Teaching

---

I would like to highlight the importance of some principles such as "温故而知新，可以为师矣" and "三人行，必有我师焉". Your contibutions to my materials would be highly appreciated.

Teaching Samples

○ Definition, properties, and derivatives of matrix traces. March 31, 2024. [Slides] [Video]
○ The relevance of t-statistics for small sample sizes. December 6, 2024. [Slides]
○ Kronecker product in matrix and tensor computations. (Under development) [Slides]
○ Derivatives and gradients in machine learning models. (Under development) [Reference material]

Tutorials

○ Time series convolution (e.g., circular convolution, convolution matrix, circulant matrix, discrete Fourier transform, and sparse regression). [Website]

Blog Post

---

I enjoy writing some stories for explaining my research. The content includes spatial (geospatial) data science, machine learning, matrix computations, and high-dimensional data analysis.

2023

37. An introduction to Laplacian convolutional representation. July 22, 2023. [Link]
36. Derivatives with circular convolution in machine learning. June 23, 2023. [Link]
35. Kronecker product: A tutorial. June 13, 2023. [Link]
33. Low-rank matrix and tensor factorization for speed field reconstruction. March 9, 2023. [Link]
32. Intuitive understanding of tensors in machine learning. January 21, 2023. [Link]

2022

30. Low-rank Laplacian convolution model for color image inpainting. December 17, 2022. [Link]
29. Low-rank Laplacian convolution model for time series imputation and image inpainting. December 10, 2022. [Link]
28. Circulant matrix nuclear norm minimization for image inpainting in Python. December 9, 2022. [Link]
27. Matrix factorization for image inpainting in Python. December 8, 2022. [Link]
25. Simple linear models for image deblurring. October 12, 2022. [Link]
24. Visualizing station-level USA temperature data in Python. October 8, 2022. [Link]
23. Reinforce matrix factorization for time series modeling: Probabilistic sequential matrix factorization. October 5, 2022. [Link]
22. Convolution nuclear norm minimization for time series modeling. October 3, 2022. [Link]
20. Reproducing dynamic mode decomposition on fluid flow data in Python. September 6, 2022. [Link] [Data]
19. Tensor autoregression: A multidimensional time series model. September 3, 2022. [Link]
18. Montreal bikeshare data analysis II: Visualizing bike trips on road networks. August 29, 2022. [Link]
17. Montreal bikeshare data analysis I: Bikeshare station visualization and analysis. August 25, 2022. [Link]
16. Implementing Kronecker product decomposition with NumPy. June 20, 2022. [Link]
14. Forecasting multivariate time series with nonstationary temporal matrix factorization. April 25, 2022. [Link]
13. Temporal matrix factorization for multivariate time series forecasting. March 20, 2022. [Link] 5,000+ views
12. Inpainting fluid dynamics with tensor decomposition (NumPy). March 15, 2022. [Link]
11. Using conjugate gradient to solve matrix equations. February 23, 2022. [Link]
10. Intuitive understanding of Newton-Raphson method. February 16, 2022. [Link]
8. Analyzing missing data problem in Uber movement speed data. February 14, 2022. [Link]

2021

7. Dynamic mode decomposition for spatiotemporal traffic speed time series in Seattle freeway. October 29, 2021. [Link]
6. Reduced-rank vector autoregressive model for high-dimensional time series forecasting. October 16, 2021. [Link] 4,000+ views
4. Generating random numbers and arrays in Matlab and Numpy. October 9, 2021. [Link]

2020

1. Intuitive understanding of randomized singular value decomposition. July 1, 2020. [Link] 10,000+ views

Favoriate Books/Textbooks

---

Computer Vision

○ Per Christian Hansen, James G. Nagy, and Dianne P. O'Leary (2006). Deblurring images: Matrices, spectra, and filtering. SIAM. [Book notes] [LaTeX code]
○ Richard Szeliski (2022). Computer Vision: Algorithms and Applications. Springer. Second Edition. [Book website]

Machine Learning

○ Carl Edward Rasmussen and Christopher K. I. Williams (2006). Gaussian Processes for Machine Learning. MIT Press. [Book website]
○ Charu C. Aggarwal (2020). Linear Algebra and Optimization for Machine Learning. Springer. [PDF]
○ Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong (2020). Mathematics for Machine Learning. Cambridge University Press. [Book website]
○ Jean Gallier and Jocelyn Quaintance (2022). Algebra, topology, differential calculus, and optimization theory for computer science and machine learning. [PDF]
○ Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (2018). Foundations of Machine Learning. MIT Press. Second Edition. [Book website]
○ Shai Shalev-Shwartz and Shai Ben-David (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. [PDF]
○ Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön (2022). Machine Learning: A First Course for Engineers and Scientists. [PDF]
○ Francis Bach (2024). Learning Theory from First Principles. MIT Press. [PDF]
○ Jeff Erickson (2019). Algorithms. [Book website] [GitHub]
○ Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray (2020). Algorithms for Decision Making. MIT Press. [PDF]
○ Tong Zhang (2023). Mathematical Analysis of Machine Learning Algorithms. Cambridge University Press. [Book website]
○ Brian Christian (2020). The Alignment Problem: Machine Learning and Human Values. W. W. Norton & Company. [Book website]
○ Christoph Molnar (2024). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. [Book website]
○ Andreas Krause, Jonas Hübotter (2025). Probabilistic Artificial Intelligence. arXiv:2502.05244. [PDF]

Deep Learning

○ Christopher M. Bishop, Hugh Bishop (2024). Deep Learning: Foundations and Concepts. Springer. [Book website]
○ Simon J.D. Prince (2023). Understanding Deep Learning. MIT Press. [Book website]
○ Daniel A. Roberts, Sho Yaida, and Boris Hanin (2021). The Principles of Deep Learning Theory. [PDF]
○ M. Weiler (2023). Equivariant and Coordinate Independent Convolutional Networks: A Guide Field Theory of Neural Networks. [PDF]
○ Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic (2021). Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. [PDF]

Data Science

○ Jesús Rogel-Salazar (2020). Advanced Data Science and Analytics with Python. CRC Press. [PDF]
○ Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, and Radislav Vaisman (2022). Data Science and Machine Learning: Mathematical and Statistical Methods. [PDF]
○ Giacomo Bonanno (2018). Game Theory. Second Edition. [PDF]
○ Vijay Kotu and Bala Deshpande (2019). Data Science: Concepts and Practice. Elsevier. Second Edition. [PDF]

Optimization Problem

○ B. Guenin, J. Konemann, and L. Tuncel (2014). A Gentle Introduction to Optimization. Cambridge University Press. [PDF]

Information Theory

○ Yury Polyanskiy and Yihong Wu (2022). Information Theory: From Coding to Learning. [PDF]
○ Gabriel Peyre (2020). The Discrete Algebra of the Fourier Transform. [PDF]
○ Antonio Ortega (2022). Introduction to Graph Signal Processing. [Book chapters]