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 the Shanghai University of Finance and Economics.

  • He is also a Visiting Reseacher at Imperial College London, collaborating with Prof. Harrison Bohua Zhu, Prof. Yingzhen Li and Prof. Samir Bhatt.
  • Before joining SUFE, he was a senior research scientist at Huawei, working on post-training & model compression of large language models.
  • Previously, he received his 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.
  • 🧑‍🔬research interns on a long-term basis (> 3 months).

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

Activities

  • Co-Organiser: S-DCE Reading Group in The Alan Turing Institute
  • PC Memember/Reviewer: AISTATS, ICLR, NeurIPS, UAI, Neurocomputing, Expert Systems with Applications

Publication

  • Li, K., Yang, Y., Chen, X., He, Y., Sun, Z.+ 📩 (2025). Multilevel Control Functional (extended). (Preprint)
  • Cheng, X., Yang, Y., Jiang, W., Yuan, C., Sun, Z., Hu, Y. (2025). From Embedding to Control: Representations for Stochastic Multi-Object Systems.
  • Cheng, X., Yuan, W., Yang, Y., Zhang, Y., Cheng, S., He, Y., Sun, Z.+ 📩 (2025). Information Shapes Koopman Representation. (Preprint)
  • 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). (Conference) (Preprint)
    • This paper was accepted for an oral presentation at UAI, top 3%.
  • Li, K.*, Sun, Z.* (2023). Multilevel Control Functional. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling. (Workshop)
    • This paper was also accepted at ICML 2023 Workshop on Computational Biology.
  • Sun, Z., Barp, A., Briol, F.-X.(2023). Vector-valued Control Variates. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023). (Conference)(Preprint)
    • This paper was awarded a Best Student Paper Award from SBSS of the American Statistical Association in 2022.
  • 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 2021).(Conference)
  • Li, X.*, Sun, Z.*, Xue, J-H., Ma, Z. (2021). A Concise Review of Recent Few-shot Meta-learning Methods. Neurocomputing.
  • 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.

Talk/Presentation

  • Talk at University of Science and Technology of China, 2025, China.
  • Talk at UAI, 2023, USA.
  • Poster at ICML, 2023, USA.
  • Talk at SIAM UKIE National Student Chapter Conference, 2023, UK.
  • Poster at the 7th London Symposium of Information Theory (LSIT), 2023, UK.
  • Talk at topic-contributed sessions of Joint Statistical Meetings, 2022, USA.
  • Talk at SIAM Conference on Uncertainty Quantification, 2022, USA.
  • Poster at the AI UK, 2022, UK.
  • Talk at International Conference on Monte Carlo Methods and Applications (Special Session on Stein’s method), 2021, German.
  • Poster at AISTATS, 2021, USA.
  • Talk at Data-Centric Engineering Seminars @ The Alan Turing Institue, 2021, UK.