Tianyu DingPh.D. Candidate
Department of Applied Mathematics & Statistics |
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Biography
I am a Ph.D. candidate from the Department of Applied Mathematics and Statistics (AMS) at Johns Hopkins University (JHU), advised by Dr. Daniel P. Robinson since 2016. And I have been working with JHU Vision Lab under the supervision of Dr. René Vidal since 2018. During my Ph.D. study, I obtained the Master's degree in Computer Science from the Department of Computer Science at JHU in 2020. Before that, I received the Master's degree in Financial Mathematics from AMS at JHU in 2016 and the Bachelor's degree in Mathematics from the School of Mathematics at Sun Yat-sen University in 2014.
My research interest focuses on the intersection between Numerical Optimization, Machine Learning, Deep Learning, and Computer Vision. I have a particular interest in the development of fundamental theory and the implementation of efficient algorithms for solving real problems.
I was a Research Intern in Applied Sciences Group at Microsoft from May 2020 to August 2020, advised by Dr. Luming Liang. My research project is about designing deep neural architectures for video frame interpolation. Our work has been accepted by CVPR 2021!
Publications
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CDFI: Compression-driven Network Design for Frame Interpolation
Tianyu Ding*, Luming Liang*, Zhihui Zhu, and Ilya Zharkov
Computer Vision and Pattern Recognition (CVPR), 2021
[pdf] [code] [arXiv] [poster] [slides] -
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
[pdf] [code] [poster] [slides] -
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
[pdf] [code] [arXiv] [supplemental] [slides] -
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
[pdf] [code] [supplemental] [poster] -
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
[pdf] [code] [poster] [slides]
Preprints
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Neural Network Compression via Sparse Optimization
Tianyi Chen*, Bo Ji*, Yixin Shi, Tianyu Ding, Biyi Fang, Sheng Yi, and Xiao Tu
arXiv:2011.04868, 2020
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Half-Space Proximal Stochastic Gradient Method for Group-Sparsity Regularized Problem
Tianyi Chen, Guanyi Wang, Tianyu Ding, Bo Ji, Sheng Yi, and Zhihui Zhu
arXiv:2009.12078, 2020
Teaching
- Instructor for EN 553.282.13(14) A Hands-On Introduction to MATLAB (Intersession 2016, 2017, 2018 & 2019)
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Teaching Assistant for EN 553.665 Introduction to Convexity (Fall 2019)
Teaching Assistant for EN 553.761 Nonlinear Optimization (Fall 2016 & Fall 2017)
Teaching Assistant for EN 553.644 Introduction to Financial Derivatives (Fall 2017)
Teaching Assistant for EN 553.645 Interest Rate and Credit Derivatives (Spring 2016 & Spring 2017)
Teaching Assistant for EN 553.627 Stochastic Processes (Fall 2015 & Fall 2016)
Teaching Assistant for EN 553.628 Stochastic Processes and Applications to Finance II (Spring 2016)