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Panel Discussion: Practical Problem Solving for Machine Learning

Published online by Cambridge University Press:  01 August 2025

Guillermo Cabrera ,
Sungwook E. Hong Open the ORCID record for Sungwook E. Hong [Opens in a new window] ,
Lilianne Nakazono Open the ORCID record for Lilianne Nakazono [Opens in a new window] ,
David Parkinson Open the ORCID record for David Parkinson [Opens in a new window]  and
Yuan-Sen Ting
Guillermo Cabrera*
Affiliation:
Department of Computer Science, University of Concepción, Chile
Sungwook E. Hong*
Affiliation:
Center for Theoretical Astronomy, Korea Astronomy and Space Science Institute, 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Republic of Korea
Lilianne Nakazono*
Affiliation:
Instituto de Astronomia, Geofísica e Ciências Atmosféricas da U. de São Paulo Cidade Universitária, 05508-900 São Paulo, SP, Brazil
David Parkinson*
Affiliation:
Center for Theoretical Astronomy, Korea Astronomy and Space Science Institute, 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Republic of Korea
Yuan-Sen Ting*
Affiliation:
Research School of Astronomy & Astrophysics, Australian National University, Cotter Road, Weston, ACT 2611, Australia School of Computing, Australian National University, Acton ACT 2601, Australia
*
email: guillecabrera@inf.udec.cl
emails: swhong@kasi.re.kr, davidparkinson@kasi.re.kr
email: lilianne.nakazono@usp.br
emails: swhong@kasi.re.kr, davidparkinson@kasi.re.kr
email: yuan-sen.ting@anu.edu.au
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Abstract

Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.

Keywords

Machine LearningAstrostatisticseducationdata science

Information

Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 50 - 55
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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Footnotes

*

Equal contribution

References

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