Miguel Ibáñez Berganza scientific site

miguel [punto] ibanezberganza [chiocciola] imtlucca [punto] com

[...] sólo algún instante de mí podrá sobrevivir en el otro.
J. L. Borges, Borges y yo.

I am a researcher interested in statistical inference, cognitive science and epistemology, with a background in quantum and statistical mechanics.

I am assistant professor at the IMT School for Advanced Studies Lucca, where I teach information theory, statistical inference and probabilistic models of cognition (as part of the teaching offer of the National PhD in Artificial Intelligence).

In my erratic early research activity, I collaborated in various projects (see publications):

As a student and collaborator of Prof. G. Sierra, I contributed to the derivation of (celebrated) exact quantum information laws for the entropy of entanglement (of elementary excitations) in low-dimensional quantum many-body systems of interacting particles near their quantum phase transition, using field-theoretical and exact numerical methods.

In this period, I also contributed to the solution of an exactly solvable model of pairing electrons, and to the description of its superconducting and superfluid phases.

Later as a post-doctoral researcher, I have accidentally worked as well in several problems in statistical physics. Among these: the solution, with methods of ensemble theory, of vector (XY, spherical) statistical models on complex networks and on disordered networks of high-order interactions. This project comprised as well the analysis of the rich emerging collective phases and phase transitions, in the context of the statistical-mechanical description of lasing regimes in non-linear photonics.

More recently, I have been working in a stochastic processes and information-theoretical description of the role plaid by across-neuron noise correlations in information transmission, in simple mathematical models of biological neural networks.

I also happened to propose a novel experimental scheme of analysis of the phenomenon of facial attractiveness, allowing for an accurate inference of the single subject idiosyncratic facial preference criterion. We exploited this method to report, with unprecedented (to the best of my knowledge) precision, the essentially subjective nature of facial attractiveness perception.