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Gianluca CiattagliaView profile

Assistant Professor

• Radar Signal Processing
• Automotive Engineering
• Aerospace Engineering
• +3 more

Gianluca Ciattaglia is an Assistant Professor (RTDa) in Electrical and Electronics Measurements at the Department of Electrical and Electronic Engineering, Università Politecnica delle Marche, Italy. Previously, he served as a Research Fellow at the same institution from 2022 to 2024 and worked at Ferrari S.p.A. as an Electronic Support Engineer for Formula 1 test teams (2018–2022). He holds a B.Sc. (2014), M.Sc. (2017), and Ph.D. (2021) in Electronic Engineering and Information Engineering from Università Politecnica delle Marche. His research focuses on radar-based measurement techniques for industrial, automotive, and aerospace applications, including radar signal processing, vibration analysis, and sensor fusion. He has contributed to projects involving drone detection, elderly telemonitoring, and automotive radar optimization. His work bridges theoretical advancements in signal processing with practical implementations in safety, health, and environmental monitoring. Publications span radar-based physiological sensing, autonomous drone identification, and mmWave radar applications in structural health monitoring. His recent work emphasizes machine learning integration with radar data for improved accuracy in activity recognition and environmental control systems. No scientific awards are explicitly listed, but his contributions to automotive and aerospace radar technologies reflect significant industry-academia collaboration. Professional activities include advising on radar sensor deployments and collaborating with automotive firms like Ferrari. His lab focuses on developing edge-node systems for real-time monitoring and smart environmental solutions. Current projects include optimizing radar configurations for energy efficiency and enhancing indoor air quality through smart ventilation systems.

• Power Electronics
• Electrical Machines and Drives
• Battery Electric Vehicles
• +4 more

Fausto Stella is a Fixed-term Tenured Assistant Professor at the Department of Energy (DENERG) of Politecnico di Torino. His research focuses on power electronics, electrical machines, and drives for applications in eMobility, renewable energy systems, and high-performance industrial drives. He leads the PEEMD research group and collaborates with industry on projects involving GaN/SiC semiconductor integration, traction inverters, and DC-link capacitor optimization. He teaches courses on power electronics, electrical drives for eMobility, and embedded systems at the BSc/MSc levels, including laboratory modules on power converters and drives. His work emphasizes practical implementations through high-fidelity hardware-in-loop (HIL) testing and 3D printed thermal solutions. Stella supervises four PhD students researching topics like WBG-based motor drives, multiphase machines, and fault-tolerant power electronics. He has co-authored over 50 publications and holds patents in junction temperature estimation, capacitor aging evaluation, and 3D-printed thermal management systems. His industry collaborations include projects with HBK Genesis and automotive companies, focusing on motor test rig design, SiC inverter validation, and advanced control strategies for electric vehicles. He actively participates in international conferences like SPEEDAM and ICEM, contributing to advancements in power electronics efficiency and reliability.

• Electrical and Electronic Measurement
• Compressive Sampling
• Data Acquisition Systems
• +5 more

Ioan Tudosa is a Research Fellow at the Department of Engineering, University of Sannio, specializing in electrical/electronic measurements and signal processing. His research focuses on developing advanced measurement systems using compressive sampling techniques, with applications spanning biomedical instrumentation, IoT security, and precision metrology. Research Focus Tudosa's primary research investigates: Novel compressive sampling architectures for data acquisition systems High-accuracy phase measurement instruments (collaborating with NIST on a $1M project) IoT security solutions through hardware measurement techniques Biomedical monitoring systems for ECG and pulse wave analysis Machine learning applications in measurement science His work bridges theoretical signal processing with practical instrumentation design for industrial and medical applications. Publication Trends Tudosa's recent publications demonstrate three key trends: Advancements in compressive sensing for spectrum monitoring and biomedical signal acquisition Integration of machine learning with measurement systems for anomaly detection Development of IoT-enabled monitoring platforms for structural health and industrial safety His research shows consistent focus on improving measurement accuracy while reducing system complexity and power consumption. Research Infrastructure Tudosa collaborates with the National Institute of Standards and Technology (NIST) and leads projects developing specialized testbeds for: High-frequency phase measurement Wireless spectrum monitoring Structural health monitoring using IoT networks Wearable biomedical sensors

• Fusion Energy
• Plasma Physics
• Energy Harvesting
• +4 more

Vincenzo Paolo Loschiavo is a Researcher in the Department of Engineering at the University of Sannio, Italy, specializing in Electrical Engineering (ING-IND/31). His academic career focuses on two primary research domains: fusion energy systems and magnetostrictive energy harvesting technologies. He maintains active collaborations with major European fusion research facilities including JET, ASDEX Upgrade, and the Divertor Tokamak Test facility (DTT). Dr. Loschiavo's research interests include: Fusion Energy and Plasma Physics Energy Harvesting Systems Magnetostrictive Materials and Applications Tokamak Engineering and Divertor Technology AC-DC Power Conversion for Energy Harvesting Plasma Control Systems His recent publications demonstrate significant contributions to both theoretical modeling and practical implementation in these fields. In fusion research, he addresses critical challenges in power exhaust management and plasma control for next-generation fusion devices. His energy harvesting work focuses on developing efficient conversion systems using magnetostrictive materials, with applications in structural health monitoring and wireless sensor networks. His research bridges fundamental physics with practical engineering solutions, particularly in designing specialized AC-DC boost converters for non-conventional energy sources. Dr. Loschiavo frequently collaborates with international research teams across European laboratories, contributing to major projects including the DEMO power plant design studies and the SHiP (Search for Hidden Particles) experiment at CERN. His work appears in leading journals such as Nuclear Fusion, Fusion Engineering and Design, and Journal of Instrumentation.

Valentina BianchiView profile

Associate Professor

• Electronics
• Embedded Systems
• Wireless Sensor Networks
• +7 more

Valentina Bianchi is an Associate Professor in the Department of Engineering and Architecture at the University of Parma, Italy. Her academic work spans electronics, embedded systems, and sensor technologies, with teaching responsibilities in Electronic Engineering and interdisciplinary programs involving preventive physical activities and intelligent vehicles. Her research focuses on advanced sensor systems, including wireless biosensors, structural health monitoring, e-nose/e-tongue platforms, and battery aging modeling. She applies machine learning techniques such as LSTM and Gaussian Process Regression to problems in energy systems and signal processing, particularly in the context of IoT and precision agriculture. The recent publications indicate a strong trend toward low-power, wireless, and real-time monitoring solutions for both industrial and biological systems. Her work bridges electronics engineering with applications in health, agriculture, and automotive domains. She has been actively teaching core electronics courses such as MATLAB for Electronics, Electronics for Automotive Systems, and Instruments for Movement Analysis across multiple academic years, demonstrating sustained involvement in graduate-level education. She serves as a teacher tutor in the Electronic Engineering program and collaborates extensively with researchers like Ilaria De Munari, Michele Caselli, and Andrea Boni.

• Vehicle Dynamics
• Control Systems
• Tribology
• +4 more

Francesco Bottiglione serves as Full Professor in the Department of Mechanics, Mathematics and Management at Politecnico di Bari, where he directs the Aerospace Systems Engineering degree program. His academic specialization in Applied Mechanics (ING-IND/13) focuses on mechanical systems dynamics with significant contributions to automotive engineering and surface science. Professor Bottiglione's research integrates theoretical and experimental approaches across five core domains: vehicle dynamics (developing nonlinear model predictive control for multi-actuated vehicles and trailer sway mitigation), surface mechanics (characterizing topography and friction through multi-laboratory benchmarking), biomechanics (applying machine learning to fencing performance analysis), additive manufacturing (fused filament fabrication for sensor integration), and seismic engineering (nonlinear viscoelastic damping systems). His work consistently bridges fundamental mechanics with practical applications in transportation, sports science, and structural safety. Analysis of his 2021-2025 publications reveals three dominant trends: (1) Advancement of model predictive control architectures for complex vehicle systems, particularly in articulated configurations; (2) Standardization efforts in surface topography characterization through multi-laboratory collaboration; and (3) Emerging integration of explainable machine learning in biomechanical analysis for aging populations and athletic performance. His research methodology emphasizes experimental validation of theoretical models, with increasing interdisciplinary collaboration evident in recent works.

• Computer architectures
• Computer aided design
• Embedded electronics testing
• +3 more

Tommaso Foscale is a Lecturer at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He is also a third-year PhD student in Computer and Control Engineering, affiliated with the Electronic CAD & Reliability group. PhD in Computer and Control Engineering (2022–2025) MSc in Computer Engineering, cum laude (2021) His research focuses on testing techniques for automotive systems on chip , with specific expertise in: Wafer-level and manufacturing testing FPGA-based test equipment design Single-event upset (SEU) injection Cost-effective characterization of SoCs Recent publications highlight trends in multi-site testing optimization and reliability engineering for automotive electronics. He has contributed to IEEE symposia on test strategies and functional safety. As a teaching assistant, he collaborates on Operating Systems courses for Computer Engineering and Computer Science Engineering programs (2023–2025). His research group actively explores fault tolerance and embedded systems testing.

Stefano ScanzioView profile

Researcher

• Industrial Communication Systems
• Wireless Networks
• Real-Time Networks
• +7 more

Stefano Scanzio is a Senior Researcher at the National Research Council of Italy (CNR-IEIIT) and teaches computer science courses at Politecnico di Torino . With over 60 publications in wireless networks, real-time communication, and industrial IoT, he serves as Associate Editor for Ad Hoc Networks , IEEE Access , and Electronics journals. Ph.D. in Computer Science (Politecnico di Torino, 2008) Laurea in Computer Science (Politecnico di Torino, 2004) His research focuses on industrial wireless networks , particularly Wi-Fi and IEEE 802.15.4 TSCH, with emphasis on: Energy-saving mechanisms in wireless sensor networks Predictive models for Wi-Fi channel quality Reliable communication through seamless redundancy Machine learning integration for network optimization Clock synchronization under non-Gaussian noise Recent publications analyze Wi-Fi 7's multi-link operation, executable QR codes for IoT, and AI-driven network self-configuration. His work has been recognized with multiple best paper awards .

• Analog, Power and Mixed-Signal Circuits
• Embedded Systems
• Big Data
• +7 more

Luca Lombardo is a Fixed-term Researcher at Politecnico di Torino , affiliated with the Department of Electronics and Telecommunications (DET) and College of Electrical and Energy Engineering . He holds the academic role of Researcher in the IMIS-01/B - Electrical and Electronic Measurements sector.

Alessio OrsinoView profile

Research Fellow

• Big Data Analysis
• Distributed Computing
• Edge-Cloud Computing
• +5 more

Alessio Orsino is a Research Fellow at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES) at the University of Calabria. His academic journey includes a Ph.D. in Information and Communication Technologies (ICT) awarded in January 2025, a Master’s in Computer Engineering (Big Data Analytics and Data Science) from 2020, and a Bachelor’s in Computer Engineering from 2018, all from the University of Calabria. He also served as a visiting researcher at the University of Cambridge’s Department of Computer Science and Technology (July–December 2023). His research focuses on big data analysis, distributed computing, machine learning, and edge-cloud integration. He contributes to projects like ASPIDE (Exascale programming models) and INSIDER (cloud-edge IoT deployment optimization), funded by EU Horizon 2020 and Italy’s MIUR. He is a contract professor at DIMES and a member of the Scalable Computing and Cloud Laboratory (SCALab). Orsino’s work bridges theoretical and applied computing, emphasizing efficient machine learning on resource-constrained systems and real-time adaptive models for edge environments. His publications address edge-cloud continuum challenges, adversarial robustness in AI, and scalable big data frameworks. Key grants include Horizon 2020 (ASPIDE) and PRIN 2022 (INSIDER), supporting his interdisciplinary research in hybrid cloud-edge infrastructures and data-intensive applications. He has authored/co-authored a book on big data programming and collaborates internationally through visiting positions and lab engagements.

Gabriel E. HineView profile

Researcher

• Biometrics
• Signal Processing
• Information Security
• +13 more

Gabriel E. Hine is a Postdoc researcher at the Biometric Systems and Multimedia Forensics Lab within the Department of Engineering at Roma Tre University, Italy. He is actively engaged in research at the intersection of signal processing, biometrics, and information security, and contributes to teaching in advanced signal processing and biometric systems. Bachelor’s Degree in Electronic Engineering, Roma Tre University (2013, cum laude) Master’s Degree in Information and Communication Technology Engineering, Roma Tre University (2015, cum laude) PhD in Applied Electronics, Roma Tre University (2018) Research collaboration at Telefonica I+D, Barcelona (2016) Research involvement in EU H2020 ENCASE and PRIN 2011/2012 projects His research focuses on biometric cryptosystems, template protection, and privacy-preserving biometric recognition, with significant contributions to fingerprint, palmprint, EEG, and signature-based systems. He has also conducted influential work on online social media dynamics, particularly analyzing 4chan’s /pol/ forum. The analysis of his recent publications reveals a strong trend in developing secure and unlinkable biometric templates using deep learning, sparse signal representations, and zero-leakage frameworks. His work spans both theoretical formulation and experimental validation on real-world data, particularly in finger-vein and palm-vein recognition. He also explores novel biometric modalities such as in-air 3D signatures and resting-state EEG. Best Paper Runner-Up, ICWSM-17 (2017) Selected as 'Best of Arxiv' paper Widely covered in Nature , MIT Technology Review , The Independent , Vice , and Italian media Hine has served as a lecturer and teaching assistant for courses including 'Biometric Systems' and 'Signal Theory' at Roma Tre University. He has led a didactic laboratory on biometric systems, guiding students in implementing and analyzing biometric recognition solutions. His research is supported by major grants such as EU H2020 ENCASE and PRIN 2011/2012. He collaborates extensively with Prof. Patrizio Campisi and other researchers in the BioMedia4n6 lab. He is a core member of the BioMedia4n6 research group, which specializes in biometrics, data hiding, and blind deconvolution, applying advanced signal processing techniques to multimedia forensics and security challenges.

Edgar Ernesto Sanchez SanchezView profile

Associate Professor

• Artificial Intelligence
• Automotive Systems
• Reliability Engineering
• +3 more

Dr. Edgar Ernesto Sanchez Sanchez is an Associate Professor at the Department of Control and Computer Science (DAUIN) , Polytechnic University of Turin. He is a member of the Power Electronics Innovation Center and leads the CAD - Electronic CAD & Reliability Group . His research focuses on Artificial Intelligence and Automotive Systems reliability, particularly addressing neural network vulnerability, approximate computing reliability, and hardware Trojan mitigation. Key projects include REACT (Self-Aware Neuromorphic Architectures) and RESHIP4EU (Chip Design Training). Recent publications demonstrate expertise in deep learning reliability , RISC-V security , and hardware-software co-design . He has received Best Paper Awards at DATE 2006 and DDECS 2013. Dr. Sanchez supervises PhD students in AI reliability and cybersecurity, while teaching High Performance Computing and Computer Architectures courses. His work bridges academic research and industrial applications through collaborations with STMicroelectronics and automotive companies.

• Computer architectures
• Computer aided design
• Automotive Systems Engineering
• +6 more

Nima Kolahimahmoudi is a Ph.D. student in Computer and Systems Engineering at the Polytechnic of Turin, currently in his 38th cycle (2022-2025). He is affiliated with the Department of Control and Computer Engineering (DAUIN) and serves as an external lecturer and teaching assistant. His research is supervised by Professor Paolo Bernardi and Richard Cantoro. Education: Master’s in Electronic Engineering (2022) from Polytechnic of Turin Research Group: CAD - Electronic CAD & Reliability Group (DAUIN) His research focuses on functional safety techniques for automotive-oriented Systems-on-Chip (SoCs), particularly targeting analog and mixed-signal circuits, embedded memories, and reliability testing. He has contributed to advancements in low-area ADC design, irradiation-based memory parameter extraction, and automated self-test methodologies for fault coverage improvement. Recent publications highlight trends in automotive electronics reliability, including analog/mixed-signal circuit design, SoC measurement systems, irradiation testing, and fault coverage optimization. These works span conferences like IEEE DTTIS, VLSI-SoC, and IEEE European Test Symposium. Nima collaborates in teaching the 2024/25 1st-level degree course in Computer Sciences at Polytechnic of Turin, specifically for Automotive Engineering. His academic work emphasizes test automation, memory reliability, and functional safety frameworks for automotive systems.

• Magnetic Positioning Systems
• Battery Technology
• Electronic Measurements
• +5 more

Francesco Santoni is a Postdoctoral Researcher at the Department of Engineering, University of Perugia. He holds a Master's degree in Physics (2010) from University of Perugia and a Ph.D. in Microelectronics and Telecommunications Engineering (2015) from University of Roma 'Tor Vergata'. From 2015-2017, he conducted postdoctoral research on charge transport modeling in organic semiconductors at his Ph.D. institution. Research Highlights: Magnetic positioning systems for biomedical applications Electrochemical impedance spectroscopy in battery technology Machine learning for state-of-charge estimation Mathematical modeling of organic electronic devices Uncertainty quantification in measurement systems Wireless sensing for wearable biometric systems Project Involvement: Dr. Santoni contributes to the 6 DOF Finger Tracking project, an inter-academic collaboration funded by Italy's PRIN 2015 grant. This initiative involves research groups from Perugia, Brescia, and Cassino, focusing on developing short-range wireless positioning technology for biomedical applications like data gloves. His work spans theoretical modeling, practical implementation, and interdisciplinary applications of electronic measurement systems.

Francesco Maria PuglisiView profile

Associate Professor

• Resistive Memories
• Neuromorphic Computing
• Device Reliability
• +7 more

Francesco Maria Puglisi serves as Associate Professor of Electronics at the University of Modena and Reggio Emilia since 2018, leading research in semiconductor device physics with emphasis on emerging memory technologies and neuromorphic systems. His expertise spans characterization, physics-based modeling, and reliability analysis of next-generation electronic devices. His research portfolio prominently features: Resistive Random-Access Memory (RRAM) for non-volatile storage and neuromorphic computing Random Telegraph Noise (RTN) mechanisms in FinFETs and memristors Compact modeling frameworks for device simulation Reliability assessment of 2D materials and III-V semiconductor devices Memory-in-logic architectures enabling non-Von Neumann computation Hardware security applications leveraging device physics Analysis of his 2023-2025 publications reveals a pronounced shift toward in-memory computing solutions, particularly SIMPLY architecture implementations using RRAM/STT-MRAM, alongside deep investigations into ferroelectric device dynamics and RTN-based cryptographic primitives. His work consistently bridges fundamental device physics with practical circuit design for energy-efficient computing. Notable scientific recognitions include: IEEE ICICDT 2013 Best Paper Award for Resistive Memories modeling IEEE ESSDERC 2016 Best Paper Award for RTN in FinFETs IEEE ESSDERC 2019 Best Paper Award for advanced memory characterization Dr. Puglisi has additionally received multiple national awards for academic excellence and research impact. While his advisory activities and grant portfolio aren't detailed in available sources, his extensive publication record demonstrates significant contributions to semiconductor device engineering through both theoretical modeling and experimental validation.