Professor Gabriel Stoltz is a faculty member at École des Ponts ParisTech, affiliated with the Mathematical and Computer Engineering department. His research focuses on mathematical and numerical analysis of models in molecular simulation, with an emphasis on computational statistical physics. He holds a joint position as a senior researcher at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique). His work integrates techniques from probability theory, stochastic processes, functional analysis, and numerical analysis. He has contributed extensively to free energy computation methods, variance reduction techniques, and the development of machine learning approaches for enhanced sampling in molecular dynamics. Notable collaborations include projects with Tony Lelièvre and Mathias Rousset on free energy calculations and with institutions like Institut Henri Poincaré. Teaching responsibilities include courses on computational statistical physics, machine learning, and scientific computing. His pedagogical approach emphasizes flipped classrooms, supported by publications detailing innovative teaching methods. Key research themes include: Statistical mechanics and stochastic processes Numerical methods for partial differential equations Machine learning applications in molecular dynamics Quantum chemistry and electronic structure calculations His contributions span over 100 peer-reviewed articles, including seminal works on Langevin dynamics, hypocoercivity, and adaptive biasing algorithms. He has served as an editor for Springer Proceedings and co-authored influential textbooks such as Free Energy Computations .
