Software the lab has released, and a small reading list of tutorials and lectures we point students to most often.
Tools, frameworks, and demos the lab has released. Source is open where the project allows.
NVIZ is an interactive, no-code neural network visualizer that lets you upload data, define a network architecture, train a model, and explore its behavior through Sankey diagram visualizations — all in the browser. Designed to improve AI literacy through hands-on experimentation, NVIZ makes explainable AI (XAI) accessible without any programming knowledge. Developed by Ursinus students Kevin Hoffman and Michael Cummins. Source code available on GitHub.
A serverless, browser-based development environment for student practice and rapid instructor exercise development, supporting multiple languages with no setup required. Developed with Christopher Tralie at Ursinus College.
Financial literacy and planning tools developed for the Grizzly Guide to Wealth platform, presented at Ursinus CoSA 2025. Developed by Eugene Thompson and George Psaradakis.
The IoT Sensor Framework provides a platform to collect data from IoT-based sensors such as knitted smart textiles for HIPAA-compliant processing and research, developed with the Drexel Wireless Systems Lab at Drexel University in Philadelphia, Pennsylvania.
The IoT Processing Framework integrates with the IoT Sensor Framework to provide real-time or offline signal processing and machine learning on data collected by the sensor framework.
External videos and courses we recommend to students starting in the lab.
This is a gentle and visual introduction to the mathematics underlying neural networks and perceptron-based learning.
This is an example-rich introduction to the intuition behind and application of the Kalman Filter to reduce measurement and process error from estimated values.
This MIT Missing Semester course teaches fundamental skills about computer literacy for the technologist. Topics include the shell, editors, shell scripting, debugging, and version control.
This Caltech course provides a theoretical foundation for data-driven machine learning models and techniques.
