About

I am Jian Xu, currently pursuing my Ph.D. in Computational Mathematics and Electronic Information at South China University of Technology (SCUT), advised by Professor Delu Zeng. My research interests lie at the intersection of probabilistic machine learning, stochastic processes, generative models, differential equations, and large language models.

I aim to integrate advanced mathematical tools with machine learning, focusing primarily on generative models and Bayesian methods such as Gaussian processes, to build principled and efficient inference frameworks. I have published multiple first-author papers in top-tier venues including ICML (Oral, Top 1%), AAAI, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Knowledge-Based Systems (KBS), and AISTATS. Several other works are currently under review at prestigious conferences and journals such as NeurIPS and TPAMI.

I have been actively collaborating with Prof. John Paisley from Columbia University, with a focus on advanced Bayesian machine learning and variational inference. Several of our joint papers have been accepted or are under review at top-tier conferences and journals, including ICML, TPAMI, and NeurIPS.

I am currently a visiting Ph.D. student at RIKEN Center for Advanced Intelligence Project (AIP) in Japan, under the supervision of Prof. Qibin Zhao, supported by the China Scholarship Council (CSC). My research during this period focuses on advanced Bayesian generative modeling and probabilistic inference.

🎓 Education

• Ph.D. in Computational Mathematics & Electronic Information
  South China University of Technology, 2020 – Present
  • First-author papers accepted at top-tier conferences, including ICML Oral (Top 1%)
  • Advisor: Prof. Delu Zeng
• 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

📝 Selected Publications

• 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

• Email: 2713091379@qq.com or jian.xu@a.riken.jp
• Location: Wushan Campus, SCUT, Guangzhou, China

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