I am a researcher in computational policy modelling and AI Governance Lead at City Business AI (Traavu Ltd). I build agent-based models that test how a policy distributes access, delay and protection across a real population, including IPS-England, a model of England’s SEND identification and assessment pathway, and the GRID Observatory, on safeguarding underreporting in specialist SEND schools. My path runs through investigative journalism, health data infrastructure at Orodata Science in Nigeria, and practice in specialist SEND education. I am a UKRI Future Leaders Fellowship nominated candidate hosted at Leeds Beckett Law School.

Prof. Christian E. Vincenot is by nature an interdisciplinary researcher with broad scientific interests. He majored in Computer Science / Embedded Systems (i.e. IoT) at the Université Louis Pasteur (Strasbourg, France) while working professionally in the field of Computer Networking and Security. He then switched the focus of his work towards Computational Modelling, writing his doctoral dissertation on Hybrid Modelling in Ecology, and was awarded a PhD in Social Informatics by Kyoto University in 2011 under a scholarship by the Japanese Ministry of Research. He subsequently started a parallel line of research in Conservation Biology (esp. human-bat conflicts) under a postdoctoral fellowship of the Japanese Society for the Promotion of Science (JSPS) (2012-2014). This led him to create the Island Bat Research Group (www.batresearch.net), which he is still coordinating to this date. In 2014, he was appointed as the tenured Assistant Professor of the Biosphere Informatics Laboratory at Kyoto University. He also been occupying editorial roles for the journals PLOS ONE, Frontiers in Environmental Science, and Biology. In 2020, he created Ariana Technologies (www.ariana-tech.com), a start-up operating in the field of Data Science/Simulation and IoT for crisis management.

I work at the intersection of archaeology and artificial intelligence, applying computational modelling to some of prehistory’s hardest questions. My doctoral research used agent-based modelling combined with genetic algorithms to explore the behavioural and biological characteristics of Neanderthals — treating ancient populations not as static artefacts but as dynamic systems that can be interrogated through simulation.
My work reflects a broader conviction: that AI and machine learning are not just tools imported from other disciplines, but frameworks that can reshape how archaeologists ask questions and interpret the past.