Ali Dadgar is an Adjunct Associate Professor in the Department of Mechanical Engineering at Columbia University, bringing more than two decades of industrial experience at the intersection of intelligent systems and complex engineering design. His research and practice span control systems, optimal and adaptive control, system identification, industrial automation, robotics, computer vision, machine learning, and reinforcement learning.
His work is anchored in machine intelligence and the design of adaptive autonomous systems, with a particular focus on quantum-classical intelligent architectures that bridge computational intelligence with real-world physical and digital environments. His current interests include physical AI, multi-agent AI architectures, real-time decision-making under uncertainty, and autonomous behavior in partially observable dynamic environments.
Central to his work is the principled development of systems that are not only theoretically grounded but practically deployable, systems capable of sensing, reasoning, acting, and adapting at scale across the complex physical and digital landscapes that define modern engineering and industrial challenges.