Matthew Madgwick


Matthew Madgwick


Research Scientist


IBM Research Europe - United Kingdom Daresbury, England


Matthew Madgwick is an AI Scientist and Computational Biologist, currently working as a Research Scientist in the Application of artificial intelligence (AI) on the Healthcare and Life Sciences (HCLS) team at IBM Research Europe - Daresbury. Matthew's work revolves around combining high-performance computing and AI to analyse massive sequencing datasets at the intersection of computational science and biology.

Matthew's research career began during his second year of undergraduate studies at the Earlham Institute, where he was introduced to systems biology. As an intern with the Korcsmáros Group, he delved deeper into the field, developing an algorithm to contextualise autophagy-related proteins within a molecular interaction network. This experience prompted Matthew to pursue a final-year research project. He developed an integrated network-medicine and machine learning pipeline to identify genomic prognosis indicators in ulcerative colitis. These projects acted as a springboard, fueling his passion for exploring the vast potential of machine learning in life sciences.

During his industrial PhD, with BenevolentAI and the Korcsmáros Group, Matthew explored the microbiome's role in Inflammatory Bowel Disease (IBD) through state-of-the-art machine learning and systems biology approaches. Matthew's research involved the identification of prognostic indicators from vast amounts of omic data, including metagenomics, metabolomics, and metaproteomics, by leveraging the power of machine learning approaches. Furthermore, by combining these approaches with host-microbiome interactions and systems biology, his research enhanced our understanding of the complex relationship between our health and the microbiota. In addition to his PhD, Matthew published multiple papers on IBD, protein-protein interaction modelling and other gut-related diseases, giving him extensive experience in different fields such as software engineering, multi-omic integration methods, and single-cell RNA-seq.


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