Teaching

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Teaching Assistant

Upcoming soon.

Teaching Samples

○ Homogeneous linear equations and it solutions. [Slides(Chinese)]
○ Lagrange’s Mean Value Theorem and it applications. [Slides(Chinese)]

Reading Notes

○ Partial Quantile Tensor Regression, JASA(2025). [Slides]
○ Matrix Completion and Decomposition in Phase‑Bounded Cones, SIMAX(2025). [Slides]
○ Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees, arXiv(2025). [Slides]
○ Functional Tensor Regression, arXiv(2025). [Slides]
○ Fast and Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems, SIMAX(2024). [Slides]
○ Dynamic Matrix Recovery, JASA(2024). [Slides]
○ Spectral Change Point Estimation via Sparse Tensor Decomposition, arXiv(2024). [Slides]
○ Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems, SISC(2023). [Slides]
○ High-Dimensional Portfolio Selection with Cardinality Constraints, JASA(2023). [Slides]
○ ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching, SIMODS(2020). [Slides]
○ Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions, The Annals of Statistics(2020). [Slides]
○ Quantum Natural Gradient, NIPS(2019). [Slides]
○ High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition, JASA(2019). [Slides(Chinese)]
○ Robust Sample Average Approximation, MP(2018). [Slides]

Reading Hub

♫ Enveloped Huber Regression, JASA(2024). [Slides](Creator: Sheng Liu)
♫ A scalable algorithm for sparse portfolio selection, INFRORMS Journal on Computing(2022). [Slides](Creator: Sheng Liu)
♫ Deep FlexQP: Accelerated Nonlinear Programming via Deep Unfolding, arXiv(2025). [Slides](Creator: Zhi-Long Han)
♫ High-Dimensional Low-Rank Tensor Autoregressive Time Series Modeling(2021). [Slides](Creator: Zhi-Long Han)

Favoriate Books/Textbooks

○ Simon J.D. Prince (2023). Understanding Deep Learning. The MIT Press. [PDF]
○ 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]
○ Cristina Garcia-Cardona, Harlin Lee (2023). Advances in Data Science. Springer. [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]