Strategic Research Agenda for AI for science
Accelerate is developing a strategic research agenda in AI for science, which we hope will help drive further progress across projects and institutions. This page gives an overview of our recent workshops convened with the AI for Science community and outputs from these discussions contributing towards an emerging strategic research agenda for the field.
Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling
Oberwolfach, June 2023
From 11 - 16 June 2023, 40 participants with expertise in applications of machine learning in the sciences took part in the workshop Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling, co-organised by Neil Lawrence and Jessica Montgomery, from the Accelerate Programme and Bernhard Schölkopf (University of Tübingen). It set out to consider how mathematical innovations can help produce machine learning tools that can be deployed in support of scientific discovery, creating new interfaces between physical and data-driven modelling approaches.
In support of this objective, the workshop convened three discussion themes - Lessons from the application of machine learning in science; Foundational concepts and emerging methods; Machine learning for Earth and climate science.
A report summarising discussions and insights from the workshop can be accessed here.
An interactive graphic summary of the talks is available here.
Machine learning for science: bridging data-driven and mechanistic modelling
Dagstuhl, September 2022
In September 2022, Accelerate Science, with collaborators at the University of Tübingen and University of Wisconsin Madison, convened 30 researchers from across disciplines at a Dagstuhl workshop on ‘Machine learning for science: bridging data-driven and mechanistic modelling’. Two outputs emerged from these discussions:
- A research agenda that identifies areas where further technology development is needed to create AI tools that can enhance scientific discovery; and
- A roadmap for researchers, research institutions, and policymakers to deliver a new wave of progress in AI and its application for scientific discovery.
AI for Science: An emerging agenda, the report outlining this emerging agenda and roadmap is available hereand on arXiv:
Berens, P., Cranmer, K., Lawrence, ND, von Luxburg, L., Montgomery, J. “AI for Science: An Emerging Agenda”, arXiv 2023 https://doi.org/10.48550/arXiv.2303.04217
You can view a graphic summary of discussions here.