Cristina Masoller is a Professor in the Physics Department at the Universitat Politecnica de Catalunya (UPC). Prior to joining UPC in 2004, she spent 18 years at the Physics Department, Faculty of Sciences, University of the Republic, Uruguay, where she was Assistant and Associated Professor. She has 190 peer-reviewed publications, 1 book, 1 issued EU/US patent, 12 PhD supervised thesis and 45 invited talks. She regularly serves on the program committee of international conferences, including CLEO-Europe, Nonlinear Optics, NetSci, CCS, Complex Networks, among others. She is a section editor of Chaos, Solitons and Fractals, topical editor of Optics Letters and editor of Scientific Reports. She is a fellow of Optica, the leading professional society in optics and photonics and a founding member of the Catalan complexity network, complexitat.cat. She received three ICREA Academia Awards (2010, 2016 and 2021). She holds a Ph. D. in physics from Bryn Mawr College, Pennsylvania, USA.

Research interests

Prof. Masoller's research lines are nonlinear dynamics, complex systems, and data analysis. Specifically, Masoller and her collaborators conduct experimental and numerical research on semiconductor laser dynamics, neural dynamics, and data analysis tools to characterize, forecast and control the behavior of complex systems, including extreme events, rogue waves, and tipping points in climate and biomedical signals. She and her collaborators have discovered remarkable similarities (but also differences) between the behavior of excitable lasers and neurons. Her team also developed new analysis tools to extract information from complex signals in fields as diverse as biomedicine and climate. Her research has focused on identifying early warning signs of critical transitions and extreme events. Masoller and colleagues have used symbolic analysis to differentiate brain states and network tools to analyze retina fundus images for early diagnosis of diseases.

Keywords

Photonics, semiconductor lasers, complex systems, nonlinear dynamics, neuronal models, time series analysis, complex networks, extreme events