Personale

Docenti

Assegnisti

  • Patrícia Cardoso de Andrade

    email: patcapatricia@gmail.com

    Patrícia Cardoso de Andrade is a Ph.D. student in Electrical Engineering, with an emphasis on Biomedical Engineering, at the University of Campinas (UNICAMP). She currently works with transcranial focused ultrasound (tFUS) for neuromodulation.

    She has a master’s degree in Electrical and Computer Engineering at the Federal University of Goias (2017), where she studied an ultrasound thermometry system to monitor the internal temperature in heated materials She graduated in Physics, at the Federal University of Goias (2015).

    She has experience with Ultrasound, Temperature Monitoring Via Pulse-Echo Ultrasound, Substitutional Defects, Discrete Breathers, Interaction Between Intrinsic and Extrinsic Modes Located in an Atomic Chain, Mathematical Models, Skills and Abilities for Physics Teaching in High School, and Ultrasound Neuromodulation.

    She is the author of 4 articles and 10 conference proceedings. She presented her scientific work at 19 scientific conferences.

    She is currently an associate member of the Brazilian Society of Physics (SBF); Brazilian Society of Biomedical Engineering (SBEB); IEEE Women in Engineering (IEEE WIE); IEEE Young Profession; IEEE Ultrasonics; IEEE Ferroelectrics and Frequency Control Society (IEEE UFFC). She is a volunteer at the IEEE WIE.

    Profiles

  • Marianna Inglese

    email: marianna.inglese@uniroma2.it

    Assegnista di ricerca presso l’Università degli Studi di Roma, Tor Vergata

    Marianna ha conseguito la laurea magistrale in Ingegneria Biomedica nel 2014 presso l’Università di Roma “La Sapienza” (Italia) con una tesi di laurea sulla correzione dell’attenuazione causata dalle bobine mammarie a radiofrequenza per risonanza magnetica nelle immagini di tomografia a emissione di positroni acquisite su piattaforma ibrida di PET/RM. Il suo progetto è stato realizzato presso l’Università del Western Ontario (Canada) dove ha lavorato come visiting research student presso il Lawson Health Research Institute. Marianna ha conseguito il dottorato di ricerca in Bioingegneria presso l’Università di Roma “La Sapienza” nel 2019 con una tesi sui modelli avanzati di quantificazione della perfusione su dati dinamici di PET e risonanza magnetica.

    Attualmente ricopre una posizione di ricercatore onorario presso l’Imperial College di Londra (Regno Unito), dove ha lavorato come ricercatrice associata e assistente di ricerca prima e durante il suo dottorato di ricerca sulla quantificazione di dati dinamici PET e sull’applicazione di modelli di machine learning per studi di radiomica.

    È autrice di 16 articoli peer-reviewed e 8 atti di conferenze. Ha vinto diversi premi scientifici, tra cui la “Magna cum laude” per la sua presentazione orale alla International Society for Magnetic Resonance in Medicine (ISMRM), un secondo posto per la sua presentazione al Perfusion Workshop e al PET/MRI Workshop organizzato dall’ISMRM.

    Attualmente è membro dell’Associazione Italiana degli Ingegneri Clinici (AIIC), del National Group of Bioengineering (GNB), del British and Irish Chapter dell’ISMRM, dell’ISMRM, della British Neuro-Oncology Society (BNOS) e dell’Association of Scienziati Italiani nel Regno Unito (AISUK).

    Profili:

    Ultime 5 pubblicazioni (fonte PubMed)

Dottorandi

  • Stefano Bargione

    email: s.bargione@campus.unimib.it

    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
    abstraction.

    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

    email: tommaso.boccato@studenti.unipd.it

    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”.

    Profili

    Ultime pubblicazioni (fonte PubMed)

  • Matteo Ferrante

    email: matteo-ferrante@hotmail.it

    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

    email: gian.angelini@hotmail.com

    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 deeplearning.ai 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.

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