Ayman Moawad is a research engineer in the Intelligent Vehicle Control Team, within the Vehicle and Mobility Simulation Group of Argonne National Laboratory. He participates in Argonne’s effort to improve energy efficiency, specializing in transportation decarbonization through data science, machine learning, modeling, and simulation.

His research interests include:

• Surrogate modeling: Build data-driven solutions to accelerate system simulation, and enable co-simulation and large-scale optimization (e.g., with transportation system tools).
• AI for vehicle control: Build prototypes of Connected and Automated Vehicle controls featuring supervised and reinforcement learning algorithms.
• Data engineering: Build data processing pipelines for big data analysis.
• Real-world driving data analysis: Analyze large-scale recordings of real-world driving to create better models of vehicles, controls, and drivers.
• Driver behavior: Understand vehicle automation behavioral and utilization aspects, and the effect on energy, via machine learning and causal inference tools.

Ayman holds Master’s degrees in Engineering from Ecole des Mines France, in Statistics from the University of Chicago, and in Data Science from UC Berkeley.