Community Resilience

Community resilience research focuses on understanding and enhancing the ability of systems—from communities and infrastructure to AI agents—to withstand, adapt to, and recover from disruptive events, particularly those related to climate change. Current research employs diverse approaches, including agent-based modeling, deep learning (e.g., convolutional neural networks, deep reinforcement learning), and novel data integration techniques (e.g., combining satellite imagery with socioeconomic data) to quantify resilience and identify key vulnerabilities. These efforts aim to improve disaster preparedness, optimize resource allocation, and promote equitable recovery, with applications ranging from climate change mitigation to enhancing the robustness of critical infrastructure and AI systems.