AI-Powered Sustainable Urban Systems
I harness AI and machine learning to transform complex urban data into actionable planning decisions. I'm working to democratize these advanced tools so cities can make smarter, faster, and more equitable decisions about their sustainable futures.
Biodiversity Conservation Modeling
Jiajia Wang, Yvonne Gu, Brian Deal
Department of Landscape Architecture, University of Illinois Urbana-Champaign
I'm creating GeoDesign frameworks that integrate machine learning with species distribution modeling to identify critical conservation areas at landscape scales. By processing extensive occurrence data and environmental variables, these models predict biodiversity suitability across different land use scenarios. This work helps planners understand where conservation efforts will have maximum impact and how to maintain ecological connectivity in increasingly fragmented landscapes. The goal is to make biodiversity planning as data-driven and precise as infrastructure planning.
Multi-Objective Urban Optimization
Jiajia Wang, Zhihan Tao
Department of Landscape Architecture, University of Illinois Urbana-Champaign
I'm developing Bayesian optimization frameworks that help cities balance competing sustainability goals—environmental protection, social equity, and economic vitality. This approach processes urban data across multiple dimensions simultaneously, identifying land use configurations that optimize trade-offs between objectives. The framework enabling planners to explore thousands of scenarios quickly and find solutions that work for diverse stakeholder needs.