Scholar
M. Ángeles Serrano
Google Scholar ID: PsIxYIsAAAAJ
Universitat de Barcelona
Citations & Impact
Citations
4,190
H-index
29
i10-index
52
Publications
20
Co-authors
33
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Published papers include 'Feature-aware ultra-low dimensional reduction of real networks' (2024), 'Renormalization of networks with weak geometric coupling' (2024), and 'Random graphs and real networks with weak geometric coupling' (2024).
Research Experience
- Research focuses on working at the interface between Network Science and Machine Learning; reducing redundant information to find simplifying patterns in data sets and complex networks through the application of hyperbolic geometrics; studying spatially embedded structural brain networks and exploring the accuracy of hyperbolic space distances in interpreting connectomes across species.
Background
- The essence of complexity is summarized by the old aphorism coined out more than two thousand years ago: «The whole is more than the sum of its parts» (Lao Tse, Tao Te Ching, VI BC; Aristotle, Metaphysics, IV BC). Complex systems consist of a large number of components interacting in such a way that the group as a whole may produce nonlinear unexpected responses, often exhibiting phase transitions, cascades, crises, catastrophes, and other critical and extreme events. These emergent behaviors come along with other amazing features, like self-organization into hierarchical or multiscale structures, self-similarity, self-regulation, memory, or the ability to adapt and to learn. Networks are graph representations of real-world complex systems. We are using networks to unravel the basic principles underlying the structure, function, and evolution of complex systems, and to model and predict them.
