Selected work translating atmospheric observations, artificial intelligence, and data-driven methods into operational weather applications, research infrastructure, and experiential student training.
Flagship Research Prototype
VisSense Cam is a camera-based visibility framework that combines outdoor imagery, atmospheric observations, and machine learning to detect operationally significant low visibility and provide situational awareness for aviation, UAS, road-weather, and environmental applications.
Camera-based visibility intelligence for operational weather awareness.
Selected Portfolio
Projects highlighted here represent distinct parts of the broader research program: sensing and data infrastructure, weather-modification evaluation, AI-enabled forecasting, and research-focused workforce development.
Research Infrastructure
A public-facing atmospheric camera observation resource supporting camera-based visibility research, weather monitoring, and development of AI-ready datasets that pair imagery with meteorological observations.
Weather Modification
Research integrating dual-polarization radar, satellite observations, numerical weather prediction, environmental analysis, and interpretable machine learning to evaluate seeded and unseeded hail-producing storms.
Experiential Learning
A university-wide experiential learning initiative connecting students with authentic datasets, faculty mentors, research problems, and workforce-relevant data skills through structured, applied learning experiences.
AI-Enabled Forecasting
Development of machine-learning approaches that emulate numerical weather prediction outputs to support rapid thunderstorm guidance and operational weather decision-making.
