VIPER: Vulnerability Intelligence Pattern Extraction and Reporting
The VIPER project is focused on developing automated methods to extract, classify, and report emerging patterns in software vulnerability data to support faster, more targeted defensive response. The project builds a vulnerability taxonomy and ingestion pipeline on Google Cloud aimed at helping our security partners gain security analysis value and enhanced analysis tooling.
Jul 15, 2026
Central Cyber-Workforce Accelerator
Central Cyber-Workforce Accelerator. Provides hands-on Cybersecurity Studio learning for undergraduates, building a network of industry and government partnerships to accelerate cybersecurity workforce development across Connecticut. Funded through the Central Connecticut State University Clusters of Excellence program, this effort connects students with authentic experiential learning while positioning employers as active collaborators in project work and curriculum. Current partner engagements span financial services, state government, and advanced manufacturing, and AI governance.
Jan 15, 2026
Cybersecurity Center for Advanced and Resilient Energy Delivery (CyberCARED)
My research with the CyberCARED project, funded by a DOE grant, focuses on enhancing the cybersecurity of cyber-physical systems (CPS) to protect power grid reliability in transmission and distribution systems. This work involves identifying vulnerabilities within the CPS attack space, developing data-driven detection tools for cyber network threats, and integrating these tools into a hardware-in-the-loop (HIL) testbed for validation. The project also includes organizing student competitions and red team exercises to refine these tools and gather feedback for future improvements. My contributions center on identifying cyber threats to these systems and developing effective defenses against them.
Jan 15, 2025
Intrusion Detection
This research focuses on creating new ways to identify previously unseen attacks in a more targeted fashion. My current work in this area is focused on improving our ability to detect new attacks faster and more reliably.
Jun 13, 2024
Securing Recommender Systems
This research focuses on protecting open systems that use user input (ratings, comments, reviews, etc.) as a means of producing recommendations and how these systems that are based on subjective measures can be protected against malicious attacks designed to manipulate the recommender system output.
May 13, 2023
Planning-Context Aware Mobile Recommendations
Jan 1, 2015
An architecture for mobile context services
Jan 1, 2015
Modeling mobile user activity planning targets
Jan 1, 2014
Prompted recall travel surveying with GPS
Jan 1, 2013
Enhancing Traveler Context through Transferable Activity Patterns
Jan 1, 2013