Dissertation Year Fellow Presentation
The Environmental Trade-offs of AI in Transportation Research
Abstract:
How the growing use of AI in transportation research brings both new insights and significant environmental costs. The talk focuses on the trade-offs between the computational footprint of AI methods (training, inference, large-scale simulations) and the value they add to transportation research outcomes, especially in sustainability-related work. My goal is to highlight how our field can balance methodological innovation with responsible use of computational resources.
Bio:
Deirdre Edward is a JD/PhD candidate in Applied Mathematics at Northwestern University. Her research sits at the intersection of quantitative modeling, law, and policy, with a particular focus on transportation systems, environmental regulation, and sustainability. She is especially interested in how emerging computational methods, including artificial intelligence, shape both research practices and broader societal outcomes in mobility and infrastructure.