Hao Zeng

Hao Zeng

Postdoctoral

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 obtained 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 obtained my Bachelor of Science in Mathematics (Peng's Class: Base of Financial Mathematics&Financial Engineering) at Shandong University in 2018.

My research focuses on model free statistical machine learning theory, methods, and their applications on predictive inference, 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 large language model, spatial statistics, econometrics, and biostatistics.

News

  • 2025/11: Our paper “Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction” has been accepted to econometrics and statistics top journal Journal of Business & Economic Statistics!
  • 2025/09: Our paper “Robust Online Conformal Prediction under Uniform Label Noise” has been accepted to top-tier AI conference NeurIPS 2026!
  • 2025/05: Our paper “Parametric Scaling Law of Tuning Bias in Conformal Prediction” has been accepted to top-tier AI conference 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

  • Zhang, J., Yue, Y., Zhong, W., & Zeng, H. (2025). “Robust Estimation of Grouped Network Vector Autoregression: An Empirical Analysis Based on China Air Quality Data.” (Submitted to Statistical Research)
  • Zeng, H., Huang, J., Jing, B., Wei, H., & An, B. (2025). “PAC Reasoning: Controlling the Performance Loss for Efficient Reasoning.” arXiv:2510.09133 (Submitted to ICLR 2026)
  • Gao, H., Zhang, F., Zeng, H., Meng, D., Jing, B., & Wei, H. (2025). “Exploring Imbalanced Annotations for Effective In-Context Learning.” arXiv:2502.04037 (Submitted to ICLR 2026)
  • Huang, H., Liao, W., Xi, H., Zeng, H., Zhao, M., & Wei, H. (2025). “Selective Labeling with False Discovery Rate Control.” arXiv:2510.14581 (Submitted to ICLR 2026)
  • Liu, Z., Zeng, H., Huang, W., & Wei, H. (2025). “High-Power Training Data Identification with Provable Statistical Guarantees.” arXiv:2510.09717 (Submitted to ICLR 2026)
  • Zeng, H., Huipeng Huang, Jing, B., & Wei, H. (2025). “Conditional Tuning in Conformal Prediction.”
  • Zeng, H., Jing, B., & Wei, H. (2025). “The Double Descent of Conformal Prediction.”
  • Zeng, H., Liu, K., Jing, B., & Wei, H. (2025). “On Tuning Bias in Conformal Prediction.”

Selected Publications

2025

  • Zeng, H., Zhong, W., & Xu, X. (2025). “Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction.” arXiv. https://arxiv.org/abs/2405.15600 (Accepted for Journal of Business & Economic Statistics)
  • 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/
  • Xi, HuaJun, Liu, K., Zeng, H., Sun, W., & Wei, H. (2025). “Exploring the Noise Robustness of Online Conformal Prediction.” NeurIPS 2025. https://openreview.net/forum?id=3veDGO9KiK
  • Zeng, H., Kangdao Liu, Bingyi Jing, and Hongxin Wei. 2025. “Parametric Scaling Law of Tuning Bias in Conformal Prediction.” Forty-Second International Conference on Machine Learning. https://openreview.net/forum?id=jnJLZXSOin.

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

2023

See my Google Scholar for the latest publications.