Dani is a Ph.D. student in economics at the University of Sussex and an enrichment student at The Alan Turing Institute. 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 undertaking an internship at the Alan Turing Institute, where he fell in love with the data science community.

Research aside, Dani is quite passionate about pedagogy. He is an Associate Fellow of the Higher Education Academy and has a broad experience teaching different subjects, spanning from macroeconomics to statistics and econometrics.

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), and an MSc in development economics from the University of Sussex (UK). He has also acted as a consultant for the International Growth Centre in London and the Institute of Development Studies.

When he is not too busy doing nerdy things, Dani enjoys playing the guitar and football. Just kidding, he is awfully bad at both.

Research interests

Dani’s main research areas relate to innovative applications of machine learning techniques, network analysis and computational methods to the fields of political economy and development economics. In the first chapter of his Ph.D. thesis, he combined his econometrics background with machine learning tools in a NLP setting to study the effect of exogenous shocks on the rhetoric of British MPs. This was performed comparing tens of thousands of tweets before and after terroristic attacks.

His current research focuses on strategic behaviour and voting in the United Nations General Assembly. Through a newly assembled dataset, the study tries to assess the extent and dynamics of votes' exchange within the institution, where countries can secretly collude in order to pursue their own goals. Gaining a better understanding of the patterns underlying this form of cooperative behaviour will help to inform the design of effective institutions in different political contexts. The study is part of the research project on vote trading networks at the Turing.

During the time at the Institute, Dani hopes to further develop his set of methodological skills and collaborate with a diverse community of data scientists to promote a data-driven approach to public policy and decision making.