Giacomo Vitali is a Ph.D. candidate in Computer and Systems Engineering at the Politecnico di Torino , Department of Control and Computer Science (DAUIN), while also serving as a Lecturer at the same institution. His research focuses on Quantum Computing , Machine Learning , and Parallel and Distributed Systems . Education: M.Sc. in Physics of Fundamental Interactions (2018) from the University of Pisa Research: Pioneering hybrid quantum-classical algorithms for industrial applications, including BBQ-mIS and DEN models for graph problems and quantum hardware optimization Teaching: Contributing to courses on Quantum Hardware Design and Quantum Computing at the M.Sc. level, as well as undergraduate courses in Computer Science
Tommaso Vanzan is a Researcher at the Department of Mathematical Sciences "GL Lagrange" (DISMA) at Politecnico di Torino . He holds a PhD from the University of Geneva (2020) under Prof. Martin J. Gander and was a postdoctoral researcher at EPFL (2020-2023) under the CSQI Chair. His research focuses on iterative methods , optimization algorithms , and uncertainty quantification for PDE-constrained problems, with applications in Stokes-Darcy coupling and manifold-based machine learning . His recent work includes multilevel quadrature for risk-averse optimization and spectral coarse spaces for substructured Schwarz methods. He has received the SWICCOMAS Prize 2021 for computational methods. He contributes to the open-source GDGMatlab library for finite element and discontinuous Galerkin methods, emphasizing educational and research usability over high-performance computing. Future developments target generalized boundary conditions in DG formulations.
Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)
Valeria Cardellini is an Associate Professor in Computer Engineering at the University of Rome Tor Vergata, with a PhD in Computer Science and Automation Engineering (2001). Her research focuses on distributed computing systems, particularly cloud-based and edge-based systems, with emphasis on resource provisioning, self-adaptation, and QoS-driven optimization. She has published over 90 peer-reviewed papers, including award-winning works at IEEE SOSE and ACM DEBS conferences, and has led collaborations in EU projects like EoCoE (2015-2018) and the Cloud for Europe tender (2016-2017). She has organized major events such as UCC 2018 and contributed to workshops like Auto-DaSP. Teaching includes distributed systems, cloud computing, and computer architectures at both undergraduate and graduate levels. Education: PhD in Computer Science and Automation Engineering (2001), University of Rome Tor Vergata Previous academic qualifications not explicitly listed Research Interests: Her work spans geo-distributed systems, serverless computing, edge-cloud continuum optimizations, and reinforcement learning for adaptive systems. Key areas include energy-efficient scheduling, fault-tolerant architectures, and QoS-aware service composition. Recent projects address scalability challenges in data stream processing and cybersecurity for distributed environments. Awards & Recognition: Best Paper Awards at IEEE SOSE 2011, ACM DEBS 2015, and ACM DEBS 2016 Keynote Speaker at IEEE MASCOTS 2016 Grants & Projects: Led EU H2020 projects (EoCoE), contributed to the European Cloud tender, and managed COST Action IC1304 (ACROSS). Current research involves cloud-edge continuum frameworks like Serverledge and FIGARO. Labs & Teams: Active in developing experimental platforms such as MOSES (for self-adaptation testing) and OpenCoarrays (coarray Fortran frameworks). Collaborates with interdisciplinary teams on HPC, distributed systems, and cybersecurity.
Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.
Wilma Russo is a Full Professor of Computer Engineering at the University of Calabria's Department of Computer Engineering, Modeling, Electronics and Systems (DIMES). She holds a degree in Physics from the University of Naples (1975). Her research focuses on distributed/parallel systems in heterogeneous environments, with current interests in agent-based computing, content delivery networks, and Internet of Things. Her publications demonstrate sustained innovation in IoT architectures, agent-based modeling, and edge computing solutions. Recent work emphasizes methodological frameworks for IoT integration and opportunistic service paradigms.
Roberto Corizzo is a Research Fellow at the Department of Computer Science, University of Bari, Italy. He holds a Ph.D. and is affiliated with the LACAM Laboratory. His research focuses on big data analytics, data mining, predictive modeling for sensor networks, energy prediction in smart grids, and anomaly detection. He has conducted research internships at INESC TEC (Portugal) under Prof. João Gama and at the American University (Washington D.C.) under Prof. Nathalie Japkowicz. Key research interests include: Big Data Analytics Predictive Modeling for Sensor Networks Energy Prediction in Smart Grids Anomaly Detection in Dynamic Systems His work spans over 15 publications from 2014–2019, addressing challenges in data stream analysis, renewable energy forecasting, and distributed computing. Notable contributions include Spark-GHSOM for large-scale data clustering and DENCAST for multi-target regression. He has organized conference challenges (e.g., ECML/PKDD Discovery Challenges) and contributed to projects like VIPOC for renewable energy prediction. No scientific awards are explicitly listed. He collaborates with academic institutions globally and participates in interdisciplinary projects involving energy systems and semantic services in big data platforms.
Dr. Gabriele Mencagli is an Associate Professor in the Department of Computer Science at the University of Pisa, Italy. He holds a Ph.D. in Computer Science (2012) and has served as an Assistant Professor (2014–2018) and Tenure-Track Professor (2018–2021) before his current position. His research focuses on parallel systems, including architectures, programming models, and runtime systems for data stream processing. He leads work on the WindFlow stream processing library and has contributed to projects like TEXTAROSSA, ADMIRE, and EUPEX. He co-organized major conferences like HPDC 2024 and DEBS 2025 and serves on editorial boards for journals like Future Generation Computer Systems and Cluster Computing. Education: B.Sc. (2006, summa cum laude), M.Sc. (2008, summa cum laude), Ph.D. (2012) in Computer Science, all from the University of Pisa. Research Interests: Parallel programming, self-adaptive systems, data stream processing, GPU/FPGA acceleration, and high-performance computing. Over 80 publications in top journals and conferences, including IEEE TPDS, JPDC, and Euro-Par. Awards include Italian Habilitation as Full Professor (2025). Teaching: Courses on High-Performance Computing and Computer Architecture, including CUDA programming and parallel design patterns. Active in curriculum development for both bachelor’s and master’s programs. Grants & Projects: Principal investigator in EU-funded projects (e.g., TEXTAROSSA, NOUS) and collaborations with industry (e.g., List-group S.p.A., Autodesk). Focus on exascale computing, digital twins, and edge computing.
Andrea Marongiu is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia, specifically affiliated with the Mathematics department. He maintains an active research profile while teaching multiple courses in computer architecture and parallel systems. His research interests span computer architecture, high performance computing, parallel programming, and embedded systems. Marongiu focuses particularly on memory systems, heterogeneous computing architectures, FPGA-based acceleration, and real-time performance analysis. His work bridges theoretical foundations with practical implementation challenges in modern computing systems, with special attention to predictable execution models and quality of service guarantees. Analysis of his recent publications reveals a strong emphasis on memory bandwidth management in heterogeneous systems, particularly focusing on FPGA-based architectures and multicore SoCs. His research trajectory shows consistent work in memory interference analysis, PREM (Predictable Execution Model) scheduling techniques, and fine-grained QoS control mechanisms. The publications demonstrate a progression from general parallel programming concepts toward increasingly specialized techniques for resource-constrained environments like autonomous vehicles and edge computing devices. Marongiu teaches several advanced computer science courses including Computer Architecture I & II, Compilers, High Performance Computing, and Electronic Calculators across multiple degree programs. His teaching approach emphasizes both theoretical foundations and practical implementation, with a focus on RISC-V architecture and modern parallel programming techniques. The course materials indicate he incorporates hands-on laboratory work as an essential component of his pedagogy, particularly in areas like compiler construction and parallel programming.
Francesco Gregoretti was a former faculty member at the Polytechnic of Turin , affiliated with the Department of Electronics and Telecommunications (DET) . He contributed to teaching and academic leadership, including roles as Course Owner for Applied Electronics in recent academic years (2019/20, 2020/21) and as a collaborator in Computer Engineering courses. His doctoral program involvement spanned Electronics and Communications Engineering cycles from 2003/2004 (19th cycle) to 2012/2013 (28th cycle) . Research interests focused on highly parallel architectures , DC-DC switch-mode power conversion , and heterogeneous computing platforms . He led research groups like CodeSimulink (automatic synthesis of analog circuits) and projects addressing high-performance software acceleration. Key projects include: ASPIDA (2002–2005) – Asynchronous open-source processor development Sviluppo di un ambiente di coprogettazione hardware/software basato su Simulink (2001–2003) – EU-funded Design of HW and SW Platforms for Sensor Networks (2007–2008) – Commercially funded Publications emphasize power electronics and parallel computing, with recent works (2021, 2020) on real-time monitoring and digital control frameworks. Earlier contributions (1993–1994) explored SIMD array processors for high-performance computing. No scientific awards are listed. However, he co-invented a patent ( Magnetic application in cancer research and therapeutic aid ) with Romano Borchiellini, Maurizio Bressan, Umberto Lucia, Bartolomeo Montrucchio, and Emilio Paolucci, reflecting interdisciplinary engagement. His academic narrative includes decades of project management and teaching, with a focus on merging theoretical research (parallel architectures) with applied engineering (power systems and sensor networks). The listed doctoral cycles and teaching roles highlight sustained contributions to academic training in electronics and telecommunications.
Ngoc Mai Monica Huynh is a Researcher in the Department of Mathematics at the University of Pavia, specializing in Scientific Computing with a focus on Numerical Methods and Applications. Her work centers on developing advanced numerical techniques for simulating cardiac electrophysiology, particularly through high-performance computing approaches. Her research interests include parallel algorithms, domain decomposition methods (BDDC/FETI-DP), and virtual element discretizations applied to cardiac models like the Bidomain equations. She has contributed to scalable solvers for reaction-diffusion systems and implicit time discretization techniques in heterogeneous media. Recent work emphasizes robust preconditioners for discontinuous Galerkin discretizations, algebraic multigrid solvers, and cellular-resolution simulations of cardiac tissue. Her publications reflect a strong focus on computational efficiency and mathematical rigor in biomedical modeling. Dr. Huynh's research is supported by collaborations within the Department's Scientific Computing group and leverages high-performance computing resources. Her expertise bridges numerical analysis, parallel computing, and biophysical modeling for cardiac electrophysiology applications.
Marco Govoni is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Physics, Informatics, and Mathematics. His research focuses on quantum defects, electronic structure theory, and many-body perturbation methods (e.g., GW/BSE), with applications to spin defects in semiconductors for quantum technologies, perovskites, and heterogeneous materials. He develops computational tools like the WEST code and explores quantum simulations on high-performance and near-term quantum computers. He teaches courses in General Physics and Theoretical Physics, emphasizing computational methods and material science. Research interests include: condensed matter physics, quantum computing, electronic structure theory, defect engineering, and computational material discovery. His work addresses challenges in simulating large systems, optimizing hybrid functionals, and leveraging GPU acceleration for many-body calculations. Key contributions include studies on self-trapped excitons, spin defects in diamond/SiC, and dielectric-dependent functionals for heterogeneous interfaces. Publications span topics like quantum defect embedding theory, GPU-accelerated Bethe-Salpeter equation solutions, and machine learning for dielectric screening. He collaborates widely, contributing to software interoperability and exascale computing strategies for electronic structure codes.
Simone Scacchi is a Researcher at the Department of Mathematics, University of Milan, and a member of the Scientific Computing Group focusing on numerical methods and applications. His work emphasizes cardiac electrophysiology modeling, parallel computing, and mathematical epidemiology. He develops advanced numerical techniques like algebraic multigrid solvers, BDDC preconditioners, and virtual element methods for complex biomedical and environmental systems. Research interests include computational cardiology (e.g., Bidomain models, arrhythmias), epidemic control strategies for agricultural diseases (e.g., Xylella fastidiosa), and high-performance computing for fluid-structure interaction problems. His contributions span parallel algorithms, domain decomposition methods, and operator learning applications in medical imaging. Key Projects: Cardiac electro-mechanical modeling, regional control of vector-borne epidemics, and scalable preconditioners for cardiac simulations. Publications highlight interdisciplinary approaches combining numerical analysis with biomedical and ecological applications. Advances in his work address challenges in simulating cardiac dynamics, optimizing epidemic containment, and enhancing computational efficiency for large-scale systems.
Walter Gallego Gomez is a PhD Student in Computer and Systems Engineering (39th cycle, 2023-2026) and an External Lecturer/Teaching Assistant at the Department of Control and Computer Science (DAUIN) of the Polytechnic University of Turin. His academic and research journey is supported by an EBRAINS-Italy research grant. Bachelor's in Electronic Engineering (University of Antioquia, 2015) Master's in Computer Engineering (Polytechnic University of Turin, 2018) His research intersects life sciences and computer science, focusing on bioinformatics algorithms, neuromorphic hardware (Loihi2/Lava), and their implementation. He collaborates with the Electronic Design Automation (EDA) group and external entities like Candiolo Oncology Center and Telluride Neuromorphic Cognition Engineering workshop. Teaching roles include System-on-chip architecture (2024/25) and Applied AI and machine learning (2023/24-2024/25) as a course collaborator. Recent publications highlight trends in somatic structural variant detection and neuromorphic ecosystem benchmarking. His work spans automotive embedded systems (Marelli, 2018-2021), academic research assistantship (2021-2023), and interdisciplinary collaborations.
Biagio Cosenza is an Associate Professor at the Department of Computer Science, University of Salerno, Italy. He leads research in high-performance computing, compiler technology, and software optimization. Previously, he was a Senior Researcher at TU Berlin (2015-2019) and a Post-Doctoral Researcher at the University of Innsbruck, Austria (2011-2015). His educational background includes: Ph.D. from University of Salerno (2011), supervised by Prof. Vittorio Scarano Dr. Cosenza's research focuses on creating efficient programming models for heterogeneous computing systems. His work spans compiler technology, automatic performance tuning, and energy-efficient computing approaches. He has made significant contributions to SYCL-based programming models and MPI implementations, with emphasis on portability across diverse hardware platforms including GPUs and accelerators. His research often bridges theoretical computer science with practical applications in scientific computing and bioinformatics. His recent publications demonstrate a strong trend toward heterogeneous and distributed computing, with particular emphasis on energy efficiency, performance portability, and SYCL-based programming models. Many papers focus on applications in drug discovery and scientific simulations, showing how his theoretical work translates to real-world scientific problems. Dr. Cosenza has received several prestigious recognitions: Best Paper Award at the 14th BenchCouncil International Symposium (2022) Elevated to Senior Member of the IEEE (2022) Recognized as ACM Senior Member (2022) He leads multiple significant research projects including the PRIN 2022 project "LibreRT" and the EuroHPC project "LIGATE." His work has secured substantial funding from sources including the German Research Foundation (DFG), the Italian Ministry of Education, University and Research, and EuroHPC. He actively mentors students and collaborators on advanced topics in high-performance computing. Dr. Cosenza is a member of the ISIS Lab at the University of Salerno and contributes to open standards through his work with the Khronos Group and SYCL Working Group. His research group develops tools like CELERITY, a C++ SYCL-based programming model for accelerator clusters, and EMPI, an enhanced message passing interface in modern C++.
