Tianjiao Ding

PhD Student • Innovation in Data Engineering and Science (IDEAS)University of Pennsylvania Email: tjding@upenn.edu

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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.

Research interest: theoretical foundations of data science & emerging applications. I aim to develop both rigorous mathematics and practical implementations in my work. Recent projects include 1) geometric 3D vision 2) manifold clustering 3) sparse representation learning for foundation models 4) trustworthy AI.

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

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)!
Dec 2023 I received a master’s degree in applied mathematics and statistics from Johns Hopkins University! Delighted to move forward with roots in both mathematics and computer science.

Papers

  1. Arxiv-pace.png
    PaCE: Parsimonious Concept Engineering for Large Language Models
    In Annual Conference on Neural Information Processing Systems, 2024
  2. ICLR24-mlc-clip.png
    Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
    In International Conference on Learning Representations, 2024
  3. CPAL24-hard.gif
    HARD: Hyperplane ARrangement Descent
    TD*Liangzu Peng*, and René Vidal
    In Conference on Parsimony and Learning, 2024
  4. ICCV23-mcr2-clustering.png
    Unsupervised Manifold Linearizing and Clustering
    In International Conference on Computer Vision, 2023
  5. ICML22-doubly-stochastic-clustering.png
    Understanding Doubly Stochastic Clustering
    In International Conference on Machine Learning, 2022
  6. CVPR22-mcr2-variational.png
    Efficient Maximal Coding Rate Reduction by Variational Forms
    In IEEE Conference on Computer Vision and Pattern Recognition, 2022
  7. CVPR20-dpcp-homo.gif
    Robust Homography Estimation via Dual Principal Component Pursuit
    In IEEE Conference on Computer Vision and Pattern Recognition, 2020
  8. ICML19-dpcp.png
    Noisy Dual Principal Component Pursuit
    In International Conference on Machine Learning, 2019
  9. CVPR18-wireframe.jpg
    Learning to Parse Wireframes in Images of Man-Made Environments
    Kun Huang, Yifan WangZihan ZhouTDShenghua Gao, and Yi Ma
    In IEEE Conference on Computer Vision and Pattern Recognition, 2018