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My early career research achievements can be synthesised as follows:
Exact results in the quantum many-body problem and quantum information. Mainly during my PhD (2007-2011) I contributed to three corpora of results: the study of the quantum entanglement (of elementary excitations) in low-dimensional many-body systems; the exact solution of a family of pairing models of superfluidity and superconductivity; the analysis of quantum entanglement among non-spatial partitions of the Hilbert space (energy and momentum space) in extended quantum systems.
We demonstrated, in particular, exact (and celebrated, see [Goldstein+ 2018]) laws relating the entanglement entropy of quantum excitations in low-dimensional critical systems of many interacting bodies, with the universal quantities of the field theories that describe the continuum limit of such systems.
I these projects, I derived part of the analytical results (as the entanglement entropy of primary operators in [IB+ 2010]), and developed the algorithms (mainly exact diagonalization, Bethe ansatz and BCS-like equations integration) that performed the numerical solution of the Schroedinger equation and the computation of the entanglement entropy of the associated wavefunctions.
Fundamental statistical mechanics of disordered systems. In two subsequent post-doctoral fellowships (IPCF-CNR/Sapienza University, 2011-2014; INFN-Parma/University of Parma, 2014-2016) I have worked in several fundamental topics in classical statistical mechanics. Mainly: the emergence of chaos in biologically inspired models of neural dynamics; the study of critical behaviour and the superfluid transition of vector statistical models on disordered and long-range networks; the statistical physical description of nonlinear processes in lasers and random lasers.
We successfully modelled, in particular, the transition to laser generation as a thermodynamic phase transition, uncovering novel phenomenology of the associated spin models, such as ergodicity breaking, replica symmetry breaking, non-equipartite energy condensation, and topological phase transitions, as a function of the properties of the network of interactions. In these years, I developed as well a fundamental description of finite size effects in metastable phases of some statistical models.
On the methodological side, I developed ad hoc, original algorithms for (parallel, and high-performing, see [I-B+ 2013]) Markov Chain Monte Carlo sampling of vector spin models on disordered networks, and of 4-body interacting systems with disordered quadruplet couplings (see [Antenucci+ 2015]). I extended the so called Langer theory of metastability, as well as the evaporation-condensation theory, beyond the Ising model paradigm, validating both against numerical (cluster, Wolff) dynamic MCMC sampling.
Statistical inference applied to neuroscience and cognition/decision making. During the second research internship in Sapienza University (2016-2020), I happened to develop a novel experimental scheme of analysis of the phenomenon of facial attractiveness. By using an interactive data-generation method where participants “sculpted” their preferred facial images, along with unsupervised probabilistic models, we proved, with unprecedented precision (to the best of my knowledge), that facial attractiveness is fundamentally subjective, and strongly determined by observer’s gender.
On the methodological side, I devised the experimental design and develop the associated software for the human-computer interaction algorithm (based on image deformation and genetic algorithms). I developed the tools for the statistical analysis (see [I-B+ 2019] [I-B+ 2020]) devoted to the unsupervised inference of the subject gender, and to the inference of the relevant order of the interaction between facial landmarks.
In this period, I developed as well statistical methods of noise-cleaning of correlation and precision matrices in the analysis of human brain fMRI activity series —a line of research that I keep working on.
Analytical theory of non-Markovian stochastic processes, with applications to information transmission in neural networks. During my internship in IIT (2021-2023), I developed a theory of information transmission in neural networks, describing how (pre-synaptic) neuronal noise correlations shape information transmission in (post-synaptic) biological and artificial neural networks. In particular, we successfully provided a clear theoretical explanation of a seemingly paradoxical phenomenon regarding the influence of noise correlations in signal transmission, that may play an important role in biological neural networks (see[I-B+ 2025]).
On the methodological side, I derived one of the (apparently) few analytical results regarding the first passage time statistics of a fundamental non-Markovian stochastic processes (the colored-noise random walker in a harmonic potential).