About Us
The Statistical Physics group has made significant contributions to the study of complex quantum systems and, more recently, to the interface between statistical physics and artificial intelligence. The work has improved our understanding of topological quantum physics and non-equilibrium phenomena, and applies insights from physics to the understanding and development of artificial intelligence technologies. By integrating principles of statistical physics with computational models, the research bridges theoretical physics and practical AI applications.
Currently, STP group's research has a strong focus on the principles of statistical physics as applied to neural networks. In an era where AI capabilities often surpass Moore's Law, advancing faster than our understanding of their learning and reliability processes, the group aims to uncover the fundamental principles that underpin AI systems. Using the techniques of physics, we explore the concept of universality, which asserts overarching laws governing a wide range of phenomena. Applied to neural networks, this principle provides insights into how different models - regardless of their architectures, datasets, or optimization techniques - can develop similar capabilities.
While the STP group has a strong focus on the physics of learning, the group continues to advance the understanding of quantum systems, with particular emphasis on non-equilibrium physics in low-dimensional systems, quantum information and control, topological states, and entanglement dynamics. This approach allows the group to address fundamental questions in quantum physics and contribute to the development of new technologies based on quantum principles.