AI for Scientific Discovery

2 - 4 September

This workshop will take participants on a fascinating journey through the world of automated scientific discovery, driven by artificial intelligence. Since its inception, AI research has been deeply intertwined with the pursuit of uncovering scientific insights, a synergy that has gained remarkable momentum since the late 1970s. Fueled by advancements across various domains, ranging from automating the scientific method, via deep learning and foundation models for scientific problems, to extracting interpretable knowledge from data through machine learning techniques like equation discovery and process model induction, AI now stands at the forefront of reshaping the landscape of scientific inquiry.

Our workshop offers an exciting exploration of the latest advancements in AI-driven scientific discovery, carefully organized around four intriguing themes:

  • Autonomy and automation of science
  • Equation discovery, symbolic regression and the induction of process models
  • Applications of AI, specifically in the life sciences
  • Integration efforts

Please register here until July 14th to join us in September 2024!

 

Confirmed speakers:

Geoff Baldwin - Imperial College, London, UK

Patrick Courtney - SiLA Consortium, Zürich, CH

Saso Dzeroski - Jozef Stefan Institute, Ljubljana, SI

Ross D. King - University of Cambridge, Cambridge, UK

Ina Koch - Goethe-Universität Frankfurt am Main, Frankfurt, DE

Mario Krenn - Max Planck Institute for the Science of Light, Erlangen, DE

Pat Langley - Stanford University, Stanford, USA

Sebastian Musslick - Universität Osnabrück, Osnabrück, DE

Burkhard Rost - TU München, München, DE

Ola Spjuth - Uppsala University, Uppsala, SE

 

Scientific organizers:

Peter Baumann - JGU Mainz, Mainz, DE

Stefan Kramer - JGU Mainz, Mainz, DE