Giulia Cencetti earned her PhD degree at the University of Florence. Her interdisciplinary background (one year studying biology, then Bachelor’s and Master’s Degree in Physics and now a PhD in Information Engineering) took her to work on complex systems and in particular on the fascinating branch of dynamical systems on complex networks. She developed different models to explore the impact of network topology on the dynamical behavior of a system, having as an ultimate goal to apply a strategic external control. Applications are in biology, ecosystems stability, urban traffic, and information flow.
Sebastian is a PhD student at the University of Bath (UK). He holds a BSc in Physics and an MSc in Theoretical Physics both from the Sapienza University of Rome, where he specialised in statistical mechanics of disordered systems. His interest in the interdisciplinary applications of statistical mechanics led him to work in the area of complex networks. In particular, his thesis focuses on identifying the possible underlying mechanisms responsible for shaping the large-scale structure of complex networks. Amongst others, his research interests include strongly disordered systems, statistical inference, and optimisation problems.
Benjamin studied Statistics in Dortmund and Genoa (Bachelor's and Master's Degree). Currently he is a PhD student and Research Assistant at the Department of Statistics at the LMU in Munich. His research interests lie in the modeling of complex networks, where he specifically attempts to find further forms of representation for the data-generative process as well as corresponding novel estimation routines.
Lizhi is a PhD student in the EPSRC Centre for Doctoral Training in Statistical Applied Maths at Bath (SAMBa). He is also an enrichment student in the Alan Turing Institute, London. His research interests include Bayesian statistics, network science and machine learning. He became fascinated with complex network during his undergraduate study in applied mathematics. He decided to do research in network after a summer placement in GlaxoSmithKline where he extracted biomedical networks from literature using text mining techniques. In Lizhi’s PhD, he focuses on community detection in networks. The goal of his PhD project is to develop a series of models and associated inference algorithms that are capable of extracting large-scale structures from network data, in a manner that includes realistic assumptions (such as preferred mixing patterns, growth dynamics, spatial embedding, etc.). The main methodology will be the Bayesian construction of generative network models, as well as algorithmic inference techniques such as MCMC, expectation-maximisation and variational methods.