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Sandro Azaele (University of Padua, Italy)<\/strong><\/p>\n Sandro Azaele earned his Ph.D. in Physics in 2007 at the University of Padua (Italy) working at the crossroads between statistical physics and ecology. Then, he moved to Princeton University as a research associate. After several international experiences, first at the University of Leeds as a Research Fellow (2009-2012), then as a Lecturer in Applied Mathematics (2012-2016), and eventually as a Visiting Fellow at the Isaac Newton Institute for Mathematical Sciences in Cambridge, in 2019 he moved back to the University of Padua as Associate Professor.<\/span> <\/p>\n H\u00e9l\u00e8ne Morlon (IBENS, Paris)<\/strong><\/p>\n H\u00e9l\u00e8ne Morlon obtained her Ph.D. in “Environmental Sciences” in 2005 at the University of Bordeaux I (France). Then, she spent five years as a postdoctoral researcher in the US between the University of California (Green lab and Potts lab), the University of Oregon (Green lab and Bohannan lab), and the University of Pennsylvania. In 2010 she obtained a tenured position at CNRS, in Paris, and a year later she became an associate member of the Center for Interdisciplinary Research in Biology, affiliated with Coll\u00e8ge de France. Since 2014 she is Research Director (DR1) of CNRS and leads her team at the Institut de Biologie de l\u2019Ecole Normale Sup\u00e9rieure<\/em>. She combines her expertise in mathematics, evolution as well as bioinformatics to study macroevolution, spatial distribution of phylogenetic diversity, and microbial communities.<\/p>\n <\/p>\n Maria del Rio Chanona (University College London)<\/strong><\/p>\n Maria del Rio Chanona is Lecturer in the Computer Science department at the University College London. Her research draws from network science, machine learning, and agent-based modelling and focuses on the future of work, the net-zero transition, and the economic impact of the Covid-19 pandemic. Maria has a PhD in Mathematics from the University of Oxford, where she was part of the Complexity Economics group of the Institute for New Economic Thinking at the Oxford Martin School. Maria was a JSMF research fellow at the Complexity Science Hub, Vienna and a visiting scholar at the Harvard Kennedy School. She has worked alongside international policy organisations, including the International Monetary Fund and the International Labour Organisation. Before her postgraduate studies, Maria did her undergraduate studies in Physics at UNAM, Mexico.<\/p>\n <\/p>\n Tim Rogers (University of Bath)<\/strong><\/p>\n Tim Rogers is a Professor of Mathematics \u00a0at the University of Bath. His research interests include graphs and networks, applied stochastic processes, and emergent phenomena. He is particularly interested in understanding and predicting the behaviour of complicated random events and processes, in particular when there is network or spatial structure involved. Most of his work is connected to the idea of emergence: how large scale order can be created out of the random interactions of individual particles or organisms. The problems come from a wide range of sources including ecology, biochemistry, random matrix theory, social dynamics, and stochastic processes.<\/p>\n <\/p>\n Fabio Caccioli (University College London)<\/strong><\/p>\n Fabio Caccioli is a Professor of Complex Systems at University College London (UCL). Before joining UCL, he served as a Research Associate at the Centre for Risk Studies, University of Cambridge, and was a Postdoctoral Fellow at the Santa Fe Institute. Fabio earned his PhD in Statistical Physics from the International School for Advanced Studies (SISSA) in Trieste, Italy. His research explores the application of complex systems and statistical mechanics to economic and financial systems, with a particular focus on systemic risk. Other research interests include complex networks, the science of science, and non-equilibrium statistical mechanics.<\/p>\n <\/p>\n
\nHe is internationally recognized for his expertise in statistical mechanics, ranging from probabilistic approaches to Dynamical Mean-Field Theory, with applications in theoretical ecology.<\/span><\/p>\n<\/a><\/p>\n<\/p>\n<\/a><\/p>\n<\/a><\/p>\n