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Juan David Guerrero BalagueraView profile

Research Fellow

• AI Accelerators
• GPU Reliability
• Fault Tolerance
• +7 more

Juan David Guerrero Balaguera is a Research Fellow at Politecnico di Torino's Department of Automatic Control and Computer Science (DAUIN), affiliated with the CAD group. He holds a Ph.D. in Computer and Control Engineering from Politecnico di Torino (2024), advised by Prof. Matteo Sonza Reorda and Prof. Ernesto Sanchez. His research focuses on dependable hardware for safety-critical systems, including GPU reliability, fault tolerance, AI accelerators, and functional in-field testing. Prior to his Ph.D., he earned a Master's (2017) and Bachelor's (2013) in Electronics Engineering from Universidad Pedagógica y Tecnológica de Colombia, where he taught digital design, embedded systems, and FPGA-based image processing from 2014 to 2020. His research interests span advanced FPGA design, computational arithmetic for AI, fault effects analysis in GPUs, and reliability assessment of neural networks. Notable contributions include methods for generating self-test libraries (STLs) for GPUs, evaluating fault impacts on TCUs, and enhancing CNN robustness via dropout layer optimization. Education: Ph.D. in Computer and Control Engineering, Politecnico di Torino (2024) M.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2017) B.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2013) He has received the Ph.D. Quality Award (2023 and 2024) from Politecnico di Torino and Best Paper recognitions at DATE 2023 and DDECS 2021. His work bridges theoretical fault models with practical GPU testing methodologies, emphasizing real-world applications in AI and edge computing. Current teaching roles include collaborating on GPU programming (Master's level) and computer sciences courses (Automotive Engineering). Research collaborations involve exploring reliability trade-offs in split-computing DNNs and developing fault-aware design flows for AI accelerators.

Valeria CardelliniView profile

Associate Professor

• Distributed Computing Systems
• Cloud Computing
• Edge Computing
• +3 more

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.

Sandro Luigi FioreView profile

Associate Professor

• Data Science
• Big Data
• Scientific Data Management
• +13 more

Sandro Luigi Fiore is an Associate Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy. He has held academic positions at the University of Salento and visiting scientist roles at the University of Chicago and Lawrence Livermore National Laboratory. His research integrates data science, big data, and high-performance computing with climate informatics and open science. Education: Ph.D. in Innovative Materials and Technologies, University of Lecce, 2004 Master of Science in Computer Engineering (with honors), University of Lecce, 2001 His research interests span Data Science, Big Data, Scientific Data Management, Artificial Intelligence, and Distributed/Cloud/Parallel Computing , with a strong application focus on Climate Change and Open Science . He develops FAIR-enabled data analytics solutions and contributes to large-scale data infrastructures such as ESGF, EOSC-hub, and INDIGO-DataCloud. His work emphasizes provenance, reproducibility, and interoperability in scientific computing. The articles reflect a consistent trajectory in large-scale scientific data systems , evolving from Grid-DBMS and parallel computing to modern applications in climate informatics, AI, and FAIR data. Key themes include distributed data management, middleware, exascale software, and reproducible workflows in HPC environments. Scientific Awards: Earth System Grid Federation Team Award (2017) UNIDATA Community Equipment Award (2011) Best Student Paper Award, ITCC2003 Fiore has advised on and led multiple national and European projects including EOSC-hub, IS-ENES, and BARRACUDA (RDA-Europe3). He actively supports the adoption of FAIR data principles in scientific repositories, advising on data policies, architecture, provenance, and interoperability. He is a member of the FAIR Champions group and serves on the Advisory Board of FAIRsFAIR. He is involved in key research teams and projects such as the Earth System Grid Federation (ESGF) , Globus Lab (University of Chicago) , and PCMDI/LLNL . His work contributes to climate model intercomparison (CMIP5/6) and the development of next-generation data infrastructure for open science.

Gabriele AccarinoView profile

Research Fellow

• Machine Learning
• Climate Science
• Environmental Data Analysis
• +4 more

Gabriele Accarino is a Postdoctoral Research Scientist at Columbia University's Learning the Earth with Artificial Intelligence and Physics (LEAP) Science and Technology Center, focusing on machine learning applications in climate science. He previously held a research fellowship at the Department of Engineering for Innovation at the University of Salento and served as an adjunct professor there. His career includes a Junior Scientist role at the CMCC Foundation's Advanced Digital Innovation Center (ADIC), where he led the machine learning research unit and contributed to European projects like Silvanus. Education: Master’s in Computer Engineering (with honors), University of Salento PhD in Environmental Sciences, University of Salento His research spans data-driven climate modeling, spatio-temporal analysis, and machine learning for extreme weather events, pandemics, and environmental conflicts. He specializes in LSTM networks, Transformer architectures, and HPC integration with big data analytics. Recent publications highlight his work on AI applications in aquatic sciences, climate change knowledge transfer, ensemble machine learning for cyclones, and pandemic modeling. His projects address interdisciplinary challenges like environmental-COVID-19 links and pollution-conflict dynamics. Scientific Awards: Research Fellowship at University of Salento's Department of Engineering for Innovation Gabriele co-supervises European research initiatives and maintains affiliations with the CMCC Foundation while advancing postdoctoral work at Columbia University. His expertise bridges climate science, computational methods, and public health analytics.

Loris BelcastroView profile

Researcher

• cloud computing
• edge computing
• big data
• +4 more

Loris Belcastro, PhD, is a researcher in computer engineering at the University of Calabria, Italy, affiliated with the Department of Electronics, Computer Science and System Sciences (DIMES). He earned his PhD in Information and Communication Engineering from the same institution and serves on editorial boards for journals like the Journal of Supercomputing and SN Computer Science. In 2024, he obtained the Italian National Academic Qualification as Associate Professor in Computer Engineering. His research focuses on cloud and edge computing big data analytics machine learning social media analysis high performance computing parallel/distributed data analysis His recent publications highlight trends in edge-cloud continuum integration, distributed machine learning, and scalable big data analysis. He has contributed to frameworks for urban mobility prediction, industrial IoT applications, and HPC optimization through libraries like ParSoDA-Py. He has served as guest editor for journals such as Future Generation Computer Systems and Sensors, reflecting his expertise in distributed systems and data analysis.

• High-Performance Computing
• Cloud Computing
• Parallel Computing
• +3 more

Dr. Luigi Santangelo is affiliated with the University of Pavia. He focuses on High-Performance Computing (HPC), Cloud Computing, and Parallel Computing, with significant contributions to bioinformatics applications and network security. His research integrates cloud-based scheduling for protein analysis and hemodynamic simulations, emphasizing computational efficiency and cost analysis in cloud vs on-premise systems. His work spans hybrid parallel computing models (OpenMP-MPI) and enterprise network management tools like RedHat Directory Server and OpenNMS. Recent efforts prioritize optimizing cloud infrastructure for bioinformatics tasks, such as protein secondary structure analysis and geometric motif searches in proteins. No scientific awards or grants are listed, but his publications reflect a strong emphasis on practical HPC solutions and cybersecurity protocols like SPID digital identity systems. He has not listed formal advisees, though his collaborative research likely involves student contributions.

• Autonomous Driving Systems
• Software Reliability Engineering
• Hardware Fault Tolerance
• +3 more

Jacopo Sini is a Research Fellow at the Department of Automatic Control and Computer Science (DAUIN) of the Polytechnic University of Turin. His research focuses on safety-critical automotive systems, software reliability engineering, and emotion recognition. He collaborates on Master's courses related to model-based software design and autonomous vehicles. He is affiliated with the CAD - Electronic CAD & Reliability Group, contributing to projects involving ISO26262 compliance, hardware fault tolerance, and simulation methodologies. His work includes developing real-time driver monitoring systems and enhancing social acceptance of autonomous vehicles through emotion analysis. Key research outputs include studies on control flow checking for automotive embedded systems, emotion recognition algorithms, and test benches for hardware failure diagnostics. Recent publications explore Rust programming language safety features and AR-based educational platforms.

Gabriele MencagliView profile

Associate Professor

• Parallel Programming
• Parallel Architectures
• Self-* Systems
• +3 more

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.

Luca SpalazziView profile

Associate Professor

• Computer Science
• Cybersecurity
• Blockchain Technology
• +5 more

Luca Spalazzi is an Associate Professor at the Department of Information Engineering , Università Politecnica delle Marche , Italy. His research spans multiple domains including cybersecurity , blockchain technology , machine learning , and telerehabilitation systems for Parkinson's disease. He applies formal methods to software verification and security analysis, with a focus on real-time systems and distributed architectures . Key research areas: Cybersecurity, Blockchain, Machine Learning, IoT, Formal Verification Recent work: Blockchain-based sustainable supply chains, Zero-Knowledge Proofs, Smartphone health monitoring His publications (2013-2025) demonstrate expertise in malware detection , smart contract verification , and AI-driven health solutions . Articles include BRAIN 2024 workshop organization and RAPIDO system for Parkinson's telerehabilitation.

• Computational Fluid Dynamics
• Aerospace Engineering
• Turbulence
• +5 more

Bernardini Matteo is an Associate Professor at the Department of Civil and Environmental Engineering, Sapienza University of Rome. His research focuses on high-fidelity computational fluid dynamics (CFD), with particular emphasis on compressible turbulent flows, shock wave/boundary layer interactions, and exascale computing. He leads the development of the STREAmS solver, a high-performance compressible flow solver optimized for exascale architectures. His work addresses challenges in aerospace engineering, including planetary entry systems, cryogenic tank pressurization, and rocket nozzle dynamics. Key research areas include direct numerical simulation (DNS) and large eddy simulation (LES) of supersonic/hypersonic flows, aeroacoustics, and flow control using microramps and surface modifications. His numerical methods are applied to study phenomena such as liquid sloshing in spacecraft, aeroelastic interactions in parachutes, and thermal effects in boundary layers. He collaborates on projects involving solid rocket motors, wind turbine aeroelasticity, and HPC optimization for exascale platforms. Publications from 2023–2025 highlight advancements in exascale-ready CFD, cryogenic system modeling, and turbulence characterization under extreme conditions. His work bridges fundamental fluid dynamics research with aerospace engineering applications, emphasizing high-performance computing and multiphysics simulation.

• Optimization Techniques
• Machine Learning
• High-Performance Computing (HPC)
• +3 more

Andrea Borghesi is an Assistant Professor (tenure-track) at the Department of Computer Science and Engineering (DISI), University of Bologna, Italy. His research focuses on optimization techniques, Machine Learning, and energy-efficient computing in High-Performance Computing (HPC) systems. He holds an M.S. (2013) and Ph.D. (2017) in Computer Engineering from the University of Bologna, and has been awarded the Italian National Scientific Qualification for Associate Professor in Computer Engineering (2018-2020). **Employment History**: 2020–Present: Executive Scientific Representative at ALMA-AI (Inter-departments Center for Artificial Intelligence, University of Bologna) 2019–Present: Assistant Professor (RTDA) at DISI, University of Bologna 2018–2019: Postdoctoral Fellow at DISI 2017–2018: Research Intern at University of Bologna’s Department of Electrical, Electronic and Information Engineering **Research Interests**: Borghesi’s work addresses complex systems, predictive maintenance, and energy efficiency. Key areas include HPC scheduling, AI-driven monitoring, and interdisciplinary AI applications. His projects span energy-aware scheduling, federated learning for anomaly detection, and AI fairness in healthcare and education. **Grants & Projects**: Lead or contributed to EU-funded initiatives such as AEQUITAS (AI fairness), StairwAI (AI accessibility), and OPRECOMP (transprecision computing), alongside national projects like vertOpt (AI application matching) and EXADATA (exascale monitoring). **Labs & Collaborations**: Active in the Inter-departments Center for Artificial Intelligence (ALMA-AI) and collaborated with institutions like CINECA and industry partners such as ENI and YANMAR. His work integrates AI, optimization, and simulation to solve real-world challenges in computing and healthcare.

Andrea MentrelliView profile

Associate Professor

• Extended Thermodynamics
• Mathematical Physics
• Hyperbolic Equations
• +4 more

Andrea Mentrelli is an Associate Professor of Mathematical Physics at the Department of Mathematics, University of Bologna, and Scientific Coordinator of the Alma Mater Research Center on Applied Mathematics (AM²). He holds a MS in Nuclear Engineering and a PhD in Mechanics of Materials from the University of Bologna. His research focuses on Extended Thermodynamics, High-Performance Computing (HPC), and computational modeling of plasma and fluid dynamics. Key roles include leadership in projects such as PRIN2022 (Mathematics of Nonlinear Wave Propagation) and Combustion-Wave Interactions. He is affiliated with INFN Bologna Section and AM². Notable contributions include shock wave analysis, hyperbolic models for incompressible fluids, and fractional calculus applications in viscoelasticity. His work combines theoretical rigor with numerical methods, addressing challenges in plasma physics, astrophysics, and materials science. **Education:** MS in Nuclear Engineering, PhD in Mechanics of Materials (University of Bologna). **Affiliations:** AM², INFN Section Bologna. **Research Projects:** Over 10+ grants including PRIN2022 and EU-funded initiatives. Extensive publications in journals like *Phys. Rev. D*, *Annals of Physics*, and *Journal of Computational Physics*.

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.

• High Performance Computing
• Embedded Systems
• Medical Imaging
• +4 more

Elisa Marenzi is an Assistant Professor at the University of Pavia's Department of Industrial and Information Engineering. She holds a PhD in Bioengineering and Bioinformatics (2014) and master's/bachelor's degrees in Biomedical Engineering from the same institution. Her research focuses on High Performance Computing (HPC), embedded systems for biomedical monitoring, signal processing in rehabilitation/automotive fields, and cerebellar neural circuit modeling. Education: PhD in Bioengineering (2014): Thesis on pressure ulcer monitoring systems Master's in Biomedical Engineering (2010): Thesis on capacitive sensor systems Bachelor's in Biomedical Engineering (2007): Thesis on medical guideline formalization Research Highlights: Her work spans HPC optimization for hyperspectral imaging, wearable medical devices, and neurocomputational modeling of cerebellar circuits. Notable contributions include: STRATUM project: AI-driven 3D decision support tools for neurosurgery HBP (Human Brain Project) simulations of hippocampal dynamics Hyperspectral imaging applications in skin cancer diagnosis FPGA-based real-time medical signal processing systems Awards: ETIC Award (2014) for PhD thesis Lifebility Award (2012) for social tech innovation NVIDIA GPU Research Center accreditation (2016) Professional Activities: She has held roles including Systems Administrator (2020-2023) and post-doctoral research in neurocomputation (2017-2019). Active in editorial roles for Microprocessors and Microsystems and Frontiers in Computational Neuroscience. Co-chair for ASHWPA special session at Euromicro DSD 2024. Labs/Teams: Key affiliations include the Neurocomputation Laboratory (DBBS) and collaboration with NVIDIA GPU Research Center.

Biagio CosenzaView profile

Associate Professor

• High-performance computing
• Compiler technology
• Software optimization
• +7 more

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++.