We study how freight and energy infrastructure should develop as demand and technology change. Our work uses data and models to evaluate where infrastructure is needed, how networks should be designed, and when investment is warranted.
This work examines where warehouses and e-commerce businesses locate, how they cluster, and how logistics activity connects regions. We use these patterns to understand how online shopping is changing metropolitan freight systems.
We develop models for planning hydrogen transport networks over time. The models compare transport modes and infrastructure investment schedules, with attention to both cost and carbon emissions.
Our recycling research examines the collection and transport of discarded electronics and EV batteries. We study where recycling facilities should be located, how much capacity is needed, and how firms coordinate within these networks.
We are extending this work to link technology adoption with infrastructure investment and to develop AI tools that support permitting for energy projects.
Developing transportation and reverse-logistics models for electronics and battery recycling within a U.S. Department of Energy–funded circular-economy initiative.
Developing multi-period planning and optimization approaches for hydrogen transportation and infrastructure development in Texas.
Developing LLM-enabled tools to support permitting workflows for electric generation and transmission infrastructure.
Designing a multi-period model for economic and low-carbon hydrogen transportation in Texas.
International Journal of Hydrogen Energy, 231, 154542.
Toward a human-centered energy transition: Concepts, models, challenges, and research opportunities.
Utilities Policy, 99, 102145.
Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains.
Scientific Reports.
Examining spatial patterns and economic interactions of logistics activities across three Texas metropolitan areas.
Journal of Transport Geography, 123, 104079.
Integrating spatial statistics and machine learning to identify relationships between e-commerce and distribution facilities in Texas, US.
Transportation Research Part A: Policy and Practice, 173, 103696.
University of Houston News