Hey, I’m Ahmad

I teach engineering at Vanderbilt University in Nashville —

I work on the modeling, control, and learning of large-scale dynamical systems — power grids, water networks, transportation, and climate — and I write papers with mostly my students —

This site also holds the more personal side of things: writing, food, travel, and other interests that matter to me —

Recruiting Students 2-3 PhD and postdoc openings I am looking for curious people interested in control, machine learning, optimization, and networked infrastructure systems. Read More
portrait of ahmad taha

News & Updates

Recent papers, teaching updates, travel notes, and announcements from the past year.

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September 2026

Two new preprints are online

Two recent works are now available as preprints: one accepted for publication in IEEE Transactions on Intelligent Vehicles, and one conditionally accepted for publication in IEEE Transactions on Automatic Control.

September 2026

CE 4240 Fall 2026 page is live

The new Infrastructure Systems Engineering course page includes the syllabus, the first two modules, and the first two homework assignments. More course content will be added as it is developed.

Research Overview

My group develops theory and algorithms in control engineering, machine learning and AI, large-scale optimization, and network science, and validates them on the energy, water, transportation, and climate systems that society depends on. The figure is the map I keep coming back to: core methods in the center, application domains around them, and dashed arrows for the places where one infrastructure sector leans on another.

Research overview: control and estimation, machine learning and AI, large-scale optimization, and network science at the center, connected to power and energy, water, transportation and mobility, and climate and natural systems

Methods and theory

  • Control theory, dynamic systems, and nonlinear networked systems
  • Machine learning and AI for dynamic systems: scientific machine learning, data-driven identification, and control-theoretic analysis of learning models
  • Large-scale and combinatorial optimization under uncertainty
  • Network science and graph theory

Application domains

  • Electric power systems: stability, monitoring, and control
  • Water systems: drinking water hydraulics and quality, stormwater, and real-time flood control
  • Transportation and mobility: connected and autonomous vehicles, traffic control, and intermodal freight
  • Climate and natural systems: catchment hydrology, hydrodynamics, and climate prediction and projection

A recurring theme is turning messy infrastructure questions into certificates, bounds, algorithms, or feedback laws that stay useful when models are nonlinear, data is incomplete, and uncertainty refuses to be polite. Lately that includes asking the same control-theoretic questions of learning models themselves: what a neural network or a language model can reach, steer, or certify.