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About John
Professor John Cartlidge holds a BSc and a PhD from the University of Leeds. He serves as Professor of Financial Technology in the School of Engineering Mathematics and Technology at the University of Bristol, leading the Financial Engineering Lab—a sub-group of the Intelligent Systems Laboratory—and acting as Academic Team Lead, line-managing ten academic staff. With 25 years of research experience as a computer scientist specializing in artificial intelligence, machine learning, and data science applied to financial markets, Cartlidge previously held positions in industry at Hewlett-Packard Research Laboratories and the London Stock Exchange. He co-founded two fintech startups and has consulted for government and industry, including as academic advisor to the Financial Conduct Authority since October 2018, expert on trading technologies for the UK Government Office for Science, and expert witness in automated trading for the High Court, London. Cartlidge created the University of Bristol's award-winning MSc in Financial Technology with Data Science.
His research specializations include artificial intelligence, machine learning, agent-based modelling, agent-based simulation, data science, evolutionary algorithms, market microstructure, blockchain, decentralised finance, financial forecasting, financial markets, and automated trading. Among his 71 research outputs are key publications such as 'Autonomous virulence adaptation improves coevolutionary optimization' in IEEE Transactions on Evolutionary Computation (2011), 'Studies of Interaction Between Human Traders and Algorithmic Trading Systems' (2011, Foresight Report), and 'Exploring the 'robot phase transition' in experimental human-algorithmic markets' (2012, Foresight Report). Cartlidge has received the Best Paper Award at ICAART 2025 (with Y. Pei and J. Zheng), Best Paper Award at the ACM SIGSPATIAL Workshop on Computational Transportation Science (2017), and Best Student Paper Award at ICAART-2013. His scholarship is evidenced by over 1,039 citations on Google Scholar, contributing to advancements in financial technology, collective intelligence, and algorithmic trading systems.
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