Dr Jin Zhu PhD

School of Mathematics
Assistant Professor

Contact details

Email: j.zhu.7@bham.ac.uk

Address: School of Mathematics
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Jin Zhu is an Assistant Professor in the School of Mathematics at the University of Birmingham. His research focuses on large language models, reinforcement learning, and high-dimensional data, particularly on developing computationally efficient methods with statistical guarantees.

Dr Zhu's personal page

Qualifications

PhD in Statistics, Sun Yat-sen University, 2023
BSc in Statistics, Sun Yat-sen University, 2016

Biography

Before joining the University of Birmingham, Jin was a Research Officer in the Department of Statistics at the London School of Economics and Political Science (LSE), where he worked on large language models, reinforcement learning, and causal inference.

From 2019 to 2023, he pursued his PhD in the School of Mathematics at Sun Yat-sen University, focusing on high-dimensional data and non-Euclidean data.

Teaching

Statistics (Jinan)
Linear Algebra and Linear Programming (Jinan)

Research

Jin’s research is driven by real-world problems, with the aim of developing effective methods to leverage data for better understanding and addressing these problems. His most recent work focuses on large language models (LLMs). This includes developing methods to detect LLM-generated text to ensure their proper use, aligning LLMs with human preferences via reinforcement learning, and enabling LLMs to conduct complex reasoning tasks in mathematics and coding.

Jin has also worked on analysing real-world data with complex structures, such as high dimensionality, nonlinearity, and spatial dependence. These works aim to develop statistically effective methods, with improvements such as requiring smaller sample sizes, lower computational cost, or fewer assumptions.

Publications

Recent publications

Article

Zheng, Y, Zhu, J, Zhu, J & Wang, X 2025, 'A Feature Transformation and Selection Method to Acquire an Interpretable Model Incorporating Nonlinear Effects', Acta Mathematica Sinica, English Series, vol. 41, no. 2, pp. 703-732. https://doi.org/10.1007/s10114-025-3329-9

Chen, X, Zhu, J, Zhu, J, Wang, X & Zhang, H 2025, 'Reconstruct Ising Model with Global Optimality via SLIDE*', Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2025.2571245

Chen, P, Zhu, J, Zhu, J & Wang, X 2025, 'Simplex Constrained Sparse Optimization via Tail Screening', Journal of Machine Learning Research, vol. 26, no. 159, pp. 1-38. <https://jmlr.org/papers/v26/24-0010.html>

Wang, X, Zhu, J, Pan, W, Zhu, J & Zhang, H 2024, 'Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces', Journal of the American Statistical Association, vol. 119, no. 548, pp. 2772-2784. https://doi.org/10.1080/01621459.2023.2277417

Shi, C, Zhu, J, Shen, Y, Luo, S, Zhu, H & Song, R 2024, 'Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process', Journal of the American Statistical Association, vol. 119, no. 545, pp. 273-284. https://doi.org/10.1080/01621459.2022.2110878

Wang, Z, Zhu, J, Wang, X, Zhu, J, Peng, H, Chen, P, Wang, A & Zhang, X 2024, 'skscope: Fast Sparsity-Constrained Optimization in Python', Journal of Machine Learning Research, vol. 25, 290. <https://www.jmlr.org/papers/v25/23-1574.html>

Zhang, Y, Zhu, J, Zhu, J & Wang, X 2023, 'A Splicing Approach to Best Subset of Groups Selection', INFORMS Journal on Computing, vol. 35, no. 1, pp. 104-119. https://doi.org/10.1287/ijoc.2022.1241

Zhu, J, Wang, X, Hu, L, Huang, J, Jiang, K, Zhang, Y, Lin, S & Zhu, J 2022, 'abess: A Fast Best-Subset Selection Library in Python and R', Journal of Machine Learning Research, vol. 23, no. 202, pp. 1-7. <https://www.jmlr.org/papers/v23/21-1060.html>

Zhu, J, Wu, W, Zhang, Y, Lin, S, Jiang, Y, Liu, R, Zhang, H & Wang, X 2022, 'Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability', Frontiers in Oncology, vol. 12, 825353. https://doi.org/10.3389/fonc.2022.825353

Chen, M, Tian, T, Zhu, J, Pan, W & Wang, X 2022, 'Paired-sample tests for homogeneity with/without confounding variables', Statistics and its Interface, vol. 15, no. 3, pp. 335-348. https://doi.org/10.4310/21-SII695

Conference contribution

Zhu, J, Li, J, Zhou, H, Lin, Y, Shi, C & Lin, Z 2025, Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach. in A Singh, M Fazel, D Hsu, S Lacoste-Julien, F Berkenkamp, T Maharaj, K Wagstaff & J Zhu (eds), Proceedings of the 42nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 267, PMLR, pp. 79918-79937, 42nd International Conference on Machine Learning, Vancouver, British Columbia, Canada, 13/07/25. <https://proceedings.mlr.press/v267/zhu25l.html>

Zhou, H, Hanna, JP, Zhu, J, Yang, Y & Shi, C 2025, Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation. in A Singh, M Fazel, D Hsu, S Lacoste-Julien, F Berkenkamp, T Maharaj, K Wagstaff & J Zhu (eds), Proceedings of the 42nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 267, PMLR, pp. 78759-78785, 42nd International Conference on Machine Learning, Vancouver, British Columbia, Canada, 13/07/25. <https://proceedings.mlr.press/v267/zhou25f.html>

Zhu, J, Wan, R, Qi, Z, Luo, S & Shi, C 2024, Robust Offline Reinforcement Learning with Heavy-Tailed Rewards. in S Dasgupta, S Mandt & Y Li (eds), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 238, PMLR, pp. 541-549, 27th International Conference on Artificial Intelligence and Statistics, AISTATS 2024, Valencia, Spain, 2/05/24. <https://proceedings.mlr.press/v238/zhu24a.html>

Xu, Y, Zhu, J, Shi, C, Luo, S & Song, R 2023, An Instrumental Variable Approach to Confounded Off-Policy Evaluation. in A Krause, E Brunskill, K Cho, B Engelhardt, S Sabato & J Scarlett (eds), Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 202, PMLR, pp. 38848-38880, 40th International Conference on Machine Learning, ICML 2023, Honolulu, United States, 23/07/23. <https://proceedings.mlr.press/v202/xu23x.html>

Preprint

Gao, Z, Zhu, J, Hu, Y, Pan, W & Wang, X 2025 'Identification of Genetic Factors Associated with Corpus Callosum Morphology: Conditional Strong Independence Screening for Non-Euclidean Responses' arXiv. https://doi.org/10.48550/arXiv.2503.02245

View all publications in research portal