AI for Scientific Data Analysis

This theme is about utilizing the power of AI as a tool for scientific research. AI can be applied to, and potentially speed up, discovery and utilization in a variety of research disciplines, such as microscopy, physics, biology, chemistry, and astronomy.

Image 1 of 1

Theme leaders Giovanni Volpe and Romain Bordes present the theme.

Graphic element

The theme is connecting the research community at Chalmers with the potential and interest in applying AI to their scientific data analysis activities. The key is to lower the entry barrier for more researchers to use AI technologies for analysing their data.

On the applied side, AI-enhanced analysis of scientific data has very broad applicability and is becoming an essential technology for many research and engineering activities. On the more fundamental side, the analysis of scientific data provides a fertile ground to develop new AI tools, algorithms, and frameworks, several of which have recently been developed by researchers in the Chalmers research community (e.g., new algorithms for particle localization, tracking, and characterization).

However, there are several domains of research where the use of AI remains largely in its infancy. One of the root causes lies in an often too high threshold for scientists outside the machine-learning community to apply such concepts and tools.

The CHAIR theme aims to bring together groups in basic and applied sciences who can benefit from the applications of AI to their field. This will be achieved by establishing bridges between them and the existing community at Chalmers that already exploits machine learning in their research. The ultimate goal is to lower the barrier for collaboration and to facilitate knowledge sharing between them.

Theme leader

Giovanni Volpe, University of Gothenburg

Leadership team

Caroline Adiels

  • Senior Lecturer, Institution of physics at Gothenburg University
To personal page

Per Bjerkeli

  • Head of Programme, Physics, Chemistry and Biological Engineering along with Mathematics and Engineering Preparatory Year
To personal page

Romain Bordes

  • Senior Researcher, Applied Chemistry, Chemistry and Chemical Engineering
To personal page

Paul Erhart

  • Deputy Head Of Department, Physics and Astronomy
To personal page

Mattias Goksör

  • Full Professor, Institution of physics at Gothenburg University
To personal page

Daniel Midtvedt

  • Associate Professor, Institution of physics at Gothenburg University
To personal page

Karl Palmås

  • Associate Professor, Science, Technology and Society, Technology Management and Economics
To personal page

Maria Carmen Toribio Perez

  • Senior Research Engineer, Onsala Space Observatory, Physics and Astronomy
To personal page

You may also be interested in

1 of 3

  • Grants within AI for Scientific Data Analysis

    The CHAIR theme AI for Scientific Data Analysis offers: – Scholarships for conferences for early stage researchers – Fellowships to invite collaborators for international grants.

    Read more
  • Structured learning

    This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.

    Read more
  • Cognition, Reasoning and AI in Collaboration

    This research theme is an expansion of a previous one focusing on Large Language Models in mathematics and programming. This time, we want to expand the scope, to think more broadly about not only how language modelling can be applied, but also how insights from cognitive science can inspire and motivate new architectures and methodologies.

    Read more