• Stefano Bargione


    PhD Student in the National PhD in AI (Health and Life Sciences track)
    Stefano received his BSc in Psychological Sciences and Techniques (L-24) from the
    LUMSA University (Italy), with a thesis focused on the Human-Robot Interaction,
    underlying the trade-off ratio in the build-up and application of human-centered
    technologies capable of providing intelligent solutions to transversal problems in daily
    life activities.

    Subsequently, he concluded his M.Sc. in Applied Experimental Psychological Sciences
    (LM-51) at the University of Milano-Bicocca with a thesis in cognitive neuroscience, by
    exploiting a non-invasive brain stimulation technique (e.g., TMS) to study the motor
    resonance mechanism of the human mirror neuron system.

    During his academic studies, he carried out research internships (e,g. IIT at the Robotics
    and Brain Cognitive Sciences unit of the Spatial Awaress and Multisensory Perception
    lab; Applied Intelligent Systems Laboratory at the Computer Science Department of the
    University of Milan) enabling him to discover “integrated” methodological approaches
    to the study of the human psyche, by making use of computer algorithms to reproduce
    the cognitive-motor processes involved in action representation.

    Lastly, he applied for the AS-AI school at the Institute of Sciences and Techniques of
    Cognition at the National Research Council (ISTC-CNR), with the aim of mastering the
    principles and the basic elements of artificial learning techniques for the design of
    computational system-level models of the brain and behavior at different levels of

    His research interests relate to the use of computational modelling approaches (e.g.,
    AI) and cutting-edge hardware and software technologies (e.g., VR, AR, XR) for the
    design of customized computer-simulated scenarios based on the subject-specific
    responses to multisensory experiences through online representation of multimodal
    individual data (e.g., psychological, cognitive, neurophysiological, and behavioural)

  • Tommaso Boccato


    Tommaso Boccato joined the “Tor Vergata” Medical Physics Section as a student enrolled in the Italian National PhD Program in Artificial Intelligence.

    He has a bachelor’s degree in Information Engineering and a master’s degree in ICT (summa cum laude), both from the University of Padova.Tommaso’s interests span the realm of neural networks: Deep Learning, Network Science and Computer Vision.He worked as a research assistant at the Vision for Robotics (Vienna University of Technology) and Computational Cognitive Neuroscience (University of Padova) interdisciplinary laboratories; such collaborations resulted in 2 publications and a best paper award.

    His current research activities focus on neuromorphic architectures and generative therapeutic “telepathy”.


    Ultime pubblicazioni (fonte PubMed)

  • Matteo Ferrante


    Matteo Ferrante is a PhD candidate at the “National AI PhD – Health and Life sciences” which joined the scientific group of University Tor Vergata on the theme “neuromorphic architectures and generative therapeutic “telepathy”.

    He studied at the University of Pavia and he has a bachelor’s deegre in Physics and a master’s deegre in biomedical Physics (summa cum laude).

    His MD’s thesis focus was on application of tractography and machine learning methods to explore the topography and the connectivity of deep structures in the brain, which resulted in a publication on Human Brain Mapping journal.

    He attended the first year of the specialization in medical physics school at the university of Milano, working at the European Institute of College, where he was involved in research projects focused on using artificial intelligence in classification and segmentation on different body districts.

    His interests are related the study and the application of artificial intelligence and computational methods the world of medicine, precision medicine and in particular for explore their applications in the neuroscience.

    His main PhD project is developing a way to decode the visual stimulus in the brain and generate brain’s activation maps from these inputs through the use of mappings between latent spaces of brain and artificial neural networks.

    Ultime pubblicazioni (PubMed)

  • Gianfrancesco Angelini


    Laurea in Ingegneria Chimica presso l’Università della Calabria (IT); Laurea Magistrale in Neuroingegneria e Bio-ICT, Bioingegneria, presso l’Università di Genova (It), con una tesi dal titolo “Modellazione computazionale del comportamento di una rete neuronale corticale in coltura da ratto EHMT1 KO”.

    Durante gli studi magistrali, ha svolto un tirocinio di ricerca all’Università di Nijmegen (NE), presso l’Istituto Donders, dove ha potuto apprendere tutte le fasi della catena di modellazione di reti neuronali modello di malattia, con reti spiking.

    Appassionato di intelligenza artificiale, ha conseguito diversi certificati di formazione in questo campo, tra cui il Deep Learning Nanodegree di Andrew NG e l’Advanced School in AI di AI2Life.

    Ha lavorato per anni come Data Scientist, Data Engineer, DevOps e Python Software Developer in diverse aziende.

    Nel 2022 è entrato a far parte del Programma Nazionale di Dottorato in AI, area Health and Life Sciences, 38° ciclo, con supervisore il Prof. Nicola Toschi, Università di Tor Vergata.

    Lavora sulle reti neurali di seconda e terza generazione, alla ricerca di punti in comune per implementare nuovi approcci di apprendimento automatico.

    Si interessa di agenti autonomi, agenti senzienti, estrazione di conoscenza e relazioni causali da big data.