Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.
Explores physics-informed imaging systems, including lensless imaging, deep learning for imaging challenges, and the development of noise models for low-light videos.
Explores phase transitions in signal processing, demonstrating their impact on signal reconstruction and the application of threshold algorithms in image denoising.
