Tianjiao Ding
PhD Student • Innovation in Data Engineering and Science (IDEAS) • University of Pennsylvania • Email: tjding@upenn.edu
Hi there! I am a PhD student at University of Pennsylvania, advised by René Vidal. I work closely with Benjamin D. Haeffele and Yi Ma. Prior to my PhD, I was a research assistant at ShanghaiTech University, advised by Manolis C. Tsakiris and collaborating with Laurent Kneip. I received a master’s degree in applied mathematics and statistics from Johns Hopkins University, and a bachelor’s in computer science with honor from ShanghaiTech.
My research interests center on theoretical foundations of machine learning and emerging applications. On one hand, I use mathematics to understand when and why existing empirical paradigms work. On the other hand, these insights allow me to develop practical algorithms that are more robust, trustworthy, and efficient. As such, my work typically involves theory, controlled simulations, and large-scale applications.
You can view my CV for project highlights, or papers clustered by keywords.
Reaching out to me
I am glad to chat about research, advising, collaborations, life, and fun.
Undergraduate and MS students: If you are interested in doing research with me, feel free to contact me. The recommended time investment is at least 15 hours per week. Students I have mentored have gone on to PhD programs at UC Berkeley, Hong Kong University, MIT, NYU, and to full-time roles at Google and Meta.
Updates
Sep 2024 | Two papers accepted to NeurIPS ‘24 (Vancouver)! |
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Mar 2024 | One paper accepted to ICLR ‘24 (Vienna)! |
Jan 2024 | Excited to give an oral talk of a paper accepted to CPAL ‘24 (Hong Kong)! |
Papers
- Concept Lancet: Representation Decomposition and Transplant for Diffusion-Based Image EditingIn Under Review, 2025
- Token Statistics Transformer: Linear-Time Attention via Variational Rate ReductionIn Under Review, 2025
- Geometric Analysis of Nonlinear Manifold ClusteringIn Annual Conference on Neural Information Processing Systems, 2024
- Unsupervised Manifold Linearizing and ClusteringIn International Conference on Computer Vision, 2023