Prof. Luca Faes | Computational neuroscience | Best Researcher Award
Professor, University of Palermo, Italy
Prof. Luca Faes is a Full Professor at the Department of Engineering, University of Palermo, Italy. He earned his MSc in Electronic Engineering (1998) from the University of Padova and a PhD in Electronic Devices (2003) from the University of Trento. His research focuses on biomedical signal processing, information dynamics, and physiological networks. He has held positions at institutions such as the Bruno Kessler Foundation, University of Trento, and has been a visiting researcher in the USA, Belgium, and Brazil. A recipient of multiple awards, including the IEEE Senior Member recognition, Prof. Faes has been named among the World’s Top 2% Scientists by Stanford University. He actively contributes to academic programs, Erasmus+ initiatives, and editorial boards in biomedical engineering.
Publication Profile
Education
Prof. Luca Faes earned his Master’s degree (Italian Laurea) in Electronic Engineering cum laude from the University of Padova, Italy, in 1998 ⚙️. He then pursued a PhD in Electronic Devices at the University of Trento, Italy, in 2003 🏅. His academic journey laid the foundation for his expertise in biomedical engineering, signal processing, and complex system analysis 📡. With a strong background in electronic systems and bioengineering applications, he has made significant contributions to research and academia, shaping the field of biomedical signal processing and physiological network dynamics 🔬📊.
Professional Experience
Prof. Luca Faes has had a distinguished career in biomedical engineering and bioinformatics. He began as a Research Fellow (1999-2000) at ITC-irst, Trento, followed by a PhD at the University of Trento (2000-2003) 🎓. He held postdoctoral positions (2003-2013) at Trento’s Biophysics and BIOtech labs 🧬. Later, he became a Researcher (2014-2017) at FBK and Professor at the University of Palermo (2018-present) 🏅. His global research collaborations span USA, Belgium, and Brazil 🌍. Recognized as an IEEE Senior Member (2019) and among the World’s Top 2% Scientists (2021-2022), he is an esteemed expert in bioengineering and complex systems ⚙️📊.
Academic Contributions
Prof. Luca Faes has played a pivotal role in academic development and international collaborations. He has been a Doctorate Committee Member at CiMeC, University of Trento (2016-2017) and later at Palermo’s ICT Department (2018-present) 📚. He co-organized the Biomedical Engineering Master’s Program and serves as Vice Coordinator & Tutor for the Bachelor’s Program 🎓. As an Erasmus+ Coordinator, he has fostered partnerships with universities across Europe 🌍. He also leads the Biomedical Information Technologies Curriculum (2021-present) and organizes seminars with global scholars, strengthening biomedical and cybernetics engineering education 📡📊.
Teaching Contributions
Prof. Luca Faes has been actively involved in teaching since 1999, starting as a Teaching Assistant in General Physics II and Signal & Image Processing at the University of Trento 🎓. He has delivered specialized courses internationally, including Brazil 🇧🇷 (2015), IEEE MOOC (2016), and Portugal 🇵🇹 (2017). Since 2018, he has been a Tenured Professor at the University of Palermo, teaching Sensors, Biomedical Devices, and Statistical Analysis of Biomedical Signals 📊. His expertise in biomedical signal processing and sensor technology continues to shape future engineers in cybernetics and biomedical fields 🤖🔬.
🧠 Research Focus
Prof. Luca Faes specializes in computational neuroscience, biomedical engineering, and physiological signal processing. His research focuses on brain-heart interactions ❤️🧠, nonlinear dynamics 🔄, entropy measures 📊, and causal inference in physiological systems. He applies Granger causality and transfer entropy to analyze cardiovascular variability, cerebrovascular function, and neural synchronization. His work contributes to wearable health technology ⌚, autonomic regulation studies, and physiological stress assessment ⚡. He collaborates on machine learning 🤖 and high-order statistical modeling for biomedical applications. His interdisciplinary approach bridges neuroscience, physics, and AI-driven healthcare solutions. 🌍✨
Publication Top Notes
📄 Measures and Models of Brain-Heart Interactions – IEEE Reviews in Biomedical Engineering, 2025 🧠❤️
📄 Chaotic Dynamics and Synchronization under Tripartite Couplings – Chaos, Solitons and Fractals, 2024 🔄⚡
📄 Comparison of Feature Selection Methods for Physiological Stress Classification – Physiological Measurement, 2024 📊📈
📄 Disentangling High-Order Effects in Transfer Entropy – Physical Review Research, 2024 | 3 citations 🔗📡
📄 Assessing Granger Causality in Cerebrovascular Variability – IEEE Transactions on Biomedical Engineering, 2024 | 2 citations 🏥🧠
📄 Entropy Rate Measures for Time Series Complexity – Biocybernetics and Biomedical Engineering, 2024 | 4 citations 🔢📉
📄 Wearable Ring-Shaped Biomedical Device for Physiological Monitoring – Biosensors, 2024 | 3 citations ⌚📡
📄 Describing Cerebral Autoregulation via State Space Methods – Conference Paper, 2024 🏥📈
📄 Decomposing Transfer Entropy in Cardiovascular Interactions – Conference Paper, 2024 ❤️📊
📄 Gender Differences in Cardiovascular Variability Entropy – Conference Paper, 2024 🚻🫀