Tianjiao Ding

Ph.D. Student • Vision, Dynamics and Learning LabUniversity of Pennsylvania • Email: tjding@seas.upenn.edu

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I am a fourth-year Ph.D. student at University of Pennsylvania, advised by René Vidal. I work closely with Benjamin D. Haeffele and Yi Ma. Prior to my Ph.D., I spent two years as a research assistant at ShanghaiTech University, advised by Manolis C. Tsakiris and collaborating with Laurent Kneip. I received my undergraduate degree in computer science with honor from ShanghaiTech, working with Manolis and Yi.

My research interests lie in the theoretical foundations of machine learning and data science as well as emerging applications. As such, I develop both rigorous mathematics and practical implementations in my work. In particular, I study manifold learning and clustering, 3D vision and robotics.

I love to talk about ideas relevant to my work. If you are a student interested in doing research with me, please email me with your CV and transcript. For master’s students and undergraduates, the minimum time commitment is 15 hours per week for six months.

News

May 2022 One paper accepted to ICML 2022 and one to CVPR 2022!
Nov 2021 All papers have code available now.
Aug 2020 I shared some stories about my undergraduate career at the SIST website, ShanghaiTech.

Publications

  1. HARD: Hyperplane ARrangement Descent
    HARD: Hyperplane ARrangement Descent
    Tianjiao Ding*, Liangzu Peng*, and René Vidal
    In Conference on Parsimony and Learning (Proceedings Track), 2024
  2. Unsupervised Manifold Linearizing and Clustering
    Unsupervised Manifold Linearizing and Clustering
    In International Conference on Computer Vision, 2023
  3. Understanding Doubly Stochastic Clustering
    Understanding Doubly Stochastic Clustering
    In International Conference on Machine Learning, 2022
  4. Efficient Maximal Coding Rate Reduction by Variational Forms
    Efficient Maximal Coding Rate Reduction by Variational Forms
    In IEEE Conference on Computer Vision and Pattern Recognition, 2022
  5. Robust Homography Estimation via Dual Principal Component Pursuit
    Robust Homography Estimation via Dual Principal Component Pursuit
    In IEEE Conference on Computer Vision and Pattern Recognition, 2020
  6. Noisy Dual Principal Component Pursuit
    Noisy Dual Principal Component Pursuit
    In International Conference on Machine Learning, 2019
  7. Learning to parse wireframes in images of man-made environments
    Learning to parse wireframes in images of man-made environments
    Kun Huang, Yifan Wang, Zihan Zhou, Tianjiao Ding, Shenghua Gao, and Yi Ma
    In IEEE Conference on Computer Vision and Pattern Recognition, 2018