Nicola Toschi

Full Professor and Principal Investigator

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

toschi@med.uniroma2.it

+39 06 72596008

Biography

Nicola Toschi is a Full Professor in Medical Physics at the University of Rome “Tor Vergata” and Research Staff and Faculty at the Athinoula A. Martinos Center for Biomedical Imaging (Harvard Medical School).

He has previously worked as a strategy consultant at McKinsey & Company, as a facilitator for the United Nations convention on Climate Change, with the Italian National Television (RAI) and as a project coordinator with AMREF.

His research is interdisciplinary, with a focus on scientific and technological solutions for the deployment of advanced physical and mathematical techniques in order to extract quantitative information of investigative, diagnostic and prognostic value in a clinical context.

He is a senior member of the IEEE society, an active member of ISMRM and OHBM, a founding member of the Alzheimer’s Precision Medicine Initiative (AMPI) a member of the Technical Committee on Cardiopulmonary Systems.

Academic Qualifications

  • 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).

Profiles

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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)

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