Sam Leeney's current work is focused on building data analysis tools for use in Cosmology - designing statistical tools that take data from telescopes and output results that are useful to Physics. Previously, during his MPhil he developed a first-of-its-kind Bayesian anomaly detection methodology using numerical sampling techniques. Initially designed for RFI mitigation in the REACH radio telescope, he is now also using these methods to search for other anomalies such as Fast Radio Bursts. Prior to that, he developed a machine learning algorithm to classify malignant tissue during breast cancer surgery. His research is funded by the European Research Council via the UKRI guarantee scheme.
