Salisbury, England, United Kingdom
As a Senior Digital Project Manager at GE HealthCare, I lead cross-functional, cross-business initiatives that bridge cutting-edge medical imaging technologies with real-world clinical impact. With deep expertise in PET, CT, MR, and ultrasound, I specialize in novel PET tracers for oncology — helping to shape the future of precision diagnostics. My background includes a PhD in Medical Imaging and Biomedical Engineering, which gives me a unique ability to connect and collaborate effectively across academia, clinical practice, and industry. I understand the language and priorities of each, and I thrive at the intersection where innovation meets implementation. I design and manage strategic research collaborations with hospitals and universities, ensuring alignment across diverse stakeholders. I’m passionate about translating complex, technical projects into clear, actionable strategies that drive innovation and improve patient outcomes. One of my proudest achievements has been co-leading the development of a training app for a novel PET tracer in breast oncology, and leading a collaboration with an external company to create tracer-specific quantitative tools that support oncologists in making more informed decisions. I’m always open to new opportunities to contribute, learn, and grow — whether through research partnerships or new professional challenges. If you're working to advance medical imaging and patient care, I’d love to connect.
- Lead academic/corporate collaboration projects - Co-supervise PhD students through academic partnerships - Develop product blueprint plans and present to product leaders and key stakeholders
- Optimised pipeline for generating patient-specific cardiac models from magnetic resonance imaging (MRI) data for a large cohort of patients and volunteers - Led team in training and validation of a neural network for identifying valve landmarks from cardiac MRI - Methodically compared impact of chosen model paradigms on modelling cardiac function in finite element study - Ran finite element simulations of the cardiac cycle in biventricular models (in-house FE code) - Estimated patient-specific tissue stiffness in cardiac models based on imaging data - Coordinated clinical study (on tissue stiffness in hypertrophic cardiomyopathy) with radiologists to acquire patient cardiac MRI data and ensured alignment of research questions with clinical needs - Wrote ethics application and designed pilot study for longitudinal investigation of hypertensive heart failure using a novel porcine model, in which imaging (MR and ultrasound), invasive ventricular pressure and ECG measurements were collected - Co-wrote book chapter on cardiac modelling methods, including an extensive literature review into the role of modelling to study pathologies such as hypertrophic cardiomyopathy, aortic valve stenosis and myocardial infarction
- Compared two common methods of data acquisition for MR elastography images (EPI and GRE sequences) and their impact on clinical metrics (i.e. stiffness) - Developed isotropic and anisotropic phantoms for the validation of inverse methods to estimate anisotropic stiffness from MR elastography data - Designed parts for phantom stretch setup in Solidworks - Took part in supervision of an MSc student investigating stiffness of the liver from a cohort of healthy volunteers
- Taught the practical component for two engineering courses: 1) an introductory programming course in Matlab and C and 2) a second semester mathematics course (Mathematical Modelling II) to first and second year engineering students, respectively.
- Developed novel inverse methods for estimating anisotropic tissue stiffness from cardiac MR elastography data - Registered data from different imaging sequences encoding deformation (MRE), geometry (cMRI) and tissue microstructure (DTI), which were collected in separate acquisitions, to generate computational models and perform simulations using subject-specific finite element models of the left ventricle - Simulated harmonic deformation in cardiac and phantom geometries using Abaqus in order to test inverse methods
Lodox is a low-dose x-ray machine that can provide full-body high resolution x-ray scans quickly. As an on-call service engineer, I provided 24-7 support for four locations with Lodox machines in the Cape Town area. I fixed minor issues (software and hardware) and served as a link between the hospital and the head office in Johannesburg.