Menachem is a theoretical physicist exploring learning in physical systems, particularly in the contexts of mechanical systems and physically inspired learning rules. His research established such learning rules for flow networks, elastic networks, and self-folding origami, highlighting their potential as designer multi-functional, dynamically controlled meta-materials. He is specifically interested in the analogy between learning in physical networks and the underlying frameworks of artificial neural networks and machine learning in general. This fundamental connection suggests the use of physically inspired systems as learning algorithms with novel properties. Menachem holds a Ph.D. in physics from The University of Chicago.