About me

I’m a Machine Learning Research Scientist, interested in working at the forefront of applied AI in Health Tech. My current role at Optellum Ltd. is developing novel AI solutions for lung cancer diagnosis, treatment planning, and post-treatment management. One of the main challenges in delivering effective treatment is identifying cancer and patient characteristics that are more likely to respond to certain therapies over others. Together with Optellum’s hospital and pharmaceutical research partners, I work on developing strategies of patient stratification for treatment planning using AI.

I completed my PhD in Computing at Imperial College London. My research topic was focused on developing a decision support system for radiologists that can facilitate the diagnostic process using machine learning without the need for labeled data. My approach was to use past radiological exams (in various modalities, including X-rays and MRI) and their corresponding reports (created by clinicians as part of the normal protocol within a hospital) to develop a learning framework that could learn the correspondence between the image regions and parts of the report. Such a learning framework has the potential to greatly expedite the diagnostic process for radiologists without the need for generating specific ground-truth labels for each imaging modality.

I have also been an active member of Women in Computing at Imperial for several years, first as President and now as Secretary. I am proud to be part of the collective of undergrads, PhDs and staff members at Imperial dedicated to tackling the gender imbalance in STEM and making the workplace more inclusive. My role as part of WiC has included organising talks with inspirational female industry and academic speakers, delivering workshops, promoting wellbeing through yoga and running events as part of International Women’s Day.

In my spare time, I like to keep active by cycling, hiking and running. I also love to unwind with some yoga, drawing, painting and knitting.