Marcelo Guzmán

Marcelo Guzmán is a physicist working at the frontier of machine learning and soft-matter physics. He studies physical networks as new platforms for the emergence of learning and adaptation. By combining theory and simulations, he takes a theoretical perspective on the fundamental differences between machine learning and physical learning, such as interpretability, scaling laws, efficiency, and robustness. His prior work involves the characterization and design of physical networks with topologically protected responses.