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AI for Radioastronomy

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Photo of the 20 m radio telescope at Onsala Space Observatory.
The 20 m radio telescope at Onsala Space Observatory.

Mini-conference, organised by the CHAIR theme AI for Scientific Data Analysis.

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Overview

The event has passed
  • Date:Starts 21 May 2024, 09:00Ends 23 May 2024, 17:00
  • Seats available:75
  • Location:
    Veras gräsmatta, Campus Johanneberg
  • Language:English
  • Last sign up date:8 May 2024
Registration (Opens in new tab)

In this event, we want to bring together experts using AI in radioastronomy and researchers in the field in a venue and program that allows for plenty of interactions. The main goal is to gather people in a collaborative space and set up a program that allows plenty of time for discussions and hopefully foster collaborations.

The applications of AI to basic research are expanding fast in every domain. There is a gap in knowledge transfer between AI experts and basic research science. In radioastronomy, with the continuous advent of new telescopes and the always even larger datasets, it is increasingly important to bring the community up to speed with the advances in the use of AI.

Invited speakers

  • Lara Alegre (Heidelberg University)
  • David Cornu (Observatoire de Paris)
  • Michele Delli Veneri (INFN Naples)
  • Fabrizia Guglielmetti (European Southern Observatory)
  • Henrik Håkansson (Fraunhofer-Chalmers Centre)
  • Michelle Lochner (University of the Western Cape/ South African Radio Astronomy Observatory)
  • Paul Tiede (CfA Harvard & Smithsonian)
  • Duo Xu (University of Virgina)

Scientific Organizing Committee

  • Carmen Toribio (Chalmers)
  • Per Bjerkeli (Chalmers)
  • Chiara Ceccobello (AI Sweden)
  • Magnus Önnheim (Fraunhofer-Chalmers Centre)
  • Franz Kirsten (Chalmers)
  • Giovanni Volpe (Gothenburg University)

Local Organizing Committee

  • Carmen Toribio (Chalmers)
  • Sara Piras (Chalmers)
  • Per Bjerkeli (Chalmers)

 

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.