Resilient Urban AI focuses on creating urban systems that can withstand and adapt to uncertainties arising from human behavior, natural disasters, and climate change.

Focus: To address these uncertainties, we develop advanced spatiotemporal deep learning models that quantify the impacts of human behavior, natural disasters, and climate events on urban systems. By integrating robust algorithms, we design resilient urban networks and operation strategies.

Objective: The goal of Resilient Urban AI is to enhance system efficiency while ensuring resilience against internal and external disruptions. By fostering connectivity and automation, we aim to build urban systems that not only operate reliably under uncertainty but also adapt dynamically to evolving urban challenges.

Ethical Urban AI focuses on promoting transparency, interpretability, and algorithmic fairness in urban applications. While urban scientists have long sought to create socially just cities, AI technologies risk amplifying inequities due to machine biases.

Focus: We develop algorithmic solutions that measure equality of opportunity, identify social biases in deep learning, and incorporate regularizations to counteract algorithmic biases. We also apply post-hoc interpretability techniques that enable stakeholders to “open the black box” and address privacy concerns in deep learning models.

Objective: The goal of Ethical Urban AI is to ensure that AI systems deliver equitable and interpretable predictions, empowering policy makers with critical social insights.

Generative Urban AI enhances urban planning and system design by integrating automation, creativity, and sustainability goals. It generates land and transportation systems guided by sustainability metrics, while keeping humans in the loop to ensure creative fidelity.

Focus: Leveraging AI to generate new urban forms, solutions, and designs. Generative AI can be used to explore innovative approaches in architecture, urban planning, and infrastructure development by creating optimized designs and strategies that traditional methods may not uncover.

Objective: To push the boundaries of urban design and problem-solving through AI-driven creativity, enabling the creation of more adaptive, sustainable, and livable cities through generative AI.