Applied research for the humanities, civic-scale engagement, and cyber security.

Building systems that detect signals, explain change, and support better decisions.

A practical research agenda organized around prediction, trust, security, and access.

Educational AI

Investigating how intelligent learning tools can support instructors, students, feedback cycles, assessment, and personalized educational pathways.
Public data systems

Building browsable, searchable, and machine-readable access layers for public documents, regulatory filings, civic data, and government records.
Cybersecurity research

Studying authentication, verification, abuse prevention, infrastructure hardening, incident patterns, and defensive automation.
Applied machine learning

Designing ML systems that move beyond demos: classification, clustering, recommendations, model evaluation, and workflows that support real users.
Trend and prediction analysis

Exploring how signals emerge over time through forecasting, anomaly detection, comparative metrics, dashboards, and explainable trend models.

Technical systems should improve access, accountability, resilience, and community benefit.

Research should produce working systems, not only abstract models.

Predictions and classifications should be traceable, auditable, and understandable.

Gather structured and unstructured data from public records, educational systems, technical logs, APIs, and research datasets.

Apply machine learning, statistical analysis, natural language processing, and forecasting techniques to identify useful signals.

Build interfaces, summaries, dashboards, and documentation that make model behavior and findings understandable.

Package research into usable tools, reproducible code, public demos, articles, and infrastructure that can be tested and reused.