We produce cutting-edge, interdisciplinary research at the interface of urban science, complex networks, and artificial intelligence. We integrate theories and methods from the fields of data science, network science, and civil engineering. Our goal is to conduct research that matters to the global community.
Disaster City Digital Twin: Integrating Machine and Human Intelligence to Augment Flood Resilience
Our goal with this research is to generate information, datasets, and novel computational algorithms, that are useable across multiple scales, regions, and stakeholders. This project will focus on creating valuable data products (integrated into a Disaster City Digital twin) that will enhance government decision making, risk reduction, and citizen preparedness and education.
Integrating Social Equality into Infrastructure Resilience Assessment
This project involves an integrative research, education, and outreach plan. The research approach uses household surveys, fine-grained social media data analytics, and computational agent-based modelling to extract, characterize, simulate, and examine household networks. The approach will be tested in the context of Hurricane Harvey in Houston with a focus on electricity, water, and road disruptions.
Human-Infrastructure Systems Resilience to Urban Flooding: Integ
rated Assessment of Social, Institutional, and Physical Networks
This project investigates the connections between flood control, transportation, and emergency response infrastructure to transform flood resilience planning and policy processes in infrastructure systems in coastal urban areas. We will be exploring methodological and theoretical questions in civil engineering, network science, urban planning, and public policy. The findings will be tested in Houston/Harris County using empirical datasets from the 2017 Hurricane Harvey.
Urban Resilience to Health Emergencies
This project analyzes time-bound data required to better understand, predict and more effectively respond to the risk of epidemic spread in urban areas. We will be characterizing human response to epidemic threat based on population activity fluctuations; examining collective sense-making and risk encoding in online social networks; and analyzing collective information processing and actions in actors coordination networks. The findings and models generated in this project will advance the understanding and prediction of urban resilience to health emergencies.
We use urban computing, networks, and complexity to advance the fundamental understanding and modeling of urban resilience, so that our cities are better equipped against crises. Are you working on similar topics or have an idea for a collaboration? Send us an email!
