I am a research assistant in the Biomedical Image Analysis (BioMedIA) group at Imperial College London working with Prof. Daniel Rueckert, where I investigate novel machine learning methods for the automatic analysis of medical images with the aim to improve current diagnostic methods and treatments.
Research InterestsMy expertise lies in the development of supervised and unsupervised machine learning methods and their applications to very large medical image data bases. Currently, my main research focus is the application of deep learning methods such as convolutional neural networks for automatic intelligent processing of fetal ultrasound scans. Previously, I was working on applying manifold learning techniques for the analysis of motion in medical MR and ultrasound images.
I was awarded a B.Sc. in Electrical Engineering and Information Technology and a M.Sc. in Biomedical Engineering from the Federal Technical Institute (ETH) in Zurich, Switzerland, focusing on signal processing and its applications to medical images. Within the scope of my master thesis I joined the Laboratory of Mathematics in Imaging (LMI) at Harvard University for a 6 month research internship in which I worked on filter-based tractography algorithms. After that I joined the the Division of Imaging Sciences and Biomedical Engineering at King's College London for a three year PhD project under the supervision of Dr. Andrew King and Prof. Daniel Rueckert. In August 2015, I joined the BioMedIA group at Imperial College.
Expertise: Machine learning for medical image analysis
Department of Computing
Biomedical Image Analysis Group
180 Queen’s Gate
London SW7 2AZ