Tieliang Gong

About Me

I am an Associate Professor at the School of Computer Science and Technology at Xi’an Jiaotong University. My students and I are part of the Shaanxi Key Lab of BDKE. Our research is funded by the National Key Research Development Program of China and the National Natural Science Foundation.

I have previously held positions as a postdoctoral researcher at the University of Ottawa and as a visiting scholar at the University of Michigan, Ann Arbor. I obtained my Ph.D in Applied Mathematics from Xi’an Jiaotong University, where I was fortunate to be advised by Prof. Zongben Xu, an esteemed Academician of the Chinese Academy of Sciences.

🤝 I am open to exploring any potential collaboration opportunities. Please let me know if you’d like to collaborate, especially if you’re interested in information-theoretical learning issues.

Research

My research focuses on statistical learning theory and information theory, aiming to develop foundational principles for computationally efficient and reliable machine learning algorithms. My current work addresses key challenges in large-scale data analysis through information-theoretical frameworks, including generalization analysis of deep learning, theoretical foundations of transfer learning and continual learning, and the development of rigorous performance guarantees. I also explore the application of these theoretical advances in AI4Science, bridging the gap between theoretical insights and practical challenges.

Recruiting

I am looking for highly motivated and self-driven students who are interested in

• Robust and Explainable Machine Learning: with emphasis on scalable data selection and compression methods (e.g., coreset construction, subsampling strategies, randomized sketching), and their role in enhancing reliability and interpretability of modern ML systems.
• Generalization of DNNs, aiming to demystify the black-box nature of deep learning via rigorous theoretical tools, including stability-based analysis, neural tangent kernels (NTK), optimization dynamics of SGD/SGLD, and information-theoretic perspectives.
• Transfer/Continual learning, focusing on fundamental challenges of knowledge transfer across tasks and domains, out-of-distribution (OOD) generalization, and biologically/plausibly brain-inspired approaches for lifelong learning.
• AI for Medical, with a particular focus on computational pathology, multi-modal medical data integration, and trustworthy medical AI through explainability and uncertainty quantification.

🎓 Students

✨ Recent News and Activities

Publications

Services

Conference Reviewers

• ICML
• NeurIPS
• ICLR
• CVPR
• AISTATS
• UAI
• ICCV
• AAAI
• IJCAI

Journal Reviewers

• Journal of Machine Learning Research
• IEEE Transactions on Information Theory
• IEEE Transactions on Signal Processing
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Cybernetics
• IEEE Transactions on Circuits and Systems for Video Technology
• IEEE Transactions on Medical Imaging
• IEEE Journal of Biomedical and Health Informatics
• Neural Computation
• Neural Networks
• Information Fusion
• Machine Learning
• Pattern Recognition
• Expert Systems with Applications