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One of the best things about Emmanuel is the
beautiful grounds, and the College
swimming pool is fantastic

Dave, 3rd Year

Dr Daniele Cassese

Photo of Dr Daniele Cassese

BSc (Siena), MSc (Siena), PhD (Siena)

Research Fellow
Mead Research Fellow

Biography

I completed both my undergraduate degree and masters in Economics at the University of Siena, Italy, where I also started my PhD. I moved to Cambridge in 2012, initially for a one-year visiting at the Department of Mathematics and at Christ’s College, which then I managed to extend till the completion of my PhD. During this period I was also Teaching Associate at Queens’ College. I started my Postdoc at the University of Namur, Belgium, in 2016 and moved to the Oxford Mathematical Institute in 2017.

I have always been an avid reader, and reading remains one of my favorite activities during my free time. I used to play the piano, and my mid term plan is to start playing again properly


Research

My research focuses on complexity, read through the lens of networks: I studied how connectivity influences dynamical processes, in particular trade, epidemic spreading and natural selection. In my dissertation I proved the conditions under which cooperation prevails in evolutionary games, and proved a version of the Second Welfare Theorem for networks in an Edgeworth barter process. I am also interested in topological data analysis, and during my postdoc I investigated the creation of knowledge in mathematics through the investigation of co-occurrence relations between concepts in mathematical articles using persistent homology.

As Mead Research Fellow I am interested in completing the publication of the articles I am writing and in bringing forward my research agenda. In particular I am working on models of dynamics on higher order structures (networks where interactions are not just between couples of agents but also groups of several dimensions). Moreover, I am interested in exploring inequality in a network perspective, both in terms of mathematical models and in terms of data analysis using persistent homology.


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