Z. Sun

Z. Sun

Assistant Professer @ SUFE

Efficient Machine Learning Lab @ SUFE

Welcome to Zhuo’s Lab, wnich is called Efficient Machine Learning Lab. We are focusing on developing novel and efficient learning algoritms for both：

  • General Artificial Intelligence (e.g. LLMs, Diffusion models, Optimization Algorithms …) and，
  • Theoretical Machine Learning (e.g. Monte Carlo, Variational Inference, Theorys for Transfer Learning, Stein’s method, Gaussian processes …) problems!

Zhuo Sun is a tenure-track Assistant Professor in the School of Statistics and Data Science at Shanghai University of Finance and Economics.

  • Visiting Reseacher at Imperial College London, collaborating with Prof. Harrison Bohua Zhu, Prof. Yingzhen Li and Prof. Samir Bhatt.
  • Ph.D. in Machine Learning and Computational Statistics: on variational inference & meta-learning & Monte Carlo… from University College London (supervised by Prof. François-Xavier Briol and Prof. Jinghao-Xue) and a master degree in statistical science from University of Oxford. He is looking for self-motivated PhD/Master/Interns.

📢 Recruitment

I am looking for:

  • 🎓PhD students and 📘Master students.

Expectations and Support for PhD and Master Students:

  • Good character and strong intrinsic motivation, with genuine interest in research exploration.
  • Satisfy at least one of : solid mathematical background (probability and statistics) & strong programming skills (eg Python and PyTorch).

What you can expect:

  • Students are encouraged to pursue research topics that they are interested in. I will provide full guidance within my expertise, and facilitate collaborations beyond my expertise.
  • 📚 For students interested in academia, I will guide you in research projects and collaborations, and support your further academic development.
  • 💼 For students interested in industry, I can recommend you for internships in leading research divisions in the field.

📩 If you are interested in the above research directions, please feel free to contact me:
Email: sunzhuo@mail.shufe.edu.cn

Preprints

* equal contribution; † corresponding author

arxiv
2026
Yang, Y.; Cheng, X.; He, Y.; Li, K.; Yuan W.; Sun, Z.† (2026). On Stability and Robustness of Diffusion Posterior Sampling for Bayesian Inverse Problems arxiv. [Preprint]

Publications 📖

* equal contribution; † corresponding author

ICLR
2026
Li, K.; Yang, Y.; Chen, X.; He, Y.; Sun, Z.† (2026). Multilevel Control Functional. In International Conference on Learning Representations (ICLR 2026). [Preprint] [Code]
ICLR
2026
Cheng, X.; Yuan, W.; Yang, Y.; Zhang, Y.; Cheng, S.; He, Y.; Sun, Z.† (2026). Information Shapes Koopman Representation. In International Conference on Learning Representations (ICLR 2026). [Preprint] [Code]
ICLR
2026
Cheng, X.; Yang, Y.; Jiang, W.; Yuan, C.; Sun, Z.; Hu, Y. (2026). From Embedding to Control: Representations for Stochastic Multi-Object Systems. In International Conference on Learning Representations (ICLR 2026). [Preprint] [Code]
UAI
2023
Sun, Z.; Oates, C. J; Briol, F-X. (2023). Meta-learning Control Variates: Variance Reduction with Limited Data. In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023). [Preprint] [Oral (top 3%)]
ICML SPIGM
2023
Li, K.*; Sun, Z.*† (2023). Multilevel Control Functional (Short Version). In ICML 2023 SPIGM.
ICML
2023
Sun, Z.; Barp, A.; Briol, F.-X. (2023). Vector-valued Control Variates. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023). [ASA SBSS Best Paper (2022)] [Code]
AISTATS
2021
Sun, Z.; Wu, J.; Li, X.; Yang, W.; Xue, J-H. (2021). Amortized Bayesian Prototype Meta-learning: A new probabilistic meta-learning approach to few-shot image classification. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics(AISTATS 2023).
Neurocomputing
2021
Li, X.*; Sun, Z.*; Xue, J-H. ; Ma, Z. (2021). A Concise Review of Recent Few-shot Meta-learning Methods. Neurocomputing.
IEEE TIP
2020
Li, X.*; Wu, J.*; Sun, Z.*; Ma, Z. ; Cao, J.; Xue, J-H. (2020). Bi-Similarity Network for Fine-grained Few-shot Image Classification. IEEE Transactions on Image Processing.