logo_upc2.jpg                                                                   Complex systems and data analysis


Many real-world systems, constituted by nonlinear interacting units, are modelled by networks (or graphs) in which the nodes represent the units and the links represent the interactions among them. These interactions are often only partially known, and we are interested in developing appropriated tools for inferring the network structure, from observed data. Typically, only limited (and noisy) data is available. We are also interested in characterizing the networks inferred from data (for the identification of regime transitions, for uncovering the community structure of highly inter-related nodes, etc.).


We are also working in interdisciplinary applications:


-Climatological time-series: we use nonlinear data analysis tools to infer climatic interactions and signatures of changes in atmospheric dynamics.


- Biomedical images: we use the network approach to develop novel tools for image classification and analysis.


Book: Networks in Climate (H. A. Dijkstra, E. Hernandez-Garcia, C. Masoller and M. Barreiro, Cambridge University Press 2019, ISBN: 9781316275757)


Recent Publications:


Mapping atmospheric waves and unveiling phase coherent structures in a global surface air temperature reanalysis dataset

D. A. Zappala, M. Barreiro, C. Masoller

Chaos 30, 011103 (2020).


Inferring the connectivity of coupled oscillators and anticipating their transition to synchrony through lag-time analysis

I. Leyva and C. Masoller

Chaos, Solitons and Fractals 133 109604 (2020). Arxiv: 2001.08195


Network-based methods for retinal fundus image analysis and classification

P. Amil, F. Reyes-Manzano, L. Guzmán-Vargas, I. Sendiña-Nadal, C. Masoller.

PLoS ONE 14, e0220132 (2019).


Outlier mining methods based on network structure analysis

P. Amil, N. Almeira, C. Masoller

Front. Phys. 7, 194 (2019).


Assessing diversity in multiplex networks

L. C. Carpi, T. A. Schieber, P. M. Pardalos, G. Marfany, C. Masoller, A. Díaz-Guilera, M. G. Ravetti

Sci. Rep. 9, 4511 (2019).


Quantifying interdecadal changes in large-scale patterns of surface air temperature variability

D. A. Zappala, M. Barreiro, and C. Masoller

Earth System Dynamics 9, 383 (2018). Supplementary information


Differentiating resting brain states using ordinal symbolic analysis

C. Quintero-Quiroz, L. Montesano, A. J. Pons, M. C. Torrent, J. García-Ojalvo, C. Masoller

Chaos 28, 106307 (2018). arXiv: 1805.0393





ITN BEOPTICAL (H2020-675512). Read more about this project.


ITN LINC (FP7-289447). Read more about this project. Read more about the results.


Ministerio de Ciencia, Innovación y Universidades, PGC2018-099443-B-I00







Quantifying changes in surface air temperature dynamics during the last 30 years (D. Zappala et al., Earth System Dynamics 2018).

Hilbert analysis of surface air temperature. Watch the video of how seasons evolve during a normal year, a El Niño year and a La Niña year (temporal evolution of the cosine of the Hilbert phase, D. Zappala et al., Chaos 2020).


Fundus image and extracted tree-like network (P. Amil et al., Plos One 2019).


Characterization of the eyes closed- eyes open transition in EEG signals (Quintero-Quiroz et al, Chaos 2018).


Back to Cristina Masoller’s web page