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 holds a Ph.D. in theoretical physics from UB and a master in mathematics for finance from the Centre de Recerca Matemàtica CRM. After four years as IT consultant and mutual fund manager, she returned to academia in 2004 to work in complexity science within the franework of network science. She conducted postdoctoral research at Indiana University (USA), the EPFL (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 and belongs to the Editorial Board of the APS journal Physical Review Research. 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 systems -e.g. the human brain, the Internet, molecular networks in the cell, international trade, and many more- 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 their multiscale nature in space and time. I am using netwoks to investigate the role of space in real complex systems, producing maps in a hidden geometry where distances measure the likelihood of interactions. Our focus is also on the impact of time flow, and on multilayer networks in which different types of interactions between a diversity of elements coexist. Our applications cover a wide variety of real systems, from biological to economic and sociotechnological systems, that we characterize using massive data.