Hands-on workshops that bridge math fundamentals to real applications (audio, images, sensors) using clear notebooks and demos.
Workshop series that connects analysis, probability, and machine learning to applied signal-processing case studies, with materials designed to be approachable for newcomers.
The goal is to make rigorous material learnable in a student setting: clear explanations, minimal prerequisites, and practical demos. Each workshop is designed so participants can run experiments, modify parameters, and understand what changes and why.
A public, notebook-based workshop series at UF that walks from math fundamentals to applied signal processing — running since 2024.
The SPS curriculum is a series of educational workshops I have built and taught at the University of Florida since 2024, covering topics across signal processing and machine-learning systems. The aim is to make rigorous material genuinely learnable in a student setting: minimal prerequisites, clear explanations, and demos you can actually run and modify.
Every workshop follows the same shape, so a newcomer always knows what to expect and can go from idea to running code in one sitting:
The material is deliberately sequenced from foundations up toward applied signal-processing and machine-learning case studies:
If a concept cannot survive being explained clearly, demoed, and reused by someone else, the underlying understanding probably is not solid yet. Teaching the material the same way I would want to consume it keeps the whole series honest — and the notebooks are public, so anyone can check the work.
UF students and study groups, and anyone who wants a from-fundamentals path into signal processing and machine learning. The prerequisites are kept deliberately low.
Yes — the notebooks are on GitHub and meant to be run, modified, and reused.