Tianyu Ding   Ph.D. Candidate Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore, Maryland, USA Email: tding1@jhu.edu GitHub: tding1 [Google Scholar Profile] [LinkedIn]

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 Science Group at Microsoft from May 2020 to August 2020, advised by Dr. Luming Liang.

Publications

• 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] [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] [GitHub]
• 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

• Neural Network Compression via Sparse Optimization
  Tianyi Chen*, Bo Ji*, Yixin Shi, Tianyu Ding, Biyi Fang, Sheng Yi, and Xiao Tu
  
• A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization
  Tianyi Chen, Guanyi Wang, Tianyu Ding, Bo Ji, Sheng Yi, and Zhihui Zhu
  

Teaching

• Instructor for EN 553.282.13(14) A Hands-On Introduction to MATLAB (Intersession 2016, 2017, 2018 & 2019)
• 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)
  

Services

• Reviewer for ICLR 2021
  Reviewer for AISTATS 2021
  Reviewer for NeurIPS 2020
  Reviewer for ECML-PKDD 2020
  

© 2020 Tianyu Ding