Since January 2022 I am a Lecturer in Data Science of the Institute for Complex Systems and Mathematical Biology (ICSMB), King's College, University of Aberdeen (UoA), United Kingdom. Previously, I was an Adjunct Professor of the Physics Institute in the School of Sciences (IFFC), Universidad de la República (UdelaR), Uruguay. I was appointed at the IFFC after my Ph.D. (in Dec. 2014), working as part of the Non-Linear Physics group (NLP) and its experimental branch, the Laboratory of Instabilities in Fluids, which I remain as an Associate Researcher. From June 2020 up to January 2022 I was working as Research Fellow of the Aberdeen Biomedical Imaging Centre (ABIC) in a project involving the development of novel neuroimaging ways to classify Alzheimer's disease and mild-cognitive impairment from large-scale brain networks, which I am still involved.

I have a Ph.D. in Physics from the UoA for the thesis: "The mathematical principles behind the transmission of Energy and Synchronisation in Complex Networks", which was supervised by Dr. Murilo S. Baptista and Prof. Celso Grebogi. The thesis got nominated to the Springer theses award "the best of the best" by the University, which after winning, was published as a book (ISBN 978-3-319-22216-5). Its outcomes (including 6 publications) involve exact and approximate results for the behaviour and stability of complex systems, which are valid for a wide range of systems, but are particularly suitable for modern power-grid models. Before my Ph.D., I got an M.Sc. in Physics from the UdelaR for the thesis: "Synchronisation of coupled electronic oscillators", which was supervised by Prof. Arturo C. Martí and Dr. Cecilia Cabeza. The thesis (including 3 publications) considered the case of an electronic oscillator that models a gregarious firefly, which is implemented experimentally under dual RC circuits and interacts via light pulses. My results include general conclusions for the synchronisation of generic piecewise oscillators.

My research is focused on the study of Complex Systems (i.e., many interacting dynamical systems), where I apply my expertise in Network theory, Dynamical Systems, Numerical Modelling, and Data Analysis. Following a bottom-up approach, I aim to explain and predict the collective behaviours that can emerge -- such as chaos or synchronisation -- from their connectivity. I am also interested in reverse engineering the system's connectivity from observations of its collective behaviours, which I do by following a top-down approach (that is, doing network inference). Since complex systems appear in many disciplines, I collaborate in various inter-disciplinary projects, such as in brain network studies (including Alzheimer's disease, depression, and ageing), consciousness and its alterations (e.g., by studying the sleep-wake cycle of mammals), climate change and forecasting at intra-seasonal time-scales, and the stability of power-grid systems. Through my research, I seek to develop (or apply) new methodologies to analyse data (i.e., image and signal data mining) or static and time-varying networks (e.g., for community detection or spectral characterisations), which are useful in the analysis of real-world complex systems.

In particular, I am fascinated by Network Neuroscience research, where complexity challenges abound -- neurons in the brain create a myriad of dynamical behaviours due to their intricate connectivity and complex substrate and our observations can only access these behaviours by indirect measurements. In this sense, I am interested in answering how do we manage to infer the brain's connectivity from indirect measurements (e.g., EEGs or MRIs)? how do particular pathologies (e.g., Alzheimer's disease or chronic depression) affect the brain's connectivity? what data-driven conclusions can we draw from studying different states of consciousness (e.g., REM sleep)? and how can we develop/improve methods (both, in data mining and analysis) to increase our understanding of these issues?

I am also interested in Fluid Dynamics, specifically, in turbulence and numerical simulations of Navier-Stokes equations; in the mathematical properties of Non-linear Dynamical Systems, mainly, with respect to bifurcation theory; in Non-equilibrium Statistical Mechanics, with a focus on the statistical properties of time-delayed systems and biological networks; and finally, in the development of novel Data Analysis techniques.