Physicist Menachem Stern Appointed as Group Leader at AMOLF to Advance Learning Machines Research

Physicist Menachem Stern Appointed as Group Leader at AMOLF to Advance Learning Machines Research

(IN BRIEF) Menachem (Nachi) Stern is set to join AMOLF on August 1st as the tenure track group leader of the Learning Machines group, which will span across the autonomous matter and information in matter research themes. With a background in theoretical physics, Stern’s focus will be on exploring the physics of learning in both natural and synthetic systems. By leveraging advancements in statistical learning theory, his research aims to bridge computational machine learning with biology, paving the way for the development of innovative bio-inspired smart metamaterials that can adapt to users’ needs. Stern’s previous work, including his Ph.D. research at The University of Chicago and his current postdoctoral fellowship at The University of Pennsylvania, has centered on mechanical systems as models for novel computation and learning, laying a solid foundation for his upcoming endeavors at AMOLF.

(PRESS RELEASE) AMSTERDAM, 19-Mar-2024 — /EuropaWire/ — On August 1st Menachem (Nachi) Stern will start as tenure track group leader at AMOLF. He will lead the Learning Machines group, which will be shared between the autonomous matter and information in matter research themes at AMOLF. Stern’s research efforts will focus on the physics of learning in natural and synthetic systems. Such learning machines can adopt desirable properties and functions given real world examples and environments. Leveraging the advances of statistical learning theory in physical machines, physical learning is a promising bridge between computational machine learning and biology, and enables the development of new classes of bio-inspired smart metamaterials that adapt in-situ to users’ needs.

A theoretical physicist by training, Stern did his Ph.D. at The University of Chicago, in the group of Prof. Arvind Murugan. There he investigated mechanical systems like spring networks and self-folding origami as models for novel computation and learning. He is currently a postdoctoral fellow at The University of Pennsylvania, studying various aspects of physical learning algorithms and their implementations as learning machines.

Media Contact:

Petra Vastenhouw
Communications
mailto:p.vastenhouw@amolf.nl

SOURCE: AMOLF