Nicola Toschi

Professore Ordinario, Responsabile di Sezione

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

toschi@med.uniroma2.it

+39 06 72596008

Biografia

Nicola Toschi è Professore Ordinario di Fisica Medica presso l’Università di Roma “Tor Vergata” e membro del personale di ricerca e della facoltà presso il Centro Athinoula A. Martinos per l’Imaging Biomedico (Harvard Medical School).

In precedenza ha lavorato come consulente strategico presso McKinsey & Company, come coordinatore per la Convenzione delle Nazioni Unite sui cambiamenti climatici, con la RAI e come coordinatore di progetti con AMREF. La sua ricerca è interdisciplinare, con particolare attenzione alle soluzioni scientifiche e tecnologiche per l’impiego di tecniche fisiche e matematiche avanzate al fine di estrarre informazioni quantitative di valore investigativo, diagnostico e prognostico in un contesto clinico.

È membro senior della società IEEE, membro attivo dell’ISMRM e dell’OHBM, membro fondatore dell’Alzheimer Precision Medicine Initiative (AMPI) e membro del Technical Committee on Cardiopulmonary Systems.

Titoli

  • B.Sc. Physics (Imperial College, London)
  • M.Sc. Applied Mathematics (ST. Catherine’s College, Oxford, UK),
  • MSc. Physics (University of Rome Tor Vergata)
  • PhD Natural Sciences (Ludwig Maximilian University of Munich, max Planck Institute of Psychiatry)
  • Specialization School in Medical Physics (University of Rome Tor Vergata).

Profili

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Ultime 5 pubblicazioni (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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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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Ultime 5 pubblicazioni (PubMed)

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