Postdoctoral Research Associate at Baylor University
Research
I develop interpretable AI, forecasting, and optimization methods for advanced mobility and autonomous systems. My work connects predictive modeling with the operational, infrastructure, and policy constraints that shape real transportation decisions.
How can emerging air-mobility demand be estimated before mature operating data exists?
I connect travel behavior, infrastructure, energy needs, and service design to support urban and regional AAM planning.
Featured: NASA ULI demand forecasting and mobility-energy coordination.
How can learned models incorporate explicit knowledge and remain understandable?
I combine neural learning with symbolic rules and constraints for more interpretable, robust, and controllable AI.
Featured: Neurosymbolic demand prediction and symbolic distillation of LLMs.
How can AI improve planning decisions under uncertainty and operational constraints?
I build transparent forecasting and optimization pipelines for scenario analysis, scheduling, and resilient mobility planning.
Featured: Neural-accelerated optimization for pre-disaster mobility planning.
Collaboration
I welcome collaborations involving advanced mobility modeling, interpretable AI, and optimization for resilient transportation systems.
