024.106196

• Wan, C., Zeng, H., Zhang, W., Zhong, W., & Zou, C. (2024). “Data‐driven Estimation for Multithreshold Accelerated Failure Time Model.” Scandinavian Journal of Statistics, November, sjos.12758. https://doi.org/10.1111/sjos.12758

Software

2023

• Wan, C., Zeng, H., Zhong, W., & Zou, C. (2023). “MTAFT: Data-Driven Estimation for Multi-Threshold Accelerate Failure Time Model.” https://cran.r-project.org/web/packages/MTAFT/index.html

See my Google Scholar for the latest publications.