Tania Dottorini is Professor of Artificial Intelligence in Infectious Diseases at King’s College London. She progressed from Assistant Professor to Full Professor between 2016 and 2025 at the University of Nottingham, following a Marie Curie European Fellowship at Imperial College London. Her research integrates artificial intelligence, bioinformatics and big data mining to address to address complex challenges in antimicrobial resistance (AMR) and infectious diseases. She aims to identify key drivers and causal mechanisms underlying the emergence, evolution, and transmission of new genetic variants of resistant pathogens and novel AMR traits, while exploring how host, co-infections, ecological, environmental and climate interactions influence the dynamics of infections and resistance across interconnected systems.

She uses artificial intelligence and large-scale data mining frameworks to integrate heterogeneous clinical, genomic, microbiome, environmental and climate data, to investigate antimicrobial resistance emergence and spread across interconnected systems. Her work drives advancements in diagnostics, surveillance, monitoring, early warning systems, and treatment selection. She has secured more than £17 million in research funding as Principal Investigator and is currently leading several major international programmes. These include an MRC funded project focused on AI driven discovery of novel therapeutic targets for resistant infections together with research organisations and governmental agencies in Bangladesh; a BBSRC programme integrating multi omics, microbiome science and precision farming to investigate AMR dynamics within One Health; a JPIAMR European initiative developing a cloud based, AI powered One Health surveillance platform across Europe and Africa; and an EU EDCTP consortium spanning Europe and Sub Saharan Africa to tackle AMR and diarrhoeal diseases in partnership with hospitals, companies and public health agencies.

She is Editor in Chief of Microbiology Horizons and actively contributes to shaping the integration of artificial intelligence, big data and microbiology.