Research

 

Below are three key research directions that guide our lab’s efforts:

Understanding how communities recover after floods, hurricanes, and other weather-related disasters by integrating social vulnerability, damage assessments, and spatial data to support equitable and data-driven recovery planning. Modeling the resilience of interconnected power, water, transportation, and drainage systems to identify vulnerabilities, evaluate cascading impacts, and support investment in more robust infrastructure. Applying computer vision, sensor data, and artificial intelligence to identify safety risks in real time, improve situational awareness, and support proactive decision-making on construction sites.
   Community Recovery After Weather-Related Extremes: Develop frameworks that merge social-vulnerability data with post-event damage assessments to tailor recovery strategies for the most at-risk areas.    Resilient Infrastructure Modeling: Model the vulnerability of power, water, and transportation networks to simulate hazard impacts, identify cascading failures, and guide proactive investments in system hardening and flood defenses.    AI-Driven Construction Safety: Leverage real-time video and sensor feeds with deep-learning algorithms to anticipate high-risk work conditions, enabling preemptive safety interventions and optimized resource deployment.