Short Bio

I am a Research Scientist at Ellison Institute of Technology, Oxford (EIT) leading research in foundation models for medical sciences and contributing to the institute's ambitious goal of translating scientific discovery into real world impact to benefit society. Previously, I was a Staff Research Scientist at Google DeepMind where I worked on safe and reliable machine learning and AI for science research. I started my industry career as a Machine Learning Researcher in the Cortex Applied Research team at Twitter UK, focusing on real-time personalisation while I carried out research at the intersection of recommender systems and algorithmic transparency. Our exploration on algorithmic amplification of political content on Twitter was featured by the Economist and the BBC, among others. 

I completed a Doctoral degree in Medical Image Computing at Imperial College London under the supervision of Professor Daniel Rueckert, as part of the High Performance Embedded and Distributed Systems (HiPEDS) Doctoral Training Programme, for which I currently serve as an Advisory Board member. My research focused on developing methods for modelling and analysing graph-structured neuroimaging data at an individual or population level using traditional graph theoretical approaches and geometric deep learning.

During my PhD, I did a Research internship with Spotify London and worked on audio generative models and machine learning for audio understanding. Before that, I visited the Stroke Group in Massachusetts General Hospital, Harvard Medical School where I was lucky to be supervised by inspiring Professor Natalia Rost and Dr Markus Schirmer, while being supported by an EMBO Short-Term Fellowship. I am passionate about community outreach and increasing diversity in technology, which led me to developing a 6-week Data Science course for Code First Girls. I'm constantly striving to learn more about Algorithmic Fairness, Transparency and AI Ethics.