Docente esterno e/o collaboratore didattico
Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS)
Dottorando in Bioingegneria E Scienze Medico-chirurgiche , 36o cycle (2020-2023)
Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS)
Profilo
Dottorato di ricerca
Argomento di ricerca
Predicting pathological risks and dynamics through explainable artificial intelligence techniques.
Tutori
Research presentation
Interessi di ricerca
Biografia
Michela Sperti (SCOPUS ID: 57217331713, h-index: 5) is a third-year Ph.D. student at Politecnico di Torino, Bioengineering Department, with Prof. M. A. Deriu. She graduated in Biomedical Engineering at Politecnico di Torino in 2019 with a thesis on machine learning techniques for cardiovascular risk prediction in rheumatic patients. She worked for one year as a Research Assistant under the European-funded MSCA VIRTUOUS project (which aims to apply machine learning techniques to investigate taste and food properties). Currently, she is studying explainability techniques for machine learning and deep learning models applied in clinical decision support systems with a special focus on cardiovascular risk prediction. The final aim of her research is the understanding of complex mechanisms that underlie physiological processes. She is very passionate about teaching and is committed to communicating her results. She is the author of nine articles published in peer-review journals, three conference papers and she took part in three international workshops as both a teaching assistant and a speaker. She is Contributing Author of the book "Applied Deep Learning with TensorFlow 2" by U. Michelucci.
Competenze
Settori ERC
SDG
Premi e riconoscimenti
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Best Review Article of the Year Award, Presented to the authors of the highest impact review article published in 2022 in Minerva Cardiology Angiology. (2023) - Description of role of Michela Sperti in the writing and preparation of “Applied Deep Learning with TensorFlow 2 – Second Edition” published by APRESS/Springer in 2022. (2022)
Didattica
Insegnamenti
Corso di laurea magistrale
- Biomechanical design. A.A. 2021/22, INGEGNERIA BIOMEDICA. Collaboratore del corso
- Biomechanical design. A.A. 2022/23, INGEGNERIA BIOMEDICA. Collaboratore del corso
- Rational Drug Design: Principles and Applications. A.A. 2021/22, INGEGNERIA BIOMEDICA. Collaboratore del corso
- Biomeccanica multiscala. A.A. 2021/22, INGEGNERIA BIOMEDICA. Collaboratore del corso
- Biomeccanica multiscala. A.A. 2022/23, INGEGNERIA BIOMEDICA. Collaboratore del corso
Ricerca
Gruppi di ricerca
Pubblicazioni
Pubblicazioni degli ultimi anni
Coautori PoliTO
Pubblicazioni per tipo
Pubblicazioni più recenti Vedi tutte le pubblicazioni su Porto@Iris
- Venturini, Francesca; Sperti, Michela; Michelucci, Umberto; Gucciardi, Arnaud; Martos, ... (2023)
Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. In: JOURNAL OF FOOD ENGINEERING, vol. 336. ISSN 0260-8774
Contributo su Rivista - Sperti, Michela; Malavolta, Marta; STAUNOVO POLACCO, Federica; Dellavalle, Annalisa; ... (2022)
Cardiovascular risk prediction: from classical statistical methods to machine learning approaches. In: MINERVA CARDIOLOGY AND ANGIOLOGY, vol. 70. ISSN 2724-5683
Contributo su Rivista - Venturini, F; Michelucci, U; Sperti, M; Gucciardi, A; Deriu, Ma (2022)
One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge: explainability techniques applied to olive oil fluorescence spectra. In: SPIE Photonics Europe. ISSN 0277-786X. ISBN: 9781510651548
Contributo in Atti di Convegno (Proceeding) - Sperti, M; Michelucci, U; Venturini, F; Gucciardi, A; Deriu, Ma (2022)
Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks. In: SPIE Photonics Europe. ISSN 0277-786X. ISBN: 9781510651548
Contributo in Atti di Convegno (Proceeding) - Gucciardi, A; Michelucci, U; Venturini, F; Sperti, M; Martos, Vm; Deriu, Ma (2022)
Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil. In: SPIE Photonics Europe. ISSN 0277-786X. ISBN: 9781510651548
Contributo in Atti di Convegno (Proceeding)