About

I hold a doctoral degree from the IDEA Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), earned within the joint Borderless research group between Imperial College London and FAU, where my doctoral work focused on machine learning for quality control and diagnostic support at the front line of care. From September 2026, I join King’s College London as a Postdoctoral Research Fellow in the Department of Cardiovascular Imaging, working on the prenatal detection of congenital heart disease.

My research centres on four themes: anomaly and out-of-distribution detection for findings no one has labelled, uncertainty quantification so predictions come with an honest confidence signal, generative modelling to synthesise training data for under-represented anatomy, and data- and resource-efficient learning for small-cohort settings. With my teams, I won the MICCAI Medical Out-of-Distribution (MOOD) Challenge in 2023 and 2024, earning the five-year GOAT prize, and placed third in the CVPR VAND Challenge 2023.

Alongside this methods work, I collaborate with clinical groups on lymphatic perfusion in Fontan patients, pancreas segmentation in diabetes, and heart calcification, and co-organise MICCAI workshops, including on pelvic imaging and ultrasound. I served as Women’s Representative for FAU’s Department of Artificial Intelligence in Biomedical Engineering from 2022 to 2026, and have supervised more than fifteen Bachelor’s and Master’s students, alongside one junior PhD student.