Dr Nicolas Rubido Obrer

Specialisms

• Applied Mathematics
• Applied Physics
• Computational Physics
• Numerical Analysis
• Physics

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is HESA open data, published under the Creative Commons Attribution 4.0 International

Research Overview

My research interests are focused on Complexity issues. In general, the complex systems that I research involve Coupled Dynamical Systems, namely, many non-trivially interacting sub-systems. I try to measure, explain, and/or predict their collective behaviour (for example, the emergence of synchronization or chaotic dynamics) in terms of how they are inter-connected, namely, in terms of the topological features of the underlying network topology (i.e., Graph Theory).

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. I am intereseted in questions such as, 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 unserstanding 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

I am a Research Fellow on a Roland Sutton Academic Trust (RSAT) funded project led by Dr. V. Vuksanovic. The project is 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 connectomes will be linked to specific behavioral and cognitive scores, aiming to help in early diagnosis.

Selected publications

• 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).

Supervision