Sara Cammarota

PhD Student

sara.cammarota@hotmail.it

+39 06 72596008

Biography

Sara is a student in the National Ph.D. Program in Artificial Intelligence at the Tor Vergata Medical Physics Section.
 
She obtained a bachelor degree in Physics from La Sapienza University in Rome, where she developed a thesis on the use of Silicon Photomultipliers in Positron Emission Tomography. She later earned her master’s degree in Data Science, focusing on Topological Deep Learning and Semantic Communications.
 
Her research interests have a particular focus on graph neural networks, topological deep learning, especially on how topology-informed neural architectures can process complex, structured biological data and deliver actionable insights.

Profiles

Last 5 articles (Scopus)

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Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging; Computer Methods and Programs in Biomedicine; November 2024; DOI: 10.1016/j.cmpb.2024.108375
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Cortical structure and subcortical volumes in conduct disorder: a coordinated analysis of 15 international cohorts from the ENIGMA-Antisocial Behavior Working Group; The Lancet Psychiatry; August 2024; DOI: 10.1016/S2215-0366(24)00187-1
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Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model; Journal of Neural Engineering; 1 August 2024; DOI: 10.1088/1741-2552/ad593c
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Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models; Computers in Biology and Medicine; August 2024; DOI: 10.1016/j.compbiomed.2024.108701
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Unraveling sex differences in Parkinson's disease through explainable machine learning; Journal of the Neurological Sciences; 15 July 2024; DOI: 10.1016/j.jns.2024.123091
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Last 5 articles (PubMed)

Created by An:Ca © 2023 Tor Vergata University P.I. 02133971008 – C.F. 80213750583