Academic interests
My research includes developing machine learning, numerical and simulation tools to look for new physics in cosmology. In my recent work I have explored applying machine learning to optimise laboratory and astrophysical scale tests of theories of gravity. In addition, I have explored applications of machine learning to emulate simulations of modified gravity, which will be particularly useful in the context of Euclid and other next-generation of surveys.
I am also interested in applying numerical and AI-based techniques to look for evidence of new physics in the Cosmic Microwave Background (CMB) data. As a member of the COMPACT Collaboration, I have developed novel AI methods to look for signatures of non-trivial cosmic topology in CMB temperature and polarisation map data.
Finally, I am interested in applying the recent breakthroughs in AI to applications in science. Recent advances in Large Language Models (LLMs), have shown great promise in general reasoning tasks. Recent research suggests that approaches, such as multi-agent systems, can be used to generate novel science ideas, provide corresponding code, debug it and iteratively improve it. I am interested in exploring approaches such as these to build tools that assist researchers.
Position history and education
- 2025-current: Marie Skłodowska-Curie Actions (MSCA) DSTrain Fellow, ITA, University of Oslo, Norway
- 2022-2025: Postdoctoral Fellow at Case Western Reserve University, USA, and the Institute for Theoretical Physics (IFT), UAM-CSIC, Spain
- 2020-2022: Postdoctoral Research Associate, University of Nottingham, UK
- 2017-2020: Data Intensive Science (DISCnet) PhD in Cosmology, ICG, University of Portsmouth, UK
- 2015-2017: Data Scientist at EasyJet Airlines, UK
- 2010-2015: MPhys in Physics, University of Edinburgh, UK.
Fellowships, awards and studentships
- 2025-current: Marie Skłodowska-Curie Actions (MSCA) DSTrain Fellow, University of Oslo (grant no. 101126636)
- 2022-2024: Richard S. Morrison Fellow, Case Western Reserve University
- 2017-2020: Data Intensive Science (DISCnet) doctoral studentship at ICG, University of Portsmouth.
Collaboration
- COMPACT Collaboration
- Euclid Collaboration
- XMM Newton Cluster Survey (XCS) Collaboration.
Publications
A full list of publications is available at INSPIRE HEP.