Frank Noé, a MATH+ member, professor of Mathematical Modeling in the Life Sciences at Freie Universität Berlin, and partner at Microsoft Research AI for Science has been elected as a Fellow of the American Physical Society (APS) for his contributions to Machine Learning in the Physical Sciences. Congratulations!

Election to APS fellowship is considered one of the highest honors for a physicist, recognizing exceptional contributions to physics through original research, innovative applications, teaching, and leadership. Each year, no more than 0.5 percent of APS members, excluding student members, are awarded Fellow status. Noé was selected “for the development of machine learning methods for advancing the physical sciences, in particular for the many-body sampling problem and the electronic structure problem.”

In response to his selection for this prestigious honor, he shared: „I am so happy and proud to have been elected a Fellow of the American Physical Society (APS) through the Topical Group on Data Science. I’ve never formally studied physics, but physics is what excites me. Being recognized for making a contribution means a lot to me.“

Noé’s work is highly interdisciplinary, combining methods from machine learning and theoretical physics to address fundamental challenges in molecular modeling and simulation. Following the deep learning revolution, his lab has focused on integrating deep learning into scientific research, leading to breakthroughs such as the Boltzmann Generator—an approach to accelerate the sampling of molecular structures from their equilibrium distribution by orders of magnitude—and a deep learning system capable of computing highly accurate solutions of the electronic Schrödinger equation.

In 2022, Noé joined Microsoft Research to establish a Berlin-based lab for Artificial Intelligence Research in the Sciences, after serving as a professor at Freie Universität Berlin for 10 years. He is a two-time recipient of the European Research Council (ERC) award and a member of the Berlin-Brandenburg Academy of Sciences.

LINKS:

• Frank Noé at FU Berlin

• He has been or is still involved in the following MATH+ projects:

• • AA1-10: New Methods for Inhibiting Sars-Cov2 Cell Entry (Application Areas 1 – Mechanism of Life)
  • AA1-6: Data-Driven Modeling from Atoms to Cells
  • AA2-20: Coarse-Graining Electrons in Quantum Systems (Application Areas 2 – Nano an Quantum Technologies)
  • AA2-8: Deep Backflow for Accurate Solution of the Electronic Schrödinger Equation
  • EF1-2: Quantum Kinetics (Emerging Field 1: Extracting Dynamical Laws from Complex Data)
  • EF1-4: Extracting Dynamical Laws by Deep Neural Networks: A Theoretical Perspective