Teaching

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Teaching Assistant

Upcoming soon.

Teaching Samples

○ Homogeneous linear equations and it solutions. [Slides(Chinese)]

Reading Notes

○ D. Zhang, A. Ringh, L. Qiu, Matrix Completion and Decomposition in Phase‑Bounded Cones, SIMAX(2025). [Slides]
○ Tongyu Li, Fang Yao, Anru R. Zhang, Functional Tensor Regression, arXiv(2025). [Slides]
○ Yuji Nakatsukasa and Joel A. Tropp, Fast and Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems, SIMAX(2024). [Slides]
○ Z. Chen, Y. Yang, F. Yao, Dynamic Matrix Recovery, JASA(2024). [Slides]
○ Levon Nurbekyan, Wanzhou Lei, and Yunan Yang, Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems, SISC(2023). [Slides]
○ A. Zhang, Y. Luo, G. Raskutti, M. Yuan, ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching, SIMODS(2020). [Slides]
○ James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo, Quantum Natural Gradient, NIPS(2019). [Slides]
○ D. Wang, Y. Zheng, H. Liang, G. Li, High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition, JASA(2019). [Slides(Chinese)]

Favoriate Books/Textbooks

○ Bernard Zygelman (2025). A First Introduction to Quantum Computing and Information. Springer. [PDF]
○ Grey Ballard, Tamara G. Kolda (2025). Tensor Decompositions for Data Science. Cambridge University Press. [PDF]
○ Jörg Liesen , Volker Mehrmann (2015). Linear Algebra. Springer. [PDF]
○ Per Christian Hansen, James G. Nagy, and Dianne P. O'Leary (2006). Deblurring images: Matrices, spectra, and filtering. SIAM. [Book website]
○ Francis Bach (2023). Learning Theory from First Principles. Draft. [PDF]
○ M. Elad (2010), Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer. [Book website]