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Symbolic regression in the physical sciences

28 - 29 April 2025 09:00 - 17:00 The Royal Society Free Watch online
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Carlton House Terrace

Discussion meeting organised by Dr Deaglan Bartlett, Dr Harry Desmond, Professor Pedro G Ferreira and Professor Gabriel Kronberger.

Symbolic Regression is a branch of Machine Learning that attempts to find interpretable mathematical expressions which can accurate approximate a data set. This meeting will bring together practitioners of Symbolic Regression with physicists who are tackling problems which are particularly amenable to their analysis.

Programme

The programme, including the speaker biographies and abstracts, will be available soon.

Attending the meeting

This event is intended for researchers in relevant fields.

  • Free to attend
  • Both virtual and in-person attendance is available. Advance registration is essential. Please follow the link to register
  • Lunch is available on both days of the meeting for an optional £25 per day. There are plenty of places to eat nearby if you would prefer to purchase food offsite
  • Participants are welcome to bring their own lunch to the meeting

Enquiries: Scientific Programmes team.

Organisers

  • Pedro Ferreira

    Professor Pedro Ferreira, University of Oxford, UK

    Pedro G Ferreira is a Professor of Astrophysics in the Physics Department of the University of Oxford and a Director of the Beecroft Institute of Particle Astrophysics and Cosmology.

    His main field of expertise is cosmology, in particular the early universe and the large scale structure of the universe with a focus on trying to extract information about fundamental physics from these data sets. This has led him to explore how one might test and constrain General Relativity on large scales. Recently he has become interested in black holes, gravitational waves and, inevitably, he has also developed an interest in machine learning with a particular focus on methods for inferring physical laws or equations using symbolic regression.