Tianyu Ding, PhD  

Principal Researcher

Microsoft
Redmond, Washington, USA

Email: tianyuding [At] microsoft [Dot] com

GitHub | Google Scholar | LinkedIn | Twitter

Biography

I am currently a Principal Researcher in Applied Sciences Group at Microsoft, Redmond. I earned my PhD in Applied Mathematics and Statistics from Johns Hopkins University (JHU) in 2021, where I was advised by Dr. Daniel P. Robinson and Dr. René Vidal. Prior to my PhD, I received my Master's degrees in Computer Science and Financial Mathematics from JHU in 2020 and 2016, respectively. I began my academic journey with a Bachelor's degree in Mathematics from Sun Yat-sen University in 2014.

Throughout my career, I have focused on improving efficiency in machine learning and artificial intelligence, especially in areas like computer vision and generative models. I am constantly seeking new ways to push the boundaries of these fields and make contributions to the world of applied science.

I would appreciate Anyverse Anonymous Feedback from anyone on anything. Feel free to ping me!

Selected Publications

(* indicates equal contribution, indicates corresponding authorship)
  1. CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
    Haidong Zhu*, Tianyu Ding*†, Tianyi Chen, Ilya Zharkov, Ram Nevatia, and Luming Liang
    European Conference on Computer Vision (ECCV), 2024
  2. Safe and Robust Subgame Exploitation in Imperfect Information Games
    Zhenxing Ge, Zheng Xu, Tianyu Ding, Linjian Meng, Bo An, Wenbin Li, and Yang Gao
    International Conference on Machine Learning (ICML), 2024
  3. DREAM: Diffusion Rectification and Estimation-Adaptive Models
    Jinxin Zhou*, Tianyu Ding*†, Tianyi Chen, Jiachen Jiang, Ilya Zharkov, Zhihui Zhu, and Luming Liang
    Computer Vision and Pattern Recognition (CVPR), 2024
  4. Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
    Wenxiao Deng, Wenbin Li, Tianyu Ding, Lei Wang, Hongguang Zhang, Kuihua Huang, Jing Huo, and Yang Gao
    Computer Vision and Pattern Recognition (CVPR), 2024
  5. InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
    Yanqi Bao, Tianyu Ding, Jing Huo, Wenbin Li, Yuxin Li, and Yang Gao
    International Conference on Learning Representations (ICLR), 2024
  6. Efficient Subgame Refinement for Extensive-form Games
    Zhenxing Ge, Zheng Xu, Tianyu Ding, Wenbin Li, and Yang Gao
    Neural Information Processing Systems (NeurIPS), 2023
  7. Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs
    Yanqi Bao, Yuxin Li, Jing Huo, Tianyu Ding, Xinyue Liang, Wenbin Li, and Yang Gao
    ACM International Conference on Multimedia (ACMMM), 2023
  8. OTOv2: Automatic, Generic, User-Friendly
    Tianyi Chen, Luming Liang, Tianyu Ding, Zhihui Zhu, and Ilya Zharkov
    International Conference on Learning Representations (ICLR), 2023
  9. On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features
    Jinxin Zhou*, Xiao Li*, Tianyu Ding, Chong You, Qing Qu, and Zhihui Zhu
    International Conference on Machine Learning (ICML), 2022
  10. RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution
    Zhicheng Geng*, Luming Liang*, Tianyu Ding, and Ilya Zharkov
    Computer Vision and Pattern Recognition (CVPR), 2022
  11. A Geometric Analysis of Neural Collapse with Unconstrained Features
    Zhihui Zhu*, Tianyu Ding*, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, and Qing Qu
    Neural Information Processing Systems (NeurIPS), 2021
  12. Only Train Once: A One-Shot Neural Network Training and Pruning Framework
    Tianyi Chen, Bo Ji, Tianyu Ding, Biyi Fang, Guanyi Wang, Zhihui Zhu, Luming Liang, Yixin Shi, Sheng Yi, Xiao Tu
    Neural Information Processing Systems (NeurIPS), 2021
  13. Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach
    Tianyu Ding, Zhihui Zhu, René Vidal, and Daniel P. Robinson
    International Conference on Machine Learning (ICML), 2021
  14. CDFI: Compression-Driven Network Design for Frame Interpolation
    Tianyu Ding*, Luming Liang*, Zhihui Zhu, and Ilya Zharkov
    Computer Vision and Pattern Recognition (CVPR), 2021
  15. Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms
    Tianyu Ding, Zhihui Zhu, Manolis C. Tsakiris, René Vidal, and Daniel P. Robinson
    Artificial Intelligence and Statistics (AISTATS), 2021
  16. Orthant Based Proximal Stochastic Gradient Method for ℓ-1 Regularized Optimization
    Tianyi Chen, Tianyu Ding, Bo Ji, Guanyi Wang, Yixin Shi, Sheng Yi, Xiao Tu, and Zhihui Zhu
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020
  17. A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning
    Zhihui Zhu, Tianyu Ding, Daniel P. Robinson, Manolis C. Tsakiris, and René Vidal
    Neural Information Processing Systems (NeurIPS), 2019
  18. Noisy Dual Principal Component Pursuit
    Tianyu Ding*, Zhihui Zhu*, Tianjiao Ding, Yunchen Yang, René Vidal, Manolis C. Tsakiris, and Daniel P. Robinson
    International Conference on Machine Learning (ICML), 2019

Selected Preprints

  1. 3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities
    Yanqi Bao, Tianyu Ding, Jing Huo, Yaoli Liu, Yuxin Li, Wenbin Li, Yang Gao, and Jiebo Luo
    arXiv:2407.17418, 2024
  2. FORA: Fast-Forward Caching in Diffusion Transformer Acceleration
    Pratheba Selvaraju, Tianyu Ding, Tianyi Chen, Ilya Zharkov, and Luming Liang
    arXiv:2407.01425, 2024
  3. AdaContour: Adaptive Contour Descriptor with Hierarchical Representation
    Tianyu Ding, Jinxin Zhou, Tianyi Chen, Zhihui Zhu, Ilya Zharkov, and Luming Liang
    arXiv:2404.08292, 2024
  4. S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing
    Guangzhi Wang, Tianyi Chen, Kamran Ghasedi, HsiangTao Wu, Tianyu Ding, Chris Nuesmeyer, Ilya Zharkov, Mohan Kankanhalli, and Luming Liang
    arXiv:2404.08111, 2024
  5. ONNXPruner: ONNX-Based General Model Pruning Adapter
    Dongdong Ren, Wenbin Li, Tianyu Ding, Lei Wang, Qi Fan, Jing Huo, Hongbing Pan, and Yang Gao
    arXiv:2404.08016, 2024
  6. OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators
    Tianyi Chen, Tianyu Ding, Zhihui Zhu, Zeyu Chen, HsiangTao Wu, Ilya Zharkov, and Luming Liang
    arXiv:2312.09411, 2023
  7. The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
    Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, and Luming Liang
    arXiv:2312.00678, 2023
  8. LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
    Tianyi Chen, Tianyu Ding, Badal Yadav, Ilya Zharkov, and Luming Liang
    arXiv:2310.18356, 2023
  9. Towards Automatic Neural Architecture Search within General Super-Networks
    Tianyi Chen, Luming Liang, Tianyu Ding, and Ilya Zharkov
    arXiv:2305.18030, 2023
  10. Sparsity-guided Network Design for Frame Interpolation
    Tianyu Ding*, Luming Liang*, Zhihui Zhu, Tianyi Chen, and Ilya Zharkov
    arXiv:2209.04551, 2022

Student Mentoring & Supervision

Teaching

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© 2024 Tianyu Ding     (Last update: September 2024)