“Theory without practice is empty, but equally, practice without theory is blind." ---- I. Kant

Hello! My name is Jingjing Zheng. My current research interests include low-rank and sparse representation learning, explainable deep neural networks, their mathematical foundation, optimization in machine learning, , as well as compression and fine-tuning of large models. Since 2023, I have been pursuing my doctoral studies in Mathematics at the University of British Columbia, under the supervision of Prof. Yankai Cao.

If you’d like to learn more about my academic and professional background, you can take a look at my [Curriculum Vitae].　

Recent News

News! My paper “Handling The Non-Smooth Challenge in Tensor SVD: A Multi-Objective Tensor Recovery Framework” has been accepted to ECCV 2024.

News! My paper “Bayesian-Driven Learning of A New Weighted Tensor Norm for Tensor Recovery” has been accepted to The Second Tiny Papers Track at ICLR 2024.

News! Awarded The Borealis AI 2023 Fellowship (awarded to ten AI researchers from across Canada).

News! Received Chinese Government Award for Outstanding Self-financed Students Abroad (2022), 2023.

Research Experience

• ZERO Lab, Peking University, Beijing, P. R. China, Visiting Student, Advisor: Prof. Zhouchen Lin, 05/2024-09/2024