We develop data-driven methods and reliability principles to solve systemic challenges in monitoring, prediction, and optimization — from UAV-based environmental sensing to complex infrastructure systems.
Data-driven approaches to reliability and intelligent systems — bridging engineering theory with real-world sensing and prediction.
Statistical and machine learning methods for system failure prediction, prognostics, and reliability modeling under real-world uncertainty.
Intelligent drone systems for water quality sensing along the Rio Grande — integrating thermal imaging, multi-site sampling, and spatial analytics.
Sensor fusion and anomaly detection frameworks applied to civil and environmental infrastructure monitoring and optimization.
A cross-disciplinary team spanning reliability engineering, data science, and environmental systems.
Peer-reviewed articles, conference papers, and technical reports from the RISE Lab.
We are actively recruiting motivated students and collaborators passionate about data-driven approaches to real-world engineering challenges. All levels of experience are welcome.