Meet PhD candidate Anna Maria Biletta
Anna Maria Biletta started as a PhD student at the Centre for Complex Systems Studies last December. She will work on the subject of Spectral Analysis of Random Graphs, under the supervision of Ivan Kryven, Mara Baudena and Anna von der Heydt. Below she briefly introduces herself.
Italy, Switzerland and the Netherland
I was born and raised in Italy, where I completed my Bachelor鈥檚 degree in Mathematics for Finance in Turin. Afterwards I moved to Lausanne, Switzerland, to pursue my Master鈥檚 in Applied Mathematics at 脡cole Polytechnique F茅d茅rale de Lausanne. During my Master鈥檚 programme, I had the opportunity to do an internship with the database group at the CWI in Amsterdam, which gave me the opportunity to get to know the Netherlands and led me to come back later after completing my Master鈥檚 programme.
Graph Theory
I began my journey in the area of combinatorics during my studies at 脡cole Polytechnique F茅d茅rale de Lausanne, and I have been fascinated ever since. It fed my interest in studying discrete structures - in particular in the study of structures that have both combinatorial and probabilistic spirit, such as Random Graphs. I completed my studies with a thesis on 鈥淪aturation Problem in Random Graphs鈥, where I studied the random analogue of an Extremal Graph Theory problem. The thesis gave me a first taste of conducting research on Random graphs. That made me realise how fascinating they are in their essence and showed me the magic of the probabilistic approach.
Complex networks
My research at the Centre for Complex Systems Studies focusses on studying the structure and function of complex networks and understanding their role in in random systems dynamics. The outstanding thing about complex network theory is that it can relate the topological property to the function and dynamics of the system. In particular, I will conduct research on spectral random graph theory and use these results to build a bridge towards random dynamical systems, with particular focus on analysis of their stability and long-term behaviours.
Impact on climate change
My studies will include several real-world system examples that have a clear network structure, such as ecological models. Analysing and understanding models for the reaction of an ecological community based on its internal relationships is essential for predicting the impact of climate change. Earth systems models do not conceal an obvious network, but they are constantly marred by a high degree of randomness and critical phenomena, and transitions are widespread. From the point of view of complex networks, Earth systems can clearly provide critical insights into the underlying dynamics of the evolution, particularly when exposed to climate change.
I鈥檓 happy to be working on such a valuable interdisciplinary and dynamic project within the Centre for Complex Systems Studies!