Society10.08.2026

Haim Sompolinsky Receives the 2026 Dirac Medal for Contributions to Statistical Physics

The Abdus Salam International Centre for Theoretical Physics (ICTP) has announced the four recipients of the 2026 Dirac Medal. Israeli physicist Haim Sompolinsky is among the laureates, recognized for his fundamental contributions to statistical mechanics and its applications to theoretical neuroscience, optimization and artificial intelligence.

Israeli physicist Haim Sompolinsky is among the four recipients of the 2026 Dirac Medal. ICTP announced the laureates on August 8, the birthday of British physicist Paul Dirac. The medal is awarded to scientists who have made significant contributions to theoretical physics.

The award recognizes Sompolinsky’s contributions to statistical mechanics and his pioneering work applying statistical-physics methods to neural networks, theoretical neuroscience and problems related to artificial intelligence.

A professor emeritus at the Hebrew University of Jerusalem and a professor of Molecular and Cellular Biology and Physics at Harvard University, Sompolinsky has played a pioneering role in applying methods from statistical physics to the study of neural networks. His work on the Hopfield model and associative memory helped establish important connections between statistical mechanics and theoretical neuroscience.

His research has also explored the collective dynamics of neural systems, learning, memory and information processing. More recently, Sompolinsky has studied connections between biological and artificial neural networks, helping develop theoretical approaches to understanding intelligence in both natural and artificial systems.

French physicist Bernard Derrida, Indian physicist Deepak Dhar and Italian physicist Marc Mézard shared the award with Sompolinsky.

The Dirac Medal was established in 1985 in honor of Paul Dirac and is awarded annually by ICTP to scientists who have made significant contributions to theoretical physics.

ICTP Director Atish Dabholkar also highlighted the importance of applying methods from statistical mechanics to fields beyond traditional physics, including biology and computer science.