Cristina Masoller (1963, Montevideo, Uruguay) is Full Professor at the Physics Department, Universitat Politecnica de Catalunya (UPC). She received the bachelor and master degrees in physics from Universidad de la República, Uruguay, where she was assistant professor (1989-2004). She received the PhD degree in physics from Bryn Mawr College (USA) in 1999. She was a postdoctoral researcher at University of Nice, at Bangor University and at Universitat de les Illes Balears (2000-2002). She joined UPC with a Ramon i Cajal fellowship in 2004, became Associate Professor in 2009 and Full Professor in 2018. She is co-author of more than 160 articles that have received 5700 citations. She has supervised 11 PhD thesis. She is member of the Editorial Board of several interdisciplinary journals. She is a Fellow of the Optical Society (OSA), the leading professional society in optics and photonics. She was recognized for contributions in the area of nonlinear dynamics of optical systems.

Research interests

She studies complex systems in a wide range of interdisciplinary fields. She is an expert in nonlinear dynamics of semiconductor lasers and has discovered some remarkable similarities (but also differences) between the behavior of excitable lasers and neurons. She has used and developed nonlinear analysis tools to extract information from complex signals in fields as diverse as biomedicine and climate. Her research interest has been focused on identifying early warning signals of critical transitions and extreme events. She has used Hilbert analysis to identify geographical regions where atmospheric dynamics has experienced pronounced variations in the last 30 years, she has applied symbolic analysis to EEG signals to differentiate brain states, and she has used complex network tools to analyze retina fundus images for early diagnosis of diseases. Her current interests also include sub-seasonal atmospheric predictability and laser speckle pattern analysis for optical remote sensing.

Keywords

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