معرفی
Dr. Stefan Gugler is a Postdoctoral Researcher at the Technical University of Berlin (TU Berlin), affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Machine Learning Group led by Prof. Dr. Klaus-Robert Müller. His research focuses on integrating machine learning methods, such as diffusion models and Gaussian processes, into theoretical chemistry to address challenges like molecular similarity, reaction networks, and catalyst design. He holds a PhD in quantum chemistry from ETH Zurich (2023), where he developed machine learning approaches for dispersion interactions, and a Master’s degree in computational inorganic chemistry from MIT (2018). His work has been recognized with an MSDE Award for his thesis on transition metal complexes. Gugler’s contributions include open-source software tools like qcscine/readuct and qcscine/puffin, advancing computational chemistry and machine learning workflows.
Education:
- PhD in Quantum Chemistry, ETH Zurich (2023)
- Master’s in Computational Inorganic Chemistry, MIT (2018)
- Interdisciplinary Sciences (BSc/MSc), ETH Zurich (2016–2018)
Research Interests:
- Molecular similarity and chemical reaction networks
- Diffusion models and generative AI for chemistry
- Machine learning in quantum chemistry and materials science
- Explainable AI (XAI) for scientific applications
Awards:
- MSDE Award (2018), MIT
Labs/Teams: Active contributor to BIFOLD and the qcscine open-source project, focusing on machine learning-driven computational chemistry tools.
