Dr Nicolas Rubido Obrer

Biography

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.

External Memberships

• Affiliations
  • Universidad de la República (UdelaR), Instituto de Física de Facultad de Ciencias (IFFC), Montevideo, Uruguay - Adjunct Professor (on-leave)

• Memberships

  • Programa de Desarrollo de las Ciencias Básicas (PEDECIBA), Ministerio de Educacióon y Cultura (MEC), Uruguay - Researcher (Grado 3)

  • Sistema Nacional de Investigadores (SNI), Agencial Nacional de Investigación e Innovación (ANII), Uruguay - Associate Researcher

• Review Editor:
  • Frontiers in Computational Neuroscience
  • Frontiers in Network Physiology - Networks in the Brain System

Prizes and Awards

• Young Researchers Award – Dynamics Days Europe conference (supported by the European Physical Society, AIP publishing, and the Chaos journal), Greece 2016
• Springer theses award: “the best of the best” – Spinger, Germany 2015
• SUPA Studentship prize (No. 1802), 2011 – Scottish Universities Physics Alliance

Research Overview

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 of neural/biological networks; and finally, in the development of novel Information Theory and Data Analysis techniques.

Current Research

Selected publications

• Critical parameters of the synchronisation's stability for coupled maps in regular graphs, Chaos, Solitons & Fractals 158, 112001 (2022).

• Small-worldness favours network inference in synthetic neural networks, Nature Scientific Reports 10, 2296 (2020).
  

• Decreased electrocortical temporal complexity distinguishes sleep from wakefulness, Nature Scientific Reports 9, 18457 (2019).

• Entropy-based Generating Markov Partitions for Complex Systems, Chaos: an Interdisciplinary Journal of Nonlinear Science 28(3), 033611 (2018).

• Dynamical detection of network communities, Nature Scientific Reports 6, 25570 (2016).

Past Research

I was a Research Fellow on a Roland Sutton Academic Trust (RSAT) funded project, led by Dr. V. Vuksanovic. The project was focused on developing a model of brain-impaired-functions in Alzheimer’s disease from large-scale brain networks; rather than on existing diagnostic categories. The objective of the overall project is to develop novel neuroimaging-informed ways to classify Alzheimer’s disease and mild cognitive impairment. Neuroimaging-defined networks can be linked to specific behavioural and cognitive scores, which aim to help in early diagnosis.

Collaborations

Ongoing (open-ended)

• Analysis of brain networks in Alzheimer's disease from functional MRI data: Dr. Vesna Vuksanovic (Swansea University Medical Research, UK), Prof. Chialvo (Universidad General de San Martín, CONICET, Argentina), Prof. Ignacio Cifre (Universitat Ramon Llull, Spain)

• Analysis of brain networks in depression from functional MRI data: Dr. Victoria Gradín (Centre for Basic Research in Psychology, Universidad de la República, Uruguay), Dr. Ignacio Rebollo (DIfE, German Institute of Human Nutrition Potsdam-Rehbrücke)

• Analysis of mice vasculature networks from functional ultrasound data: Dr. Javier Brum & Prof. Carlos Negreira (Laboratory of Acoustic Ultrasound, Universidad de la República, Uruguay), Dr. Alejandra Kun (Clemente Estable Institute of Biological Research, Uruguay)

• Analysis of consciousness from sleep-wake state's EEG data: Prof. Pablo Torterolo (Laboratory of Sleep Neurobiology, Universidad de la República, Uruguay), Dr. Diego Mateos (Universidad Autónoma de Entre Ríos, CONICET, Argentina)

• Stability analyses of power grids at the transmission level and phase synchronisation: Dr. Murilo S. Baptista and Prof. Celso Grebogi (ICSMB, University of Aberdeen, UK)

• Development of climate models and forecast at the intra-seasonal time-scale: Prof. Marcelo Barreiro (Department of Atmospheric Sciences, Universidad de la República, Uruguay)

• Development of community detection methods for network analysis: Dr. Marcos G. Quiles and Prof. Elbert E. N. Macau (Institute of Science and Technology, UNIFESP, Brazil)

• Development of network and data analysis methods: Prof. Cristina Masoller (Universitat Politécnica de Catalunya, DONLL, Spain), Prof. Arturo C. Marti and Dr. Cecilia Cabeza (Non-Linear Physics group, Universidad de la República, Uruguay), Dr. Murilo S. Baptista and Prof. Celso Grebogi (ICSMB, University of Aberdeen, UK)