Publications

Christodoulou L. and Chang S. The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations, ESWA, 2026.

This project cautions that hign ML model accuracy does not ensure robust counterfactual explanations.

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Loveday J. et al. Galaxy and Mass Assembly (GAMA): Small-scale anisotropic galaxy clustering and the pairwise velocity dispersion of galaxies. MNRAS, 2017.

Presents measurements of the galaxy pairwise velocity dispersion as a function of their luminosity using data from the spectroscopic GAMA survey.

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Mehrtens N. et al. The XMM Cluster Survey: The Halo Occupation Number of BOSS galaxies in X-ray clusters. MNRAS, 2016.

Obtains direct measurements of the galaxy halo occupation distribution (as opposed to model fits) using data from the XMM cluster survey.

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Blake C. et al. Galaxy And Mass Assembly (GAMA): improved cosmic growth measurements using multiple tracers of large-scale structure. MNRAS, 2013.

Growth of structure measurements have long been a crucial test of General Relativity. This is the first application of the multi-tracer technique using real data from GAMA which stood at the right intersection of galaxy density, bias spread and large scale structure probing. The results were consistent with GR.

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Robotham A. et al. Galaxy And Mass Assembly (GAMA): The Life and Times of L* Galaxies. MNRAS, 2013.

An investigation into the observational characterics of L* galaxies.

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Hopkins A. M. et al. Galaxy And Mass Assembly (GAMA): Spectroscopic analysis. MNRAS, 2013.

Data release paper from the GAMA survey.

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Christodoulou L. et al. Galaxy and Mass Assembly (GAMA): Colour and luminosity dependent clustering from calibrated photometric redshifts. MNRAS, 2012.

An analysis of large scale structure using galaxy data from SDSS and GAMA. "Calibrated" stands for "inferred from supervised machine learning" in this instance.

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Mehrtens N. et al. The XMM Cluster Survey: Optical analysis methodology and the first data release. MNRAS, 2010.

Data release paper from the XMM survey.

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