Tianjiao Ding

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

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I am a fourth-year 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 spent two years as 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 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 chat about research, life, and fun. 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 recommended time investment is at least 15 hours per week.

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
    Paper Code
  2. Unsupervised Manifold Linearizing and Clustering
    Unsupervised Manifold Linearizing and Clustering
    Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, and Benjamin D. Haeffele
    In International Conference on Computer Vision, 2023
    Paper
  3. Understanding Doubly Stochastic Clustering
    Understanding Doubly Stochastic Clustering
    Tianjiao Ding, Derek Lim, René Vidal, and Benjamin D. Haeffele
    In International Conference on Machine Learning, 2022
    Paper
  4. Efficient Maximal Coding Rate Reduction by Variational Forms
    Efficient Maximal Coding Rate Reduction by Variational Forms
    Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, and Benjamin D. Haeffele
    In IEEE Conference on Computer Vision and Pattern Recognition, 2022
    Paper
  5. Robust Homography Estimation via Dual Principal Component Pursuit
    Robust Homography Estimation via Dual Principal Component Pursuit
    Tianjiao Ding, Yunchen Yang, Zhihui Zhu, Daniel P. Robinson, René Vidal, Laurent Kneip, and Manolis C. Tsakiris
    In IEEE Conference on Computer Vision and Pattern Recognition, 2020
    Paper Code
  6. Noisy Dual Principal Component Pursuit
    Noisy Dual Principal Component Pursuit
    Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, and René Vidal
    In International Conference on Machine Learning, 2019
    Paper Code
  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
    Paper Code