Research Interest: Applied and computational harmonic analysis; graph signal processing and analysis, statistical signal/image processing and analysis; feature extraction, pattern recognition, classification, and regression; data compression; elliptic PDEs, Laplacian eigenfunctions, boundary value problems, potential theory; geophysical inverse problems; human and machine perception (auditory and visual systems); computationalneuroscience; data sonification

Five Relevant Publications:

1. Y. Shao and N. Saito, "The extended Generalized Haar-Walsh Transform and applications," in Wavelets and Sparsity XVIII (D. Van De Ville, M. Papadakis, and Y. M. Lu, eds.), Proc. SPIE 11138, Paper #111380C, 2019.
2. N. Saito, “How can we naturally order and organize graph Laplacian eigenvectors?” Proceedings of 2018 IEEE Workshop on Statistical Signal Processing, pp. 483-487, 2018.
3. J. Irion and N. Saito, “Efficient approximation and denoising of graph signals using the multiscale basis dictionaries,” IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 3, pp. 607-616, 2017.
4. Y. Nakatsukasa, N. Saito, and E. Woei, “Mysteries around the graph Laplacian eigenvalue 4,” Linear Algebra and its Applications, vol. 438, no. 8, pp. 3231-3246, 2013.
5. N. Saito, “Data analysis and representation on a general domain using eigenfunctions of Laplacian,” Applied and Computational Harmonic Analysis, vol. 25, no. 1, pp. 68-97, 2008.