Research

machine learning, synthetic data, artificial data, generative modelling, prognostic modelling, diagnostic modelling

Underpinning Bioengineering, Cancer Models, Cardiovascular Models, Infectious Disease and Inflammation Models

Interests

My interdisciplinary research focuses on analysis of large-scale, longitudinal health records, including biomarkers, imaging, multi-omics, and routine care data to optimise treatment strategies, improve patient care and mitigate health inequities. Apart from clinical data, I also work on incorporating human behaviour data, such as those generated from wearable devices and smartphones, with a particular focus on mental health. Methodological aspects of my research include generative architectures, such as GANs VAEs, and Diffusion Models, for synthetic (artificial) patient data, explainable AI methods and sample complexity. The overarching objective of my research is to integrate predictive modelling at the bedside and bring the acquired evidence back, in a continuously improving feedback loop, consequently establishing a learning digital health system.