Human-Machine Intelligent Systems Lab
Lab member

William M. Mongan

Portrait of William M. Mongan, Principal Investigator, HMIS Lab.
Principal Investigator

William M. Mongan

William M. Mongan, Ph.D. is the Founder and Principal Investigator of the HMIS Lab. His research interests include machine learning on wireless signals for biomedical applications, as well as experiential education through undergraduate STEM research.

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Projects

  • AI in K-12 Math Education Active

    Part of the Gates Foundation-funded MentirAI project, this research builds an AI-powered math tutoring assistant on the historic Mathforum problem-thread archive, exploring RAG (retrieval-augmented generation) and fine-tuning strategies to provide pedagogically sound feedback on student mathematical thinking, in collaboration with Jason Silverman. The work extends into broader investigations of AI applications in educational contexts — including generative AI, classroom integration, and pedagogy — conducted with students across CS394 and the Honors thesis sequence (CS491–CS492). This collaboration began in 2019.

  • End-to-End Intelligent Cardiorespiratory Monitor Active

    A full-stack wearable system in collaboration with the Drexel Wireless Systems Lab for continuous, ambulatory cardiorespiratory monitoring. The project integrates signal acquisition, edge processing, and machine learning inference for real-time health monitoring outside clinical settings, and includes the IoT Sensor Framework (a HIPAA-compliant platform for collecting data from IoT-based sensors including knitted smart textiles) and the IoT Processing Framework (real-time and offline signal processing and machine learning on sensor data), both developed with the Drexel Wireless Systems Lab. Related student work includes multi-semester research in RF-based biomedical signal acquisition and analysis using wearable IoT devices (conducted across CS391 and CS394), independent study in multi-sensor data fusion techniques for wearable and IoT applications (conducted in CS392), and a machine learning pipeline for minute-level and subject-level sleep apnea detection using ECG signals (presented at Ursinus CoSA 2026 by Andrei Bogdan). We became involved in this research in 2013.

  • RF Antenna State Selection for Localization and Tracking Active

    Research into intelligent antenna state selection for RFID-based sensing in collaboration with the Drexel Wireless Systems Lab, targeting improved read range and accuracy in multi-tag environments. Related student research includes RFID-based indoor localization, multi-tag tracking, and dynamic radar sensing techniques, as well as research and educational module development for software-defined radio (SDR) platforms in smart-grid communication scenarios, conducted across CS392 and CS394. We became involved in this research in 2016.

  • NVIZ: Neural Network Visualizer Active

    An interactive, no-code neural network visualizer that lets users upload data, define a network architecture, train a model, and explore its behavior through Sankey diagram visualizations — all in the browser. Developed by Ursinus students Kevin Hoffman and Michael Cummins. Source on GitHub. Related work includes tools for making machine learning model decisions more interpretable and transparent (presented at Ursinus CoSA 2025 by Michael Cummins), and independent studies developing methods to visualize internal feature representations in trained neural networks. Conducted by multiple students across CS391 and CS392. Development began in 2022.

  • Ursinus WebIDE Completed

    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. Development took place from 2020 to 2026.

  • Grizzly Guide to Wealth (GGTW) Completed

    A suite of 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. Related independent study work developed FinLitKit, an open financial literacy toolkit for educational and personal finance use, conducted in CS392. This work was conducted from 2024 to 2025.

  • UCPlaces: Campus Orientation App Completed

    A mobile campus orientation app using ArcGIS to help new students navigate Ursinus College. Presented at Ursinus CoSA 2023. Developed by Arthur Artene and Tristan Ashcroft. This work was conducted from 2022 to 2023.

  • RF IoT Security Layer Completed

    A radio-frequency security layer for IoT sensor networks, adding authentication and encryption at the physical layer. Presented at Ursinus CoSA 2023. Developed by Jonas Ling. Related independent study explored side-channel electromagnetic signatures for hardware-level malware detection, conducted in CS391. This work was conducted from 2021 to 2023.

  • Automated Moon Crater Detection Completed

    Exploring algorithmic and neural network approaches to automatically detect and catalog craters on the lunar surface from LRO imagery. Presented at Ursinus CoSA 2023. Developed by Dylan Melby. Related independent study applied deep learning techniques to astrophysical image and data analysis more broadly, conducted in CS391. This work was conducted from 2020 to 2023.

  • Interactive AI Theater Presentation Active

    An AI-driven interactive theater performance exploring human-machine collaboration in live performance art. Presented at Ursinus CoSA 2026. Developed by Levi Fritz and Shannon Zura. This work began in 2025.

  • Pennsylvania Statewide Computing Education Data Systems (NSF ECEP) Active

    An internship project developing statewide data infrastructure for Pennsylvania to broaden access to K–12 computing education, in support of the NSF Expanding Computing Education Pathways (ECEP) Alliance. Conducted by one student (CS382). This work began in 2025.

  • Institutional Tonality Analysis for At-Risk Student Re-Engagement Active

    An internship project analyzing institutional communication data to identify trends in linguistic tonality and their relationship to academic re-engagement among at-risk students. Conducted by one student (CS382). This work began in 2025.

  • AI-Based Wi-Fi Penetration Tool Active

    Honors independent study designing an AI-assisted tool for automated Wi-Fi security assessment and penetration testing, using the Pwnagotchi software framework to explore and mitigate Wi-Fi vulnerability exploits. Conducted in the Honors thesis sequence (CS491–CS492). This work began in 2025.

  • Human Pose Estimation through WiFi Completed

    Research independent studies using RF signal analysis and machine learning to estimate human body pose without cameras. Conducted by two students (CS394). This work was conducted from 2023 to 2024.

  • Natural Language Processing to Parse the Taino Language Completed

    Research independent study applying natural language processing techniques — including retrieval-augmented approaches — to parse and analyze the Taino language. Conducted by three students (CS394). This work was conducted from 2023 to 2024.

  • Virtual Museum Environments Completed

    Research independent study in virtual reality museum environment design and interactive digital exhibit development. Conducted in CS391. This work was conducted from 2023 to 2024.

  • Wireless Emergency Communications Active

    Independent studies in reliable RF-based emergency communication systems and protocols, including amateur radio integration, conducted across CS392 and CS394. Work is conducted in collaboration with the Montgomery County ARES/RACES group and the Jim Fisher Memorial Digital Network Association. This work began in 2024.

  • Pixel Pandemonium Active

    An "unplugged" interactive activity for teaching computer science fundamentals through pixel-based visual demonstrations — no computer required. Participants fill in grids to recreate images and animations, building intuition for how digital graphics, encoding, and algorithms work. The activity has been presented at venues including CSTA, CS4Philly, CS4AllPA, the Philadelphia Science Festival, and Pixar, and can be explored at the Pixel Pandemonium drawing canvas. Developed in part through independent research projects by two students in CS394. This work began in 2022.

  • Facilitating Rapid Emergency Response via Graph Optimization Algorithms Active

    Research applying graph pathfinding algorithms and network flow analysis to GIS data to optimize emergency fire response. The project identifies the optimal fire hydrants to use given a fire location, and determines which roads to close to protect equipment and personnel while maintaining driver visibility.

Publications