About me
I am now a postdoctoral researcher at the University of Pennsylvania under the supervision of Weijie Su and Qi Long.
Prior to this, I obtained my Ph.D. in 2026 at the Center of Foundational Artificial Intelligence of University College London, supervised by
François-Xavier Briol and Arthur Gretton.
I am interested in machine learning algorithms that are theoretically grounded and practically relevant.
My current research focuses on generative models, Monte Carlo methods, and causal inference. Prior to my PhD, I obtained my bachelor's degree from the department of Electronic Engineering, Tsinghua University, 2022.
Recent News
- 📌 October 2026: 🎉 I will be a postdoctoral researcher at the University of Pennsylvania under the supervision of Weijie Su and Qi Long.
- 📌 August 2026: 🎉 I just passed my PhD viva! Many thanks to my examiners Samuel Livingstone and Qiang Liu.
- 📌 August 2026: 🏛️ I will be visiting Siu Lun Chau's group at NTU from Aug 31-Sep 5.
- 📌 August 2026: 🎤 I will give a talk at CSML 2026 | China Conference on Scientific Machine Learning.
Older news
- 📌 July 2026: 📝 New paper on using MMD flow for training minimum MMD models (paper).
- 📌 July 2026: 🎉 Paper: BayesSum: Bayesian Quadrature in Discrete Spaces is accepted to ProbNum 2026.
- 📌 June 2026: 🎤 I will give a talk at MCQMC 2026 in Edinburgh.
- 📌 June 2026: 🏛️ I will give a talk at the UCL Annual Student Conference in the Department of Computer Science.
- 📌 June 2026: 🚆 I will attend ProbAI: Scaling Laws 2026 at Warwick.
- 📌 May 2026: 🎉 Two papers accepted to ICML 2026. One on accelerating kernel-based Wasserstein gradient flows via kernel thinning (paper), and one showing that samples from MMD flow have better cubature properties (paper).
- 📌 April 2026: I am co-organising UCL IMSS Annual Lecture on Computational Statistics and Machine Learning. This will be followed by London Meeting on Computational Statistics.
- 📌 March 2026: Research visit to Weijie Su at the University of Pennsylvania.
- 📌 March 2026: Research visit to Pradeep Ravikumar at Carnegie Mellon University.
- 📌 March 2026: I am serving as Area Chair for the ICLR 2026 Delta Workshop.
- 📌 November 2025: Two new papers on nonparametric instrumental variables (NPIV)! 🎉 One paper studies convergence guarantees under neural network representations, and the other paper establishes sharp statistical rates for kernel-based estimators.
Selected Publications and Preprints
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(De)-regularized Maximum Mean Discrepancy Gradient FlowZonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. SriperumbudurJMLR '25 Journal of Machine Learning Research
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Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and GeneralizationZonghao Chen, Atsushi Nitanda, Arthur Gretton, Taiji SuzukiReject and Resubmit, JMLR
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Nonparametric Instrumental Variable Regression with Observed CovariatesZikai Shen*, Zonghao Chen*, Dimitri Meunier, Ingo Steinwart, Arthur Gretton†, Zhu Li†Reject and Resubmit, Annals of Statistics
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Stationary MMD PointsZonghao Chen, Toni Karvonen, Heishiro Kanagawa, François-Xavier Briol, Chris. J. OatesICML '26 International Conference on Machine Learning
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Nested Expectations with Kernel QuadratureZonghao Chen, Masha Naslidnyk, François-Xavier BriolICML '25 International Conference on Machine Learning
Awards
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RIKEN Visiting Fellowship in Machine Learning, RIKEN AIP, Japan
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Newcastle Visiting Fellowship in Machine Learning, Newcastle University
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Tsinghua Presidential Scholarship, Tsinghua University
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Yinghua Scholarship, Tsinghua University
Professional Service
Reviewer for Journal of Machine Learning Research (JMLR), IEEE Transactions on Information Theory (TIT), SIAM Journal on Mathematics of Data Science (SIMODS), SIAM/ASA Journal on Uncertainty Quantification (SIAM/ASA JUQ), and Statistics and Computing.
Conference reviewer for ICML, NeurIPS, AISTATS, and ICLR.