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

Hello! My name is Jingjing Zheng, but you can call me Jen. My current research interests include low-rank recovery, sparse representation learning, explainable deep neural networks, their mathematical foundation, optimization in machine learning, and math discovery in machine learning. This fall, I’ll be embarking on my doctoral studies in Mathematics at the University of British Columbia. I’m honored to have the opportunity to be supervised by 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