Summary
Virginia Aglietti is a Senior Research Scientist at DeepMind with nine years of experience at the intersection of causality and large language models, focused on causal decision-making methods that identify optimal actions and improve decision efficiency. Her background blends rigorous academic training—a PhD in Statistics from the OxWaSP programme at Oxford/Warwick—with hands-on research roles at DeepMind, The Alan Turing Institute, Microsoft Research, and industry internships at Amazon and the UN. She combines probabilistic and causal inference expertise with practical ML systems, applying theory to real-world decision problems in high-impact research environments. Based in London, she brings a rare mix of economics and statistical training (Bocconi, exchange programs at USC and IIFT) that helps her translate complex causal ideas into actionable decision strategies. An underappreciated strength is her policy- and economics-informed perspective, which sharpens how causal models are evaluated for consequential decisions.
9 years of coding experience
5 years of employment as a software developer
Master of Science (M.S.), Economic and Social Sciences, 110/110 L, Master of Science (M.S.), Economic and Social Sciences, 110/110 L at Università Commerciale 'Luigi Bocconi'
Exchange Program, Exchange Program at University of Southern California - Marshall School of Business
Master of Business Administration (MBA), Exchange Program, Master of Business Administration (MBA), Exchange Program at Indian Institute of Foreign Trade
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at University of Warwick