Andrea Gargano
Andrea Gargano, born in Salerno, Italy, earned his B.Sc. degree in Biomedical Engineering from the University of Pisa in 2018 and his M.Sc. degree (cum laude) in Bionics Engineering from the University of Pisa and the School of Advanced Studies Sant’Anna in 2021. Since November 2021, he has been pursuing a Ph.D. in Smart Industry under the supervision of Prof. Enzo Pasquale Scilingo and Prof. Mimma Nardelli. From December 2023, he has been visiting Prof. Michael Muma at Technische Universität Darmstadt, in Germany, for seven months. Before embarking on his Ph.D. journey, he gained research experience at ADATEC s.r.l. and at the Dipartimento di Ingegneria dell’Informazione of the University of Pisa, where he worked as a part-time industry-research trainee for six months, validating the feasibility of custom piezoresistive sensors for gait detection. Additionally, he received a four-month scholarship at the Research Center “E. Piaggio” of the University of Pisa, working on the development and implementation of a personality model for social robots. His primary research interests encompass affective computing, physiological time series analysis, statistical signal processing, machine learning, and wearable and unobtrusive sensing. He actively contributes to various professional initiatives, volunteering as a representative of the Ph.D. candidates in Smart Industry and as a member of the Presidio della Qualità di Ateneo. He is also appointed as vice-coordinator of the local branch of ADI in Pisa.
Affective computing; physiological signal processing; statistical signal processing; machine learning; wearable sensors; unobtrusive sensors; biomedical engineering; statistics
My research focuses on developing novel, advanced, and robust statistically informed signal processing techniques to investigate if and how different human emotional states can be detected by analysing physiological signals. The main challenge lies in working with data solely from peripheral physiological sources, such as cardiac, respiratory, and electrodermal activity. The objective is to develop innovative methodological tools tailored towards understanding if and how physiological data can indicate arising emotions compared to a basal/resting state, with the potential to differentiate among diverse emotional states. Potential applications include developing objective markers for mental state assessment and enhancing communication in human-computer or human-robot interaction environments.
• Collaborating with IMT Lucca to develop a personality model for enhanced human-robot interaction and working on physiological data analysis. • Collaborating with the Università della Calabria to analyse physiological data collected from remote sensors. • Participating in the EU Project POTION, during a six-month internship at the spin-off FeelING. • Collaborating with TU Darmstadt on the development of robust signal processing methods for physiological signals. • Working as Teaching assistant for the course "Bionics Senses" course (M.Sc. Bionics Engineering) since 2022 and the "Sensi Naturali e Artificiali" course (B.Sc. Biomedical Engineering) since 2023.