Find Academic Supervisors

• Chemical Sensors
• Measurement Systems
• Circuits and Systems Design
• +4 more

Valerio Vignoli is an Associate Professor of Electronics at the University of Siena's Department of Information Engineering and Mathematics since 2005. He holds a Laurea in Electronic Engineering (1989) and a Ph.D. in Nondestructive Testing (1994) from the University of Florence. His research focuses on chemical sensors, measurement systems, and circuits for ICT applications. He has pioneered low-cost IoT sensor solutions for environmental monitoring, healthcare, and industrial safety. Notable projects include magnetic contaminant detection systems, wearable health monitors for hazardous environments, and QCM-based biosensors for on-site diagnostics. His work integrates interdisciplinary approaches, such as applying machine learning to sensor signal processing and developing energy-efficient self-powered sensor nodes. He has contributed to advancing photoacoustic gas sensing, entropy estimation for secure random number generation, and wearables for occupational safety. His research emphasizes practical applications, including smart agriculture CO₂ monitoring and real-time worker health tracking. Dr. Vignoli has designed innovative systems like the remotely controlled test bench for ultrasonic anemometers and self-tunable chaotic TRNGs. His publications span sensor design, environmental technology, and hardware security, reflecting a commitment to bridging theoretical advancements with real-world implementation challenges.

• Sensor Technology
• Environmental Monitoring
• IoT Systems
• +3 more

Ada Fort is an Associate Professor in the Department of Information Engineering and Mathematics at the University of Siena, Italy. Her research focuses on advanced sensor systems, including environmental monitoring, biomedical instrumentation, and IoT applications. She holds a Laurea in Electronic Engineering (1989) and a Ph.D. in Nondestructive Testing (1992) from the University of Florence. Key research areas include wearable sensors for air quality monitoring, magnetic detection of contaminants, and QCM-based biosensors. Her work integrates machine learning for signal processing and fault detection in industrial and environmental systems. Notable projects involve self-sufficient IoT nodes powered by solar energy and low-cost sensor networks for agriculture and healthcare. Publications highlight innovations in sensor design, data imputation for environmental monitoring, and entropy-based security systems. Her contributions span interdisciplinary fields, linking engineering principles with applications in healthcare, agriculture, and climate science.

Marco GrangettoView profile

Professor

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Luca CrocettiView profile

Assistant Professor

• Cybersecurity in Automotive Systems
• Embedded Systems Design
• VLSI Systems for Space Applications
• +3 more

Luca Crocetti is an Assistant Professor at the Department of Information Engineering (DII) of the University of Pisa. His research focuses on hardware security, embedded systems, and VLSI design for applications in Automotive, Space, IoT, and High-Performance Computing. He specializes in cybersecurity modules for automotive systems, space-grade FPGAs, and digital signal processing in satellite communications. His teaching includes electronics laboratory practices and cybersecurity principles, with a recent course on hardware's role in security for the Ph.D. program in Information Engineering. He has authored over 20 publications and holds 3 patents (1 national, 2 international), with notable work on cryptographic cores, secure boot mechanisms, and automotive cybersecurity. His contributions span efficient hardware implementations of AES, SHA-3, and other cryptographic algorithms, emphasizing compliance with standards like CCSDS for space applications. His research trends highlight a focus on hardware-efficient architectures, modular design for scalability, and robust security protocols for trusted environments. Notable publications address cryptographic co-processors for the European Processor Initiative, PUF-based security for RISC-V, and FPGA-based RNGs. While no formal awards are listed, his active role in cutting-edge cybersecurity projects underscores his contributions to embedded and space system security. His teaching and research emphasize hands-on lab experiences and real-world applications of hardware security principles.

Federico ReghenzaniView profile

Assistant Professor

• Computer Science
• Embedded Systems
• Fault Tolerance
• +3 more

Federico Reghenzani is an Assistant Professor at Politecnico di Milano in the Department of Electronics, Information and Bioengineering. His research focuses on computer science, embedded systems, fault tolerance, high-performance computing, real-time systems, and compiler technology. He leads the HEAP Lab where his team investigates reliability engineering and hardware-software co-design for safety-critical applications. Reghenzani's research examines software-based approaches to hardware fault tolerance, compiler technologies for reliability enhancement, and resource management in high-performance computing environments. His work has significant applications in aerospace systems, real-time embedded platforms, and next-generation computing architectures. His publications demonstrate a consistent focus on improving system reliability through compiler techniques, fault injection methodologies, and hardware-software co-design. The research spans theoretical frameworks, practical implementations, and experimental validation across diverse computing environments.

Sandro BartoliniView profile

Associate Professor

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +5 more

Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.

Giovanni SquilleroView profile

Professor

• Artificial Intelligence
• Soft Computing
• Evolutionary Algorithms
• +7 more

Giovanni Squillero is a Full Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He leads the CAD group (Electronic CAD & Reliability) and serves on Politecnico's Joint Committee for Teaching and Ph.D. Steering Committee (Pure and Applied Mathematics).

• Structural Engineering
• Civil Engineering
• Bridge Engineering
• +12 more

Gabriele Bertagnoli is an Associate Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino, Italy. His research and teaching focus on civil and structural engineering, particularly in bridge design, composite and concrete structures, finite element analysis, and structural health monitoring. His research interests include: Structural Engineering Bridge and Composite Structures Finite Element and Nonlinear Analysis Structural Health Monitoring (SHM) Optimization of Reinforced and Prestressed Concrete Seismic Retrofitting and Safety Assessment Bertagnoli's recent publications reveal a strong trend in developing and applying advanced computational models for the safety assessment of existing bridges and buildings, especially under damage and seismic loading. His work integrates innovative sensor technologies for SHM, genetic algorithms for structural optimization, and nonlinear finite element simulations to evaluate structural performance and reliability. His scientific awards include: Best Ph.D. Thesis of 2006 in the field of concrete structures – fib (Fédération Internationale du béton) CTE Congress 2018 Memorial Award – CTE (Italy, 2020) He is actively involved in research supervision and grant leadership: Supervises PhD students: Mario Ferrara and Sofia Villar Principal Investigator on multiple national and international projects (e.g., PRIN, PNRR, DECORI, Ri-REVERSE) Leads commercial and applied research contracts in structural consultancy and monitoring His laboratory and research group are engaged in developing intelligent monitoring systems for urban infrastructure (e.g., INSIST, Smart Concrete) and innovative retrofitting solutions using steel exoskeletons, composite connections, and sensor-embedded materials.

Francesco PicarielloView profile

Researcher

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +7 more

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +6 more

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

Lorenzo VangelistaView profile

Associate Professor

• Internet of Things
• Low Power Wide Area Networks (LPWAN)
• LoRa
• +7 more

Lorenzo Vangelista serves as Associate Professor of Telecommunications at the Department of Information Engineering, University of Padua, Italy, a position held since 2006. He teaches core courses including Internet of Things and Smart Cities, Telecommunications, and Project Management. His academic leadership includes chairing the Information Engineering Degree curriculum from 2010 to 2014. His research expertise spans Internet of Things , Smart Cities , 5G systems (with focus on machine-to-machine traffic), DVB systems , and OFDM-based communications . He is a pioneer in LoRa and LPWAN technologies, contributing to standardization bodies such as IEEE 802.11, 3GPP, ETSI, and the LoRa Alliance. His work integrates statistical signal processing , embedded systems design , and protocol development for next-generation wireless networks. With over 80 publications, his recent work explores LoRaWAN enhancements for satellite IoT, vehicular communications, and interstellar optical systems. Key innovations include golden modulation for massive IoT and machine learning applications for network optimization. His research bridges theoretical advances with real-world deployment, as seen in field tests for vehicle-to-roadside communication and underwater acoustic modems. Professor Vangelista has supervised nine PhD students (including a dual PhD with Supèlec, Paris) and numerous MSc theses. He led European projects PACIF and SWAP (a Marie Curie best practice) and regional initiatives. His entrepreneurial endeavors include founding Patavina Technologies (acquired by A2ASmartCity in 2017) and Wireless and More, developing IoT solutions like the LoRa Suite. He maintains strong industry ties with Nokia Bell Labs, RAI, and Wisycom, and collaborates with international research centers including CTTC. His standardization contributions notably include co-authoring foundational DVB-T2 papers and serving as ETSI Rapporteur for LPWAN-CSS.

• Computational Complexity
• Logic in Computer Science
• Proof Complexity
• +4 more

Nicola Galesi serves as an Associate Professor in the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome since 2022, following 17 years in Sapienza's Department of Computer Science (2005-2022). His academic journey began with an Associate Professorship at Universitat Politecnica de Catalunya (2001-2005) after postdoctoral positions at the Institute for Advanced Studies in Princeton (2000-2001) and University of Toronto (2002-2003) under Stephen Cook and Toni Pitassi. His educational background features a PhD from Universitat Politecnica de Catalunya supervised by Maria Luisa Bonet, complemented by dual Italian habilitations as full professor in Mathematical Logic (2012) and Computer Science. These qualifications underpin his rigorous theoretical approach across research domains. Galesi's research program centers on Computational Complexity and Logic in Computer Science , with specialized expertise in Proof Complexity (investigating resolution refinements and algebraic proof systems), SAT-Solving , Optimization , and applied domains like Group Testing and Network Tomography . His seminal work on space complexity in algebraic proof systems (JACM 2015) established foundational frameworks, while recent network tomography research develops mathematical models for node failure identification in communication networks using Boolean algebra and graph connectivity principles. Analysis of his 15 most recent publications (2022-2025) reveals a cohesive research trajectory bridging theoretical proof complexity and practical network analysis. Key trends include depth lower bounds in stabbing planes for combinatorial principles, vertex-connectivity metrics for failure localization, and algebraic investigations of vanishing sums in polynomial calculus. His work consistently applies combinatorial principles to derive tight bounds across graph structures while advancing the theoretical understanding of proof systems through tensor isomorphism and roots of unity analyses. His scientific recognition includes: ACM Computing Review Most Notable paper in Theory of Computing for 2012 Galesi mentors the next generation through PhD supervision of Massimo Lauria (2009), Ilario Bonacina (2015), and Fariba Ranjbar (2021), while hosting postdocs including Alan Skelley, Olaf Beyersdorff, and Massimo Lauria. His research is amplified through prestigious visiting positions at the Simons Institute for Theory of Computing (2015, 2021) and Tokyo Institute for Technology (2015), building on his foundational work at IAS Princeton and Toronto. He actively shapes the theoretical CS landscape as organizer of the Sapienza LOC3 (Logic, Complexity, Combinatorics, Computability) seminar series and founder of the RaTLoCC workshops (Ramsey Theory in Logic, Complexity and Combinatorics). His editorial role for Logical Methods in Computer Science (LMCS) and program committee service for CIAC, IJCAI, and FSTTCS conferences demonstrate sustained community leadership beyond his core research and teaching responsibilities in Calculus, Mathematical Logic, and Computational Complexity.

• Functional Safety
• Functional Testing
• Microprocessor Testing
• +9 more

Riccardo Cantoro is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is a member of the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. He is affiliated with the CAD - Electronic CAD & Reliability Group and the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His work bridges academic research and industrial applications through multiple commercially funded projects. Scientific Disciplinary Sector: IINF-05/A - Information Processing Systems ERC Sectors: PE7_4, PE6_2, PE6_11, PE6_12 His research focuses on functional safety, functional testing, and microprocessor testing, with a strong emphasis on embedded systems and reliability. He applies machine learning and formal methods to enhance test efficiency and system robustness, particularly in automotive and safety-critical domains. His work integrates computer-aided design, fault modeling, and resilience assessment in both hardware and AI systems. The recent publications highlight a trend toward data-efficient and intelligent testing methodologies, combining machine learning (e.g., TabPFN, active learning) with traditional electronic design automation. Topics include microcontroller performance screening, CNN resiliency, FeFET device testing, and system-level test optimization, reflecting a cohesive research agenda in trustworthy computing and hardware reliability. Scientific Awards: No awards explicitly mentioned in the provided texts. Advising and Grants: Dr. Cantoro supervises numerous PhD students in Computer and Systems Engineering, focusing on functional safety, test methodologies, and AI for CAD. He leads multiple industry-funded research projects, including collaborations with Infineon Technologies and Dana-TM4 Italia, on topics such as ATPG tools, speed monitor modeling, and power module reliability. His role as Scientific Manager/Head underscores his leadership in applied research and technology transfer. Labs and Teams: He is a core member of the CAD - Electronic CAD & Reliability Group (DAUIN) and contributes to the CARS@PoliTO center, fostering interdisciplinary research in automotive systems and sustainable mobility.

• Artificial Neural Networks
• Calibration
• Circuit Simulation
• +7 more

Simone Corbellini is an Associate Professor at the Polytechnic University of Turin, Department of Electronics and Telecommunications (DET), and a member of the College of Electronic, Telecommunications and Physics Engineering. His research spans electrical and electronic engineering, with a focus on artificial neural networks, calibration, wireless sensor networks, and embedded systems. Research keywords: Artificial Intelligence Metrology & Measurement IoT & Signal Processing Recent projects include MicrowaveRad (microwave radiometers for thermometry) and PVZEN Lab (energy community monitoring systems). He supervises PhD students Davide Ceschini, Davide Sgro', and Marco Sento, contributing to projects on low-power distributed sensors and biomedical devices. His scientific accolades include a National Patent for tire production process control. Teaching roles cover Testing and Certification and Electronic Circuits in Electronic, Biomedical, and Electrical Engineering programs.

Gianluca CAIAZZAView profile

Researcher

• Cybersecurity
• Robotics
• Software Engineering
• +3 more

Gianluca Caiazza is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. He is affiliated with the Research Institute for Complexity and the Temporary Center for the Innovation Ecosystem Project. His work focuses on cybersecurity within robotics and software systems, particularly in the Robot Operating System (ROS) framework. He teaches courses such as 'Operating Systems - MOD.1' and 'Software and System Verification' at both undergraduate and doctoral levels. His research interests span cybersecurity for robotic systems, static analysis techniques, blockchain applications in robotics, and secure middleware development. He has conducted extensive work on enhancing ROS security through tools like SROS2 and static analysis frameworks like LiSA, addressing vulnerabilities in DDS systems and penetration testing methodologies. Caiazza's recent publications emphasize automated policy extraction, microservice security, and IIoT system scalability. His work bridges theoretical computer science with practical applications in robotics, contributing to both academic journals and industry-oriented conferences. He collaborates actively with institutions like IEEE and Springer, focusing on advancing secure robotic technologies. His teaching activities include laboratory modules for software project development and advanced verification techniques for software systems. Despite no listed awards, his contributions to ROS security frameworks are recognized within academic and industrial robotics communities.