Jian Xu

Jian Xu

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About

I am Jian Xu. I received my Ph.D. in Computational Mathematics and Electronic Information from South China University of Technology (SCUT) in December 2025, under the supervision of Professor Delu Zeng. I am currently a postdoc researcher at the RIKEN iTMEMS & AIP in Japan, supervised by Professor Qibin Zhao.

My research lies at the intersection of probabilistic machine learning, stochastic processes, generative modeling, differential equation–based methods, and Quantum Machine Learning. I aim to integrate advanced mathematical tools with modern machine learning, with a particular focus on Bayesian generative models and principled inference, including Gaussian processes, variational inference, and diffusion models.

During my postdoctoral research, I primarily work on projects related to Quantum Machine Learning. This project is jointly led by RIKEN iTMEMS and RIKEN AIP, with the goal of promoting the application of quantum methods in machine learning.

My first-author work has appeared in ICML (Oral, Top 1%), ICLR, AAAI, AISTATS, TNNLS, and KBS. Several manuscripts are currently under review. I also collaborate closely with Professor John Paisley (Columbia University) on Bayesian machine learning and variational inference.

🎓 Education

  • Ph.D. in Computational Mathematics & Electronic Information
    South China University of Technology, 2020 – 2025
    • First-author papers accepted at top-tier conferences, including ICML Oral (Top 1%) and ICLR
    • Advisor: Prof. Delu Zeng
    • Mentor/host: Prof.Qibin Zhao & Prof. John Paisley
  • M.Sc. in Basic Mathematics
    South China University of Technology, 2018 – 2020
    • First-class academic scholarship
    • Advisor: Assoc. Prof. Bingsheng Lin
  • B.A. in Business Administration
    Communication University of China, 2013 – 2017
    • Second Prize, National College Student Mathematics Competition

🔬 Research Focus

  • Probabilistic Machine Learning
  • Deep Gaussian Processes
  • Stochastic Differential Equations
  • Generative Models & Diffusion Inference
  • Variation Inference & Stochastic Processes Models
  • Quantum Machine Learning

📝 Selected Publications

  • Diffusion Bridge Variational Inference for Deep Gaussian Processes - ICLR 2026 https://arxiv.org/pdf/2509.19078
  • Sparse Inducing Points in Deep Gaussian Processes – ICML 2024 (Oral, Top 1%) https://arxiv.org/pdf/2407.17033
  • Sparse Variational Student-t Processes – AAAI 2024 https://arxiv.org/abs/2312.05568
  • Neural Operator Variational Inference – IEEE TNNLS https://ieeexplore.ieee.org/abstract/document/10637293
  • Fully Bayesian Differential Gaussian Processes – KBS https://www.sciencedirect.com/science/article/abs/pii/S0950705125002345
  • Double Normalizing Flows for Bayesian ODEs – AISTATS 2025 https://proceedings.mlr.press/v258/xu25b.html
  • Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling – UAI 2025 https://www.arxiv.org/abs/2408.06710

📬 Contact

Thanks for visiting! Feel free to contact me or check out my research projects.