Qiong Zhang (张琼)

Institute of Statistics and Big Data
Renmin University of China

qiong.zhang@ruc.edu.cn GitHub Google Scholar CV

About

Hi! Welcome to my website. I’m Qiong (pronounced similarly to “Chiong” if that helps!).

I am currently an assistant professor in the Institute of Statistics and Big Data at Renmin University of China. As a statistician by training, I have ventured into the world of AI, exploring how data and algorithms can unravel complex stories. My research bridges statistics and machine learning, with a focus on mixture reduction, empirical likelihood, and tabular foundation models for in-context statistical learning. See a detailed description of my current research directions here.

I am passionate about addressing real-world problems through statistical and AI-driven approaches, and I believe that data is the most compelling storyteller of all. If you’d like to chat about statistics, AI, or the hidden narratives within your dataset, I’d be delighted to connect!

→ More about me (education, research interests)

News

• Apr 2026 paper Forgettable federated linear learning with certified data removal — Paper accepted at IEEE TNNLS.
• Mar 2026 paper Byzantine-tolerant distributed learning of finite mixture models — Paper accepted at JRSSB.
• Mar 2026 paper Neyman-Pearson multiclass classification under label noise via empirical likelihood — New preprint.
• Jan 2026 paper TabClustPFN: A prior-fitted network for tabular data clustering — New preprint.
• Jan 2026 paper Beyond aggregation: Guiding clients in heterogeneous federated learning — Paper accepted at ICLR 2026.
• Sep 2025 paper Beyond aggregation: Guiding clients in heterogeneous federated learning — New preprint on how to leveraging data heterogeneity as guided router in federated learning
• Sep 2025 paper Gaussian Herding across Pens: 3DGS compaction via Gaussian mixture reduction — Paper accepted at NeurIPS 2025 (spotlight).
• May 2025 paper TabPFN: One model to rule them all? — New preprint on tabular foundation models for statistical estimation.

Selected Publications

• Byzantine-tolerant distributed learning of finite mixture models

  Qiong Zhang^*, Yan Shuo Tan^*, Jiahua Chen

  Journal of the Royal Statistical Society, Series B, 2026
• Gaussian mixture reduction with composite transportation divergence

  Qiong Zhang, Archer Gong Zhang, Jiahua Chen

  IEEE Transactions on Information Theory, 2024
• Distributed learning of finite mixture models

  Qiong Zhang, Jiahua Chen

  Journal of Machine Learning Research, 2022
• Beyond aggregation: Guiding clients in heterogeneous federated learning

  Zijian Wang^🎓, Xiaofei Zhang, Xin Zhang, Yukun Liu, Qiong Zhang^📧

  International Conference on Learning Representations (ICLR), 2026
• Gaussian Herding across Pens: An optimal transport perspective on global Gaussian reduction for 3DGS Spotlight

  Tao Wang^*🎓, Mengyu Li^*🎓, Geduo Zeng^🎓, Cheng Meng^📧, Qiong Zhang^📧

  Neural Information Processing Systems (NeurIPS), 2025
• TabPFN: One model to rule them all?

  Qiong Zhang^*, Yan Shuo Tan^*, Qinglong Tian^*, Pengfei Li

  Minor revision at JASA

→ Full publication list