Dani is a research associate in the Policy Modelling Theme, within the Institute's public policy programme.
After an intensive period spent in political activism, he developed a keen interest in a more quantitative approach to political dynamics and decision making. This led him to explore methodologies from different fields and disciplines, ranging from survey design to text and network analysis. Eventually, he ended up at The Alan Turing Institute, where he fell in love with the data science community.
Dani holds a BA in political science and international relations from Università Degli Studi di Pavia (Italy), a Certificat d’Études Politiques from Sciences Po Toulouse (France), an MSc in development economics and a PhD in economics from the University of Sussex (UK). He has also acted as a consultant for IGC, IDS, GAIN, and UNDP.
When he is not too busy doing nerdy things, Dani still engages in politics through different grassroots associations that carry out social-impact projects at the community level. He is a passionate runner, swimmer, and (more recently) boxer.
Dani’s research interests span a broad range of topics across political science, development economics and political economy. He has worked on social media, sustainable development, food policy, public financial management, trade and innovation, political violence, and decision-making dynamics. From the methodological side, in his research he has employed tools from text analysis, network science, agent-based modelling, machine learning and more traditional econometrics.
At the Turing, he works on the ESRC-funded project “Agent Computing and AI to Achieve the 2030 Agenda”, where he applies computational methods to inform policymakers on how to allocate public resources to achieve the UN Sustainable Development Goals.