Alberto Oliveri is an Associate Professor at the University of Genoa in the Department of Naval, Electrical, Electronic and Telecommunications Engineering. He teaches courses including Circuits and Systems, Nonlinear Circuits and Systems, Power Management, and Elements of Electrical Technology for undergraduate and graduate programs in Electronic Engineering, Information Engineering, Industrial Technologies, and Chemical Engineering. His research focuses on advanced topics in power electronics and control systems, with particular emphasis on: Modeling and optimization of magnetic components (inductors) for switch-mode power supplies FPGA implementation of nonlinear model predictive control algorithms Synthetic inertia solutions for renewable energy grid integration Embedded control systems for power converters Advanced modeling of ferrite-core and amorphous-core inductors His recent publications (2022-2025) demonstrate a consistent focus on improving power conversion efficiency through advanced control strategies and hardware implementation. Research themes include predictive control optimization, magnetic component characterization under saturation conditions, renewable energy grid support functions, and embedded algorithm development for real-time power system monitoring.
Maurizio Zamboni is a Full Professor at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he also serves as Student Ombudsman. His academic career spans over three decades with continuous teaching and research contributions in electronics and computing fields. Professor Zamboni's research interests focus on cutting-edge areas including CMOS integrated circuits, computer architecture, quantum computing, semiconductor devices, and VLSI design. His work particularly emphasizes emerging nanotechnologies for digital microelectronic architectures and the design of high-performance or low-consumption processing systems. He has developed expertise in circuit architectures for probabilistic computing, logic-in-memory computing, magnetic devices, and quantum architectures. His recent publications (2021-2025) reveal a strong trend toward quantum computing applications, in-memory processing architectures, and novel approaches to overcoming the memory wall problem. These works span both theoretical algorithm development and practical hardware implementations, with significant focus on quantum annealing, FPGA-based quantum emulation, and memory-mapped processing architectures. Professor Zamboni has been actively supervising PhD students working on quantum computing algorithms, hardware AI accelerators for automotive applications, and quantum-related optimization approaches. He leads research within the VLSILAB Group at DET, focusing on the intersection of nanoelectronics, quantum computing, and advanced computer architectures. His work bridges theoretical computer science with practical electronic design, creating novel solutions for next-generation computing challenges.
Gian Pietro Picco is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento, Italy. His research focuses on wireless networks, particularly ultra-wideband (UWB) technology, wireless sensor networks, and cyber-physical systems. He teaches courses including Distributed Systems, Low-power wireless networking for the Internet of Things, and Programming 2. Professor Picco's research interests span several interconnected domains in pervasive computing and networking: Ultra-wideband (UWB) technology for precise localization and communication Wireless sensor networks and Internet of Things (IoT) systems Cyber-physical systems and networked control Energy-efficient networking protocols Distributed systems and middleware Software engineering approaches for networked embedded systems His recent publications demonstrate a strong focus on ultra-wideband technology applications, particularly in localization, ranging, and concurrent transmissions. His work bridges theoretical foundations with practical implementations, often addressing real-world challenges in human-robot interaction, contact tracing, and infrastructure monitoring. A notable trend is the increasing application of UWB technology for precise positioning in complex environments, with significant contributions to understanding and mitigating human occlusion effects on ranging accuracy. Professor Picco has received numerous prestigious awards for his research contributions: Best Paper Award at IPSN'23 for "Network On or Off? Instant Global Binary Decisions over UWB with Flick" Best Paper Award at IPIN 2019 for "TALLA: Large-scale TDoA Localization with Ultra-wideband Radios" Best Paper Award at EWSN 2018 for "Concurrent Ranging in Ultra-wideband Radios" Best Paper Award at IPSN 2015 for "Geo-referenced Proximity Detection of Wildlife" Best Paper Award at IPSN 2011 for "Wireless Sensor Networks for Adaptive Lighting in Road Tunnels" Best Paper Award at IPSN 2009 for "Monitoring Heritage Buildings with Wireless Sensor Networks" Mark Weiser Best Paper Award at PerCom 2012 Professor Picco actively advises numerous PhD and Master's students, with several of his advisees becoming prominent researchers in wireless networking. His laboratory has secured significant funding for research projects in wireless sensor networks, IoT systems, and cyber-physical systems. His work has practical applications in heritage building monitoring, wildlife tracking, road tunnel lighting systems, and pandemic contact tracing. His research group maintains the Cloves large-scale ultra-wideband testbed and has developed several middleware systems for wireless sensor networks, including Lime and TeenyLIME. The group collaborates extensively with international research institutions and has made significant contributions to standardization efforts in IoT networking protocols.
Pietro Manzoni is a Professor of Computer Engineering at the Polytechnic University of Valencia (UPV), Spain. He holds a Master's from the University of Milan (1989) and a Ph.D. from Politecnico di Milano (1995). His research focuses on IoT, edge computing, and wireless networks, with emphasis on TinyML, LPWAN, and edge-cloud systems. He coordinates the Computer Networks Research Group (GRC) and is active in IEEE committees. Education includes a Master's in Computer Science (Università degli Studi di Milano, 1989) and a Ph.D. in Computer Science (Politecnico di Milano, 1995). He interned at Bellcore Labs (USA, 1992–1993) and ICSI (USA, 1994). Research interests span IoT applications, resource-constrained devices, and distributed systems. His work prioritizes empirical validation through prototypes. Teaching includes courses on Networks and Security, Intelligent IoT Systems, and IoT fundamentals in Spanish programs. Publications emphasize IoT protocols, UAV swarms, and TinyML. No scientific awards listed, but over 130 theses advised. Coordinates GRC projects and contributes to editorial boards and conferences.
Gemma Catolino is an Assistant Professor at the Department of Computer Science, University of Salerno, and affiliated with the Software Engineering (SeSa) Lab. She has also served as an Assistant Professor at Tilburg University and Eindhoven University of Technology through the Jheronimus Academy of Data Science from September 2022 to December 2023, and previously as a Postdoctoral Researcher at Delft University of Technology and Tilburg/Eindhoven institutions. PhD in Computer Science, University of Salerno (2020), supervised by Prof. Filomena Ferrucci MSc in Management and Information Technology, University of Salerno (2016, magna cum laude) BSc in Computer Science, University of Molise (2014) Her research centers on empirical software engineering, focusing on both technical and social aspects affecting software development. Key areas include code smells, defect prediction, testability, changeability, and the emerging concept of “Community Smells”—social dysfunctions in developer teams. She investigates how human factors, team diversity (especially gender), and developer experience influence software quality and maintenance effort, often using mining software repositories and machine learning techniques. Her recent publications span high-impact journals and conferences such as IEEE TSE, EMSE, JSS, ICSE, and ICSME, with a strong trend toward integrating social and technical metrics for just-in-time defect prediction in mobile applications, analyzing community dynamics, and applying software quality metrics to cybersecurity contexts like dark web analysis. She has also contributed to MLOps and serverless computing. She has received several honors including a DEI research grant (2020), Best Technical Paper at BENEVOL 2019, first and second place in ACM Student Research Competitions (2018, 2017), and the Best Master Thesis award from the Italian Software Metrics Association (2017). Gemma Catolino has been actively engaged in academic service as a referee for top journals like IEEE TSE, EMSE, JSS, and IST, guest editor for special issues, and program/organizing committee member for major conferences including ICSE, MSR, SANER, and MobileSoft, where she served as Program Co-Chair in 2022. She has also contributed as a teaching assistant, lecturer, and course coordinator in machine learning and software engineering courses. She leads and contributes to research projects involving international collaborations, particularly with researchers such as Prof. Filomena Ferrucci, Prof. Andy Zaidman, Prof. Willem-Jam van den Heuvel, and Prof. Alexander Serebrenik. Her work bridges empirical software engineering with practical tool development and socio-technical analysis, positioning her at the forefront of modern software engineering research.
Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)
Marco Aldinucci is a Full Professor and Head of the Parallel Computing group at the University of Torino's Computer Science Department. He leads the HPC Key Technologies and Tools (HPC-KTT) national lab under CINI, involving 38 Italian universities. His expertise spans parallel programming models, HPC systems, federated learning, and energy-efficient computing. Aldinucci has secured over €10M in EU research funding, contributed to frameworks like Fastflow and Streamflow, and pioneered initiatives like the HPC4AI lab and the CINI HPC-KTT lab. His research focuses on advancing exascale computing, cloud-HPC integration, and AI-driven medical solutions. Notable projects include the Gaia AVU-GSR solver for exascale systems and the DeepHealth Toolkit for medical AI. He has held governance roles in EuroHPC and chairs the Observatory on Trends and Applications of Supercomputing in Italy. Aldinucci’s publications (150+) address parallel algorithms, distributed learning, and sustainable HPC infrastructure. His work has been recognized with awards from HPC Advisory Council, NVIDIA, IBM, and Autodesk. Current initiatives include the Software & Integration lab at the Italian National HPC Centre (ICSC) and leadership in the OpenScience working group at Torino. His advising includes Iacopo Colonelli, whose thesis won CINI’s 2023 best award. He actively engages in EU projects, workflow systems, and standards for hybrid computing environments. Aldinucci’s labs and collaborations drive innovations in HPC portability, energy efficiency, and AI scalability.
Mirco Marchetti is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Engineering 'Enzo Ferrari'. He specializes in cybersecurity, network security, and automotive systems. His teaching responsibilities include courses on cybersecurity fundamentals, computer networks, operating systems, and automotive cyber defense. Research interests focus on intrusion detection systems (IDS), vehicular networks (VANETs), machine learning applications in cybersecurity, and automotive cybersecurity. He has contributed to projects like HackCar (automotive attack/defense testbed) and RealCAN (real-time CAN bus analysis tools). His work also explores adversarial attacks on ML-based systems and secure communication protocols for industrial and vehicular systems. Recent publications emphasize automotive cybersecurity (e.g., Mercedes-Benz infotainment system analysis, CAN bus anomaly detection), ML robustness against adversarial attacks, and zero-trust architectures. He collaborates with the SECloud research group (secloud.ing.unimore.it) and uses experimental platforms like Software-Defined Radios for security evaluations. Teaching responsibilities span multiple academic programs including Master's degrees in Computer Engineering and Artificial Intelligence Engineering. Courses emphasize practical skills in Linux/Unix administration, network configuration, and embedded system security.
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 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.
Mauro Andreolini is a University Researcher at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. He teaches Operating Systems and Secure Software Development courses within the Computer Science degree program. His research focuses on Cybersecurity , Network Security , Machine Learning in Security , and Cloud Computing . His recent publications analyze Data Privacy through geohashing and clustering, Adversarial Attacks in cybersecurity, and Moving Target Defense architectures. He has also contributed to frameworks for Automated Security Assessments using deductive reasoning and Realistic Botnet Detection benchmarks. Andreolini's work addresses Graph Neural Networks in intrusion detection, n-Gram Analysis for automotive network security, and Side-Channel Vulnerabilities in USB devices. He collaborates with researchers like Artioli, Ferretti, Marchetti, and Colajanni on projects spanning Adversarial Machine Learning , Secure Software Development , and Cloud-Based Monitoring .
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).
Beppe Liotta is a Full Professor at the Department of Engineering, University of Perugia. He serves as Rector Delegate for ICT and Digital Agenda. His research spans network discovery, graph drawing, algorithm engineering, and computational geometry . Laurea in Electrical Engineering (1990), Ph.D. in Computer Engineering (1995), both from University of Rome 'La Sapienza' Post-doc at Brown University (1995-1996) Current teaching: Information Visualization and Database Management Systems Liotta has authored over 170 papers and led projects like VisFAN (financial crime detection), VHyXY (large graph visualization), COWA (web traffic analysis), and WhatsOnWeb (web clustering). His work focuses on hybrid visualizations and network robustness . Recent articles highlight his expertise in biological networks , financial activity networks , and one-to-many matched graph visualizations . He has contributed to journals like IEEE Transactions on Visualization and Computer Graphics and conferences including PacificVis and Graph Drawing . Liotta actively participates in scientific service, including editorial roles for the Journal of Graph Algorithms and Applications and program committees for IEEE PVIS 2019.
Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing
Andrea Calimera is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is actively involved in teaching and research, contributing to doctoral programs and undergraduate and graduate courses in computer engineering and data science. Full Professor (L.240), Polytechnic University of Turin Department of Control and Computer Science (DAUIN) Member, SmartData@PoliTO - Big Data and Data Science Laboratory Member, College of Computer, Film and Mechatronics Engineering His research interests center on electronic design automation, energy-efficient electronic systems, and low-power design, with strong connections to artificial intelligence, embedded systems, and IoT. His work bridges hardware and software optimization for intelligent edge devices. The recent publications (2023–2025) reflect a focused trend on federated learning, secure and efficient AI deployment on edge devices, and low-power embedded systems. Topics include robust evaluation in federated learning, resource management under label skew, homomorphic encryption for private tensor operations, pipeline optimization for keyword spotting, and side-channel attacks via DVFS for neural network fingerprinting—highlighting expertise in both performance and security of AI systems on constrained hardware. Andrea Calimera supervises PhD and master's students and leads research projects funded by competitive and commercial grants. He has contributed to national and international patents on low-power depth estimation and single-image signal processing. Scientific Director, SENSEI Project (2017–2019): Energy-efficient machine learning on chip for IoT Scientific Director, Commercial Project (2020–2022): Design tools for AI on energy-efficient embedded mobile devices Supervision of PhD student Erich Malan (ongoing, since 2022) on distributed and federated learning over IoT networks Supervision of Bachelor's student Chen Xie (2020–2024) on synthesis of smart sensors He teaches courses such as High-Level Synthesis (PhD), Synthesis and Optimization of Digital Systems, Machine Learning for IoT, and Efficient Computing for Artificial Intelligence across Computer Engineering and Data Science programs. His research group is EDA - Electronic Design Automation (DAUIN), which focuses on hardware-software co-design for intelligent systems.
