CV / Biography
Dr. Menachem (Nachi) Stern leads the Learning Machines group at AMOLF, where he studies how physical systems can learn, adapt, and process information through their own dynamics. His research lies at the intersection of statistical physics, soft matter, and learning theory, with the goal of uncovering the physical principles that enable matter to gain functionality through experience.
Stern received his PhD from the University of Chicago (2019), where he worked with Arvind Murugan. He then joined the University of Pennsylvania as a postdoctoral researcher with Andrea Liu and Vijay Balasubramanian. His work helped establish the emerging field of physical learning, which seeks to understand learning as a physical process occurring directly in material systems rather than conventional digital computers.
His research has shown how flow networks, electronic circuits, mechanical metamaterials, and other dynamical systems can learn on their own, in theory and laboratory experiments. More recently, his group has developed analytical frameworks that describe how learning reshapes the physics of adaptive systems, opening new routes to understanding how and what physical systems learn.
At AMOLF, Stern leads a research program on learning in dynamical matter supported by an ERC Starting Grant. He is also a board member of NextGenPhys@NL, a network that connects and represents the next generation of physicists in the Netherlands.
Prizes and Awards
2023 Herbert B. Callen Memorial Prize, awarded by the University of Pennsylvania Department of Physics and Astronomy for "ground-breaking work on the theory of learning by mechanical systems."
2026 ERC Starting Grant, Physical Learning in Dynamical Systems (PhyLDS)
