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Tianyu Ding, PhD  

Principal Researcher

Microsoft
Redmond, Washington, USA

Email: tianyuding [At] microsoft [Dot] com

Tianyu Ding

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. CoT-VTM: Visual-to-Music Generation with Chain-of-Thought Reasoning
    Xikang Guan, Zheng Gu, Jing Huo, Tianyu Ding, and Yang Gao
    Association for Computational Linguistics (ACL), 2025
  2. DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs
    Jongwoo Ko, Tianyi Chen, Sungnyun Kim, Tianyu Ding, Luming Liang, Ilya Zharkov, and Se-Young Yun
    International Conference on Machine Learning (ICML), 2025
  3. ONNXPruner: ONNX-Based General Model Pruning Adapter
    Dongdong Ren, Wenbin Li, Tianyu Ding, Lei Wang, Qi Fan, Jing Huo, Hongbing Pan, and Yang Gao
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025
  4. Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration
    Yiyang Chen, Tianyu Ding, Lei Wang, Jing Huo, Yang Gao, and Wenbin Li
    Computer Vision and Pattern Recognition (CVPR), 2025
  5. Automated Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression
    Xiaoyi Qu, David Aponte, Colby Banbury, Daniel Robinson, Tianyu Ding, Kazuhito Koishida, Ilya Zharkov, and Tianyi Chen
    Computer Vision and Pattern Recognition (CVPR), 2025
  6. OFER: Occluded Face Expression Reconstruction
    Pratheba Selvaraju, Victoria Fernandez Abrevaya, Timo Bolkart, Rick Akkerman, Tianyu Ding, Faezeh Amjadi, and Ilya Zharkov
    Computer Vision and Pattern Recognition (CVPR), 2025
  7. 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
    IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2025
  8. StructSR: Refuse Spurious Details in Real-World Image Super-Resolution
    Yachao Li, Dong Liang, Tianyu Ding, and Sheng-Jun Huang
    AAAI Conference on Artificial Intelligence (AAAI), 2025
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. Efficient Subgame Refinement for Extensive-form Games
    Zhenxing Ge, Zheng Xu, Tianyu Ding, Wenbin Li, and Yang Gao
    Neural Information Processing Systems (NeurIPS), 2023
  15. 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
  16. OTOv2: Automatic, Generic, User-Friendly
    Tianyi Chen, Luming Liang, Tianyu Ding, Zhihui Zhu, and Ilya Zharkov
    International Conference on Learning Representations (ICLR), 2023
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. CDFI: Compression-Driven Network Design for Frame Interpolation
    Tianyu Ding*, Luming Liang*, Zhihui Zhu, and Ilya Zharkov
    Computer Vision and Pattern Recognition (CVPR), 2021
  23. 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
  24. 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
  25. 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
  26. 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. ProCrop: Learning Aesthetic Image Cropping from Professional Compositions
    Ke Zhang, Tianyu Ding†, Jiachen Jiang, Tianyi Chen, Ilya Zharkov, Vishal M. Patel, and Luming Liang†
    arXiv:2505.22490, 2025
  2. Cat-AIR: Content and Task-Aware All-in-One Image Restoration
    Jiachen Jiang*, Tianyu Ding*†, Ke Zhang, Jinxin Zhou, Tianyi Chen, Ilya Zharkov, Zhihui Zhu, and Luming Liang†
    arXiv:2503.17915, 2025
  3. Robust Dataset Distillation by Matching Adversarial Trajectories
    Wei Lai, Tianyu Ding, Dongdong Ren, Lei Wang, Jing Huo, Yang Gao, and Wenbin Li
    arXiv:2503.12069, 2025
  4. Analyzing and Improving Model Collapse in Rectified Flow Models
    Huminhao Zhu, Fangyikang Wang, Tianyu Ding, Qing Qu, and Zhihui Zhu
    arXiv:2412.08175, 2024
  5. FORA: Fast-Forward Caching in Diffusion Transformer Acceleration
    Pratheba Selvaraju, Tianyu Ding†, Tianyi Chen, Ilya Zharkov, and Luming Liang
    arXiv:2407.01425, 2024
  6. AdaContour: Adaptive Contour Descriptor with Hierarchical Representation
    Tianyu Ding, Jinxin Zhou, Tianyi Chen, Zhihui Zhu, Ilya Zharkov, and Luming Liang
    arXiv:2404.08292, 2024
  7. 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
  8. 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
  9. 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
  10. Towards Automatic Neural Architecture Search within General Super-Networks
    Tianyi Chen, Luming Liang, Tianyu Ding, and Ilya Zharkov
    arXiv:2305.18030, 2023

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