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/Explainable machine learning, especially on coreset selection, subsampling, random sketching etc.
• Generalization of DNNs, aims to open the black-box of deep learning. Theoretical investigation including complexity analysis,stability analysis, NTK, SGD/SGLD analysis etc.
• Transfer/Continual learning, including out-of-distribution generalization and brain-inspired CL methods.
• AI for Medical, especially on pathology image analysis and related topics on XAI for Medical.

People

• Formal doctoral students:
  Zeyu Gao (University of Cambridge)
  Kai He (National University of Singapore)
  Yuxin Dong (Ohio State University)
  Jialun Wu (Northwestern Polytechnical University)
  
• Current Ph.D. students:
  Wen Wen
  Xinmeng Zuo
  
• Current Master students:
  Zeyang Zhang
  Haoyang Zhang
  Ying Huang
  Huiting Huang
  Qi Kong
  Yanan Chen
  Zhongbo Zhang
  Longteng Li
  

Recent news and activities

• [Jan. 2025] One paper (about domain generalization) has been accepted to IEEE TIT.
• [Dec. 2024] One paper (about Chain-of-Diagnosis) has been accepted to IEEE TMI.
• [Oct. 2024] One paper (about multi-instance learning for WSI classification) has been accepted to IEEE TMI.
• [Sep. 2024] Two papers (about Non-Maximum Suppression and diffusion model) have been accepted to NeurIPS.
• [May. 2024] One paper (about high-order contrastive learning) has been accepted to ICML.
• [Apr. 2024] Two papers (about Stocastic Bi-level optimization and adversarial contrastive learning) have been accepted to IJCAI.
• [Feb. 2024] Our paper about information theoretical analysis on pairwise learning has been accepted to ICLR.
• [Feb.2024] Our paper about multi-instance learning has been accepted to CVPR 2024.
• [Aug. 2023] I will be the Machine Learning Session Chair of IJCAI in MACAO!
• [Mar. 2023] I have been invited to give a talk at The Second Symposium on Mathematical Technology and New Generation Communication Technology!

Publications

Services

Conference Reviewers

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

Journal Reviewers

• Journal of Machine Learning Research (JMLR)
• IEEE Transactions on Information Theory (IEEE TIT)
• IEEE Transactions on Signal Processing (IEEE TSP)
• IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
• IEEE Transactions on Cybernetics (IEEE TCYB)
• IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT)
• IEEE Transactions on Medical Imaging (IEEE TMI)
• IEEE Journal of Biomedical and Health Informatics (IEEE JBHI)
• Neural Computation
• Machine Learning
• Pattern Recognition
• Expert Systems with Applications