Research

Description

Nonlinear dynamics refers to the behaviour of systems where changes in the inputs can generate disproportionate changes in the outputs. These type of systems usually contain non-linear relationships between variables, leading to complex and unpredictable behaviours, such as those observed in the atmosphere, power-grids, the brain, and ecosystems, to name a few.

Chaotic systems are a subset of nonlinear systems. Their main characteristic is their sensitivity to initial conditions, meaning that small uncertainties in the initial state grow exponentially fast, rendering predictions impossible after a short time. Another characteristic is that their behaviour is aperiodic, meaning that they appear to behave randomly, although they follow deterministic rules.

My aims

I am interested in extracting statistical properties (such as the degree of uncertainty, recurrence, predictability, or chaoticity) of non-linear and chaotic systems from finite precision measurements and short signals.

Collaborators

• Dr Murilo S. Baptista, University of Aberdeen, ICSMB, U.K.

• Prof Celso Grebogi, University of Aberdeen, ICSMB, U.K.

• Dr Melvyn Tyloo, University of Exeter, Living Systems Institute, U.K.

• Dr Chris Antonopoulos, University of Essex, SMSAS, U.K.

• Dr Juan Gancio, Universitat Politécnica de Catalunya, DONLL, Spain.

• Prof Cristina Masoller, Universitat Politécnica de Catalunya, DONLL, Spain.

• Prof Arturo C. Marti, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

• Dr Caracé Gutierrez, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

• Dr Nicolás Dı́az, Universidad de la República, Department of Atmospheric Sciences and Oceanic Physics, Uruguay.

• Dr Rodrigo F. Pereira, Universidade Tecnológica Federal do Paraná, Brazil.

Description

A complex system is a system composed of many interacting parts whose collective behaviour cannot be easily explained from the behaviour of the individual parts. For example, ecosystems, neural circuits, transportation networks, power-grids, biological systems, or the climate system can be considered a complex system. The self-organised collective behaviours that can appear in these systems are known as emergent phenomena, and are strongly dependent on the way that the parts are interconnected forming a network.

My aims

On the one hand, I am interested in characterising complex network structures  for example, using spectral characteristics, distances, or by community structures, as well as designing methods to infer the underlying network structures from time-series measurements. On the other hand, I seek to understand and forecast the collective behaviours emerging in complex systems (particularly, in relation to the network structure), such as synchronisation, intermittency, chimeras, and chaos.

Collaborators

• Dr Murilo S. Baptista, University of Aberdeen, ICSMB, U.K.

• Prof Celso Grebogi, University of Aberdeen, ICSMB, U.K.

• Dr Melvyn Tyloo, University of Exeter, Living Systems Institute, U.K.

• Dr Rodrigo García-Tejera, University of Edinburgh, Institute of Genetics and Cancer, U.K.

• Dr Juan Gancio, Universitat Politécnica de Catalunya, DONLL, Spain.

• Prof Cristina Masoller, Universitat Politécnica de Catalunya, DONLL, Spain.

• Dr Marcos G. Quiles, Universidade Federal de São Paulo, ICT, Brazil.

• Prof Elbert E. N. Macau, Universidade Federal de São Paulo, ICT, Brazil.

• Prof Arturo C. Marti, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

• Dr Caracé Gutierrez, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

Description

Chronobiology is the scientific study of biological rhythms—the natural cycles in living organisms that follow regular, predictable patterns over time. These rhythms influence physiology, behaviour, and health across nearly all forms of life, from bacteria to humans.

Network neuroscience is a field that studies the brain as a complex network of interconnected elements, using tools and concepts from graph theory, physics, computer science, and systems biology. Instead of focusing on individual brain regions in isolation, it examines how patterns of connections among neurons, brain regions, or functional systems give rise to cognition, behaviour, and disease.

My aims

In terms of chronobiology, I am interested in understanding the sleep-wake cycle and its alterations, and understanding how neuronal populations or fireflies can synchronise. On the other hand, I am also interested in understanding how neurons and brain regions interconnect (which implies inferring the underlying functional networks from measurements/signals) and how these are altered due to depression, healthy ageing, dementia, and other neurodegenerative diseases.

Collaborators

• Dr Murilo S. Baptista, University of Aberdeen, ICSMB, U.K.

• Prof Celso Grebogi, University of Aberdeen, ICSMB, U.K.

• Prof Bettina Platt, University of Aberdeen, Institute of Medical Sciences, U.K.

• Dr Rodrigo García-Tejera, University of Edinburgh, Institute of Genetics and Cancer, U.K.

• Dr Vesna Vuksanovic, Swansea University, Health Data Science, U.K.

• Prof Cristina Masoller, Universitat Politécnica de Catalunya, DONLL, Spain.

• Dr Juan Gancio, Universitat Politécnica de Catalunya, DONLL, Spain.

• Prof Arturo C. Martí, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

• Dr Javier Brum, Universidad de la República, Acoustic Ultrasound laboratory (IFFC), Uruguay.

• Dr Alejandra Kun, Clemente Estable Biological Research Institute, Uruguay.

• Dr Victoria Gradin, Universidad de la República, Centre for Basic Research in Psychology (CIBPsi), Uruguay

• Prof Pablo Torterolo, Universidad de la República, Laboratory of Sleep Neurobiology, Uruguay.

• Dr Claudia Pascovich, Universidad de la República, Laboratory of Sleep Neurobiology, Uruguay.

• Dr Matías Lorenzo Cavelli, Universidad de la República, Laboratory of Sleep Neurobiology, Uruguay.

• Dr Joaquín González, New York University, Grossman School of Medicine, U.S.A.

• Dr Gonzalo Marcelo Ramírez-Ávila, Université de Namur, naXys, Begium.

• Prof Dante Chialvo, Universidad Nacional de San Martín, CEMSC3, Argentina.

Description

Signal processing is an extremely broad field, applicable to many areas of research that involve analysis, manipulation, creation, and transmission of signals. For example, within signal processing we have data mining, which is a collection of methods used to extract useful information from datasets and is related to feature engineering (a pre-processing step for other methods such as machine learning).

Signals contain information (in the mathematical sense), and information theory is concerned with the classification, storage, and quantification of information, data, signals, and related concepts. For example, one may want to quantify the degree of uncertainty or predictability in a signal, which can be quantified using entropy or mutual information, respectively.

My aims

I am interested in developing methods to extract meaningful information from signals (time series and imaging) using theories from non-linear dynamical systems (i.a., symbolic dynamics, sofic shifts, and Markov partitions) and applying them in real-world scenarios (such as to improve polysomnography or for early detection of abnormal neurodegeneration).

Collaborators

• Dr Murilo S. Baptista, University of Aberdeen, ICSMB, U.K.

• Prof Celso Grebogi, University of Aberdeen, ICSMB, U.K.

• Prof Cristina Masoller, Universitat Politécnica de Catalunya, DONLL, Spain.

• Dr Juan Gancio, Universitat Politécnica de Catalunya, DONLL, Spain.

• Dr Jordi Tiana-Alsina, Universitat de Barcelona, Departament de Física Aplicada, Spain.

• Dr Andrés Aragoneses, Physics Department, Whitman College, U.S.A.

• Prof Arturo C. Martí, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

• Dr Caracé Gutierrez, Universidad de la República, Nonlinear Physics group (IFFC), Uruguay.

• Dr Nicolás Dı́az, Universidad de la República, Department of Atmospheric Sciences and Oceanic Physics, Uruguay.

• Dr Rodrigo F. Pereira, Universidade Tecnológica Federal do Paraná, Brazil.

Description

Atmospheric physicists study the atmosphere and climate of the Earth (or other planets). They want to understand the structure and dynamics of the atmosphere, how it evolves over time, and how humans and natural processes affect the climate.

Atmospheric physics is part of fluid dynamics, which is the study of how liquids and gases flow. In particular, hydrodynamic stability is the field which analyses the stability and the onset of instability of fluid flows, including turbulence.

My aims

My interests lie in deriving data-driven models of atmospheric phenomena to improve predictability and develop numerical simulations that can complement experimental set-ups of fluid instabilities.

Collaborators

• Dr Roland Young, University of Aberdeen, ICSMB, U.K.

• Prof Marcelo Barreiro, Universidad de la República, Department of Atmospheric Sciences and Oceanic Physics, Uruguay.

• Dr Nicolás Dı́az, Universidad de la República, Department of Atmospheric Sciences and Oceanic Physics, Uruguay.

• Dr Cecilia Cabeza, Universidad de la República, Laboratory of Fluid Instabilities, Uruguay.

• Dr Daniel Freire, Universidad de la República, Laboratory of Fluid Instabilities, Uruguay.

• Dr Nicasio Barrere, Universidad de la República, Laboratory of Fluid Instabilities, Uruguay.

Description

The stability of power grids is crucial for safe and stable operation during and after a disturbance. Disturbances are inevitable (such as short circuit faults or changes in load), so the power systems need to be designed to behave satisfactorily during and after these disturbances.

Stochastic dynamics studies the behaviour of systems influenced by randomness, combining probability theory with dynamical systems to model and predict complex phenomena. In relation to power systems, stochasticity can be seen in the current smart grids because of intermittent renewable energy sources, prosumers (consumers that also "produce" energy to supply into the grid), and random loads.

My aims

I seek to understand how the connectivity (at the transmission level) affects the voltage-angle stability, which is a key aspect of maintaining and returning to equilibrium after a disturbance (given that instability can lead to blackouts with disastrous consequences.  I am also interested in finding switch states that optimise the load distribution in islanded hybrid power systems.

On the other hand, I am interested in understanding how the structure of the network interconnecting the components of a stochastic complex system affects its communication and synchronisation, which is relevant in many systems, including power grids, neuronal circuits, surface growth, and stock returns.

Collaborators

• Dr Murilo S. Baptista, University of Aberdeen, ICSMB, U.K.

• Prof Celso Grebogi, University of Aberdeen, ICSMB, U.K.

• Dr Rodrigo Carareto, Insper Institute of Education and Research, São Paulo, Brazil.