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UKRI AI Metascience Early Career Fellow, RoRI

Basil is a Research Fellow at RoRI, where he leverages computational methods to understand how research informs policy. He is a UKRI AI Metascience Early Career Fellow investigating how AI systems will transform the way science reaches policymakers. 

This work builds upon his PhD in Science, Technology, Engineering & Public Policy at University College London (UCL), where he developed machine learning approaches to study how governments access and use research. 

Basil’s research has been supported by partnerships with Elsevier and visiting fellowships at Northwestern University’s Centre for Science of Science & Innovation and Harvard University’s Centre for International Development. His work has been published in journals including Patterns and Sustainable Development, and presented at major international conferences including MetaScience, the Annual International Conference on Science and Technology Indicator, and the Atlanta Conference on Science and Innovation Policy. 

Before academia, Basil co-founded SynSapien, a collective intelligence platform connecting innovators globally to tackle societal challenges. He also worked as a strategic communications consultant for universities and research organisations in Qatar. His interdisciplinary background spans environmental technology (MSc, Imperial College London) and international politics (BSc, Georgetown University). 

His research interests focus on how AI and technology can enhance science by making it more accessible and impactful for society. 

About the UKRI Fellowship 

Basil’s UKRI AI Metascience Fellowship investigates a fundamental question at the intersection of artificial intelligence and evidence-based policymaking: how will AI systems change which scientific evidence informs government decisions? As governments increasingly adopt AI-powered tools for evidence identification, this research will provide the first large-scale empirical analysis of whether AI recommendations systematically differ from human-selected evidence. In collaboration with Northwestern Centre for Science of Science and Innovation and Elsevier, the fellowship will compare what policymakers have historically cited in systematic reviews and policy documents against what AI would recommend for the same topics, at scale. This will reveal the ‘invisible college’ of researchers whose work AI consistently identifies as policy-relevant but who remain unrecognised in current evidence pathways.