Serrano, M. Ángeles
ICREA Research Professor at Universitat de Barcelona (UB).
Experimental Sciences & Mathematics
Short biography
M. Ángeles Serrano is an ICREA Research Professor at the Dept. of Condensed Matter Physics of the University of Barcelona (UB). She obtained her Ph.D. in Physics at UB with a thesis about gravitational wave detection, and a master in mathematics for finance from the Centre de Recerca Matemàtica CRM. After four years in the private sector as IT consultant and mutual fund manager, she returned to academia in 2004 to work in the field of complex networks. She completed her postdoctoral research at Indiana University (USA), the École Polytechnique Fédérale de Lausanne (Switzerland) and IFISC Institute (Spain). She came back to Barcelona in 2009, when she was awarded a Ramón y Cajal Fellowship at UB. M. Ángeles obtained the Outstanding Referee Award of the American Physical Society. She is a founding member of Complexitat, the Catalan network for the study of complex systems, and a promoter member of UBICS, the UB Institute of Complex Systems.
Research interests
Complex networks -e.g. the Internet, human brain, molecular networks in the cell, or international trade- are ubiquitous and around us. All of them, regardless of their origin, talk a common language that we are starting to understand. A major challenge for a better comprehension of the relation between their structure and function, and so for the prediction of their evolution and adaptation capabilities, is the characterization of the multiscale nature of networks in space and time. We are investigating the role of space in real networks, producing maps in a hidden geometry where distance measures the likelihood of interacttions and enables multiscale unfolding. Our focus is also on the impact of time flow on their structure and function, and on multilayer networks in which different types of interactions or nodes coexist. Our applications cover a wide variety of real systems, from biological to economic and sociotechnological systems, that we characterize using massive data.