Andrea Duggento

Professore Associato

Macro-area: Fisica Applicata, didattica e storia della Fisica
SSD: FIS/07 - SC: 02/D1

duggento@med.uniroma2.it

Biografia

Andrea Duggento, professore Associato di Fisica Medica presso l’Università di Tor Vergata, ha conseguito la laurea e il master in Fisica teorica presso l’Università di Pisa e il dottorato di ricerca in Fisica presso la Lancaster University.

Laureato in Fisica Medica all’Università di Roma “Tor Vergata”, la ricerca di Andrea si concentra sui sistemi dinamici non lineari, sull’analisi statistica e sui processi nelle reti biologiche.

Il suo lavoro recente esplora le reti funzionali dirette nel cervello, con pubblicazioni su riviste prestigiose come Physical Review Letters e Philosophical Transactions of the Royal Society.

Profili

Scopus

Orcid

Google Scholar

Pubmed

Insegnamenti

Gomp

Docenze

A. A. 2024 - 2025

A. A. 2023 - 2024

A. A. 2022 - 2023

Ultime 5 pubblicazioni (Scopus)

Beyond multilayer perceptrons: Investigating complex topologies in neural networks; Neural Networks; March 2024; DOI: 10.1016/j.neunet.2023.12.012

4Ward: A relayering strategy for efficient training of arbitrarily complex directed acyclic graphs; Neurocomputing; 1 February 2024; DOI: 10.1016/j.neucom.2023.127058

Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow; Brain Stimulation; 1 November 2023; DOI: 10.1016/j.brs.2023.10.007

VAESim: A probabilistic approach for self-supervised prototype discovery; Image and Vision Computing; September 2023; DOI: 10.1016/j.imavis.2023.104746

Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer; IEEE Transactions on Radiation and Plasma Medical Sciences; 1 July 2023; DOI: 10.1109/TRPMS.2023.3268361

Ultime 5 pubblicazioni (PubMed)

Beyond multilayer perceptrons: Investigating complex topologies in neural networks
This study delves into the crucial aspect of network topology in artificial neural networks (NNs) and its impact on model performance. Addressing the need to comprehend how network structures influence learning capabilities, the research contrasts traditional multilayer perceptrons (MLPs) with models built on various complex topologies using novel network generation techniques. Drawing insights from synthetic datasets, the study reveals the remarkable accuracy of complex NNs, particularly in...
Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow
CONCLUSION: Our causal approach allowed us to noninvasively evaluate directional interactions between fMRI BOLD signals from brainstem nuclei and cardiovagal outflow.
Heritability of human "directed" functional connectome
CONCLUSION: We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.
Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study
CONCLUSION: ML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.
Physically constrained neural networks for inferring physiological system models
Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. Nevertheless, such models are often based on complex combinations of multiscale (and possibly multiphysics) strategies that require ad hoc computational strategies and pose extremely high computational demands. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal...

Andrea Duggento

Biografia

Profili

Scopus

Orcid

Google Scholar

Pubmed

Insegnamenti

Gomp

Docenze

Ultime 5 pubblicazioni (Scopus)

inizio

inizio

inizio

inizio

inizio

Ultime 5 pubblicazioni (PubMed)

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