Corrado De Sio is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN) , Politecnico di Torino , affiliated with the College of Computer, Film, and Mechatronics Engineering . His academic roles include course instruction and collaboration for Reconfigurable Computing , High Performance Computing (HPC) , and Operating Systems for High-Performance Supercomputers across multiple academic years (2020-2025). Research Interests focus on: Reliability of reconfigurable systems and FPGAs under radiation effects Hardware-software co-design for fault tolerance Embedded systems in aerospace and safety-critical applications Machine learning acceleration on reconfigurable hardware Radiation effects on real-time operating systems and CNN implementations Recent publications address: 2025: Selective hardening of RISCV soft-processors for space applications 2025: Real-time 'signal for help' gesture recognition systems 2024: Reliability analysis of RISC-V processors and CNN placement algorithms 2023: Fault tolerance in FPGA-based CNNs and radiation effects on RTOS Patents include: PyXEL - Python Toolkit for Reconfigurable Hardware Surveillance Software for Real-Time Violence Detection Academic Supervision : Co-supervisor for PhD candidate Arash Amini Bardpareh in Computer and Systems Engineering .
Luca Iocchi is a Full Professor at Sapienza University of Rome, where he teaches in the Master in Artificial Intelligence and Robotics program. He is affiliated with the Department of Computer, Control, and Management Engineering and the Faculty of Engineering of Information, Computer Science and Statistics. Iocchi serves as an Associate Editor for Artificial Intelligence Journal and has been the scientific coordinator of Spoke of PNRR project FAIR (Future AI Research). His educational background includes: Master in Engineering in Computer Science (Laurea in Ingegneria Informatica) cum Laude, Sapienza Università di Roma, 1995 PhD in Engineering in Computer Science (Dottorato in Ingegneria Informatica), Sapienza Università di Roma, 1999 Professor Iocchi's research focuses on cognitive robotics, task planning, multi-robot coordination, robot perception, robot learning, human-robot interaction, and social robotics. His work has significant applications in security, surveillance, and environmental monitoring. He has published over 200 referred papers with an h-index of 46 (Google Scholar). His research bridges theoretical AI with practical robotic systems operating in real-world environments, with a particular emphasis on developing intelligent systems that can interact effectively with humans. His recent publications show a strong trend toward multi-agent reinforcement learning, trust modeling in human-AI teams, UAV coordination, and planning systems. There's a clear focus on making robotic systems more reliable, efficient, and capable of operating in complex real-world scenarios like healthcare facilities and smart cities. His work increasingly integrates formal planning approaches with machine learning techniques. Professor Iocchi has received numerous scientific awards: 1999 Top Paper Award WebNet'99 2006 Best Paper Award RoboCup 2006 2008 Best Robotics Demo Award AAMAS 2008 2014 Best Paper Award For Engineering Contribution RoboCup 2014 2017 RoboCup@Home SSPL 2017 - 3rd place 2018 Canada-Italy Innovation Award 2019 Best Paper Award For Engineering Contribution RoboCup 2019 As an academic advisor, Iocchi has directed the PhD Program in Engineering in Computer Science from 2020 to 2023. He has been Principal Investigator for numerous research projects including SciRoc (European Robotics League), AI4EU (European AI project), BUBBLES, AIPlan4EU, ROSITA, Trust Your Agents, and FAIR. His research has been supported by EU H2020 programs, national grants, and industry collaborations, demonstrating strong connections between academia and practical applications. Professor Iocchi is actively involved with the Cognitive Cooperating Robots Lab (LabRoCoCo) and is a key member of the RoboCup Federation, having served as Vice-President from 2019 to 2024. He has played a significant role in benchmarking domestic service robots through RoboCup@Home and the European Robotics League Service Robots (ERL-SR), which he helped establish. His leadership in organizing international scientific robot competitions has been instrumental in advancing the field of service robotics.
Ana Maria Alonso Rodriguez is a Full Professor of Numerical Analysis at the Department of Mathematics, University of Trento. She holds a PhD in Applied Mathematics from Universidad Complutense de Madrid (1993) and has held academic positions across Italy and Spain since 1990. Her research focuses on numerical methods for partial differential equations, computational electromagnetism, finite element methods, and domain decomposition techniques. She has organized international workshops and minisymposia, including the 2022 Oberwolfach workshop on Hilbert Complexes and the 2018 ICOSAHOM conference session on high-order methods. Her work bridges numerical analysis, topology, and applied electromagnetism, with recent contributions to Whitney finite elements and discrete potential theory. Education: PhD in Applied Mathematics, Universidad Complutense de Madrid (1988-1993) Licenciatura en Ciencias Matematicas, same institution (1982-1987) Research emphasizes high-order discretizations for electromagnetic problems, leveraging finite element exterior calculus and graph-based decomposition techniques. Recent work (2024) advances tree-cotree methods for curl operator spectra and Whitney form interpolation. She actively collaborates with international institutions like the CI2MA in Chile and the Laboratoire J. A. Dieudonné in France. Teaching includes courses on numerical PDEs, finite elements, computational electromagnetism, and MATLAB-based numerical analysis at both undergraduate and PhD levels. She has supervised numerous courses in Italy and Spain since 2000, integrating practical software tools like FreeFem and MODULEF into instruction.
Alessia Ferrari is a fixed-term researcher in the Department of Engineering and Architecture at the University of Parma, Italy. She lectures on Hydrology within the Bachelor’s degree programme in Civil and Environmental Engineering and serves as the reference teacher for the same programme across multiple academic years (2020/2021 – 2025/2026). Research Focus Ferrari’s research integrates advanced numerical modelling with real-world flood-risk management. Key themes include: High-resolution 2-D shallow-water simulations using GPU-parallel codes. Porosity-based approaches for large-scale urban flood modelling. Levee-breach hydraulics and emergency-action planning. Calibration of hydraulic models using tools such as PEST. Integration of machine-learning techniques with physics-based flood forecasting. Publication Trends Across more than 25 peer-reviewed works (2015-2025), Ferrari has concentrated on computational hydraulics applied to extreme flood events in Northern Italy (e.g., Parma 2014, Lamone 2024). Her papers consistently advance numerical schemes (ADER, HLLEM Riemann solvers) and GPU acceleration while validating models against field data, thereby bridging theoretical development and practical flood-mitigation strategies. Contact & Office E-mail: alessia.ferrari@unipr.it Office: Science and Technology Campus – Pavilion 10, Engineering Scientific Headquarters, Parco Area delle Scienze 181/A, 43124 Parma, Italy.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Luciano Lavagno is a Full Professor at the Department of Electronics and Telecommunications, Polytechnic University of Turin, with over two decades of academic and research contributions. His work bridges hardware acceleration, low-power electronics, and intelligent system design. Research Focus: Hardware-accelerated machine learning, high-level synthesis (HLS) for FPGA/ASIC, heterogeneous CPU/GPU/FPGA platforms Key Projects: SPACE (predictable acceleration), REBECCA (secure AI acceleration), HPC-National Center (quantum computing), and oral history preservation via "Ti racconto una storia" initiative His recent publications analyze CNN inference optimization, subgraph isomorphism, and superword-level parallelism exploitation. Lavagno supervises multiple PhD students working on FPGA acceleration, neural network hardware, and embedded systems. As Principal Investigator for national and EU-funded projects (PRIN, JTI-ECSEL, PNRR), he drives advancements in sustainable computing infrastructure. His patented technologies include MIx&Latch timing methodology, capacitive sensing innovations, and 5G acceleration frameworks.
Guido Perboli is a Full Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he also serves as Logistics Coordinator and Project Coordinator for activities supporting relationships with government bodies. He is a member of the Interdepartmental Center CARS@PoliTO (Center for Automotive Research and Sustainable Mobility) and serves as Director of the ICT for City Logistics and Enterprises (ICElab@Polito) research center, which he founded in 2016. His research interests span a broad range of topics including Operations Research, Logistics, Last-mile Delivery, Sustainable Logistics, Combinatorial Optimization, Stochastic Programming, Business Development, and Lean Business methodologies. His work particularly focuses on City Logistics, Green Logistics, and the application of emerging technologies like Blockchain and AI in supply chain management. He has developed GUEST, a Lean Business methodology for innovation processes from early idea definition to implementation. Professor Perboli's recent publications demonstrate a strong focus on urban logistics, last-mile delivery optimization, blockchain applications in supply chains, and the integration of AI techniques in transportation systems. His work shows an increasing trend toward interdisciplinary research that combines optimization methods with emerging technologies to address sustainable urban mobility challenges. Professional Recognition: CASE Best Paper award from IEEE Conference on Automation Science and Engineering (2011) Effective member of INFORMS (2019-present) Effective member of EURO (1995-present) Effective member of AIRO (1995-present) Associate Editor for Journal of Applied Research and Technology (2020-present) Associate Editor for Sustainability (2018-present) Professor Perboli actively advises PhD students and has supervised numerous research projects, including EU-funded initiatives like SINFONICA, HESTER, and 5G-LOGINNOV. He serves as Scientific Director for multiple commercial research projects focused on blockchain, IoT, and AI applications in logistics. Beyond academia, he is Chief Scientific Officer of Arisk S.p.A., a fintech company specializing in business crisis prediction using AI and machine learning. His research group, ICElab@Polito, focuses on two main areas supporting urban growth: logistics and enterprises. The center collaborates with numerous companies including Amazon, DHL, and FCA, addressing real-world challenges in urban logistics and supply chain management through innovative research approaches.
Giovanna Turvani is an Associate Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino, with affiliations in both the College of Electronic, Telecommunications and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Scientific Branch: IINF-01/A - Electronics ERC Sectors: PE7_4, PE7_11, PE6_1, PE6_14, PE7_3 SDG Goals: Quality Education, Gender Equality, Affordable Energy, Industry Innovation Her research focuses on advanced electronics and quantum technologies, including: Logic-in-memory computing Quantum computing architectures Microwave imaging for medical and agricultural applications CAD tools for emerging nanotechnologies Embedded systems for bee health monitoring IoT solutions for bio-waste valorization Publications show strong expertise in quantum computing, nanocomputing, and microwave imaging, with recent trends emphasizing quantum optimization frameworks, in-memory architectures, and IoT-based agricultural technologies. She supervises PhD students in areas like quantum machine learning algorithms, predictive on-board systems, and quantum hardware design. Collaborations span multiple disciplines, including medical device development and agricultural electronics. Patents include innovations in microwave imaging, racetrack memory logic functions, and in-memory computing devices.
Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.
Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.
Nicola Capodieci is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia, specializing in Information Processing Systems (IINF-05/A). He actively teaches multiple courses including Object-Oriented Programming, Web Technologies, and General Computer Science across Computer Science and Mathematics degree programs. His research interests focus on GPU acceleration for embedded systems, autonomous vehicles, and real-time computing. Dr. Capodieci's work addresses critical challenges in heterogeneous computing platforms, particularly for automotive applications and smart city infrastructure. His research bridges theoretical computer science with practical applications in autonomous driving and urban mobility systems. Analysis of his recent publications reveals a strong focus on optimizing GPU performance for latency-sensitive applications, particularly in autonomous vehicles. His work spans path planning algorithms, memory interference management, and real-time scheduling on heterogeneous platforms. A significant portion of his research addresses practical implementation challenges in embedded systems where computational resources are constrained but timing predictability is critical. Dr. Capodieci's teaching portfolio demonstrates expertise in both foundational programming concepts and advanced topics in web technologies. His courses emphasize practical implementation skills while covering theoretical foundations of object-oriented programming, web development frameworks, and computational thinking.
Luca Sterpone is a Full Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He serves as Head of the Control and Computer Engineering Department (2023-2027), coordinates the Aerospace and Safety Computing Lab, and is a member of the Academic Senate and Power Electronics Innovation Center (PEIC). His research spans reconfigurable computing, fault tolerance, and radiation effects analysis in electronic systems. Professor since 2021 Department Head (DAUIN) since 2023 Coordinates international collaborations with ESA, AMD Xilinx, NVIDIA, and Thales Alenia Space Develops radiation-hardened FPGA tools (SETA, VERI-Place, PyXEL) 2007 EDAA Outstanding Dissertation Award and 2005 IEEE Best Paper Award Research Focus : Designing radiation-tolerant systems for aerospace, including fault-tolerant AI accelerators, FPGA reliability, and software-based error mitigation. He investigates soft error propagation in nanoscale circuits and develops tools for radiation sensitivity analysis in VLSI. His work integrates hardware-software co-design for mission-critical applications. Awards : EDAA Outstanding Dissertation Award (2007) IEEE European Test Symposium Best Paper (2005) SMACD Best EDA Tool Award (2018) ARC Best Paper candidate (2018) Teaching : He leads courses in Reconfigurable Computing (PhD level), GPU Programming , and Operating Systems . He has formal responsibility for teaching roles across 9 bachelor's and 7 master's years, and mentors multiple PhD students. Collaborations : Coordinates with the European Space Agency (ESA), University of Bielefeld, Universidad de Sevilla, and industrial partners like AMD Xilinx, NVIDIA, and General Motors. He leads projects such as RESCHIP4EU, VEGAS, and TERRAC for radiation-hardened computing solutions.
Prof. Zhong-Zhi Bai is a Professor of Computational Mathematics at the Institute of Computational Mathematics and Scientific/Engineering Computing, part of the Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing. He is based at the State Key Laboratory of Scientific/Engineering Computing, a leading institution in applied mathematics and high-performance computing in China. His research spans a broad range of topics in computational mathematics, with a strong emphasis on numerical linear and nonlinear algebra , parallel computations , numerical optimization , and sparse matrix techniques . He has made significant contributions to the development and analysis of preconditioners and numerical methods for differential-algebraic equations and partial differential equations . His work is foundational in advancing iterative solvers and large-scale scientific simulations. The available publications, including the notable book Milestones in Matrix Computation (2007), reflect a long-standing engagement with matrix computation and numerical algorithms. The research trends indicate a deep focus on theoretical and practical aspects of numerical methods, particularly in the context of high-performance and scientific computing environments. Prof. Bai serves on the editorial boards of several leading journals in the field, including: Applied Numerical Mathematics Communications on Applied Mathematics and Computation Computational and Applied Mathematics East Asian Journal on Applied Mathematics Electronic Transactions on Numerical Analysis Journal of Computational and Applied Mathematics Journal of Mathematical Study Journal of Scientific Computing Numerical Algorithms Numerical Linear Algebra with Applications He has advised students and mentored researchers, though specific names are not listed. His work is supported by institutional affiliations and likely national research grants, given his position in a State Key Laboratory. He has developed software tools and delivered lecture series, contributing to both research and education in computational mathematics. Prof. Bai is actively involved in the numerical linear algebra community and maintains a comprehensive academic presence, including publications, editorial service, and software contributions. He leads or participates in research teams focused on advancing computational methods within the State Key Laboratory framework.
Elisa Capello is a Full Professor of Flight Mechanics at Politecnico di Torino, Department of Mechanical and Aerospace Engineering (DIMEAS). She serves as Contact person for internationalization of innovation and technology transfer, Member of the Interdepartmental Center PIC4SeR (PoliTO Interdepartmental Center for Service Robotics), and Deputy Coordinator of the Doctoral College in Aerospace Engineering. With over 100 publications, her work spans aerospace engineering, control systems, and robotics. Her research focuses on flexible spacecraft, flight control systems, robotic systems, and unmanned aerial vehicles. She designs guidance, control and navigation systems for aircraft and spacecraft, develops control systems for wind turbines and wind farms, studies flight mechanics of fixed and rotary wing aircraft, tests unmanned aerial systems, and plans mission control for autonomous systems. Her work bridges theoretical control systems with practical aerospace applications, with strong emphasis on experimental validation. Her recent publications demonstrate a strong focus on advanced control techniques for aerospace applications, particularly in UAV control, spacecraft formation flying, and robotic systems. There's a clear trend toward integrating machine learning with traditional control methods, as seen in transformer-based MPC and multimodal learning approaches. Her work spans theoretical development, simulation, and experimental validation across multiple platforms. Member of the Editorial Board of IEEE Control Systems Society (2019-) Member of the Scientific Committee - IEEE Technical Committee Aerospace Control (2016-) International FAI Judge for Helicopter Championship (2009-2015) Professor Capello supervises numerous PhD students working on topics including autonomous aerial vehicles, path planning, risk analysis, spacecraft dynamics, and control. She leads multiple research projects including CREATEFORUAS (2019-2022), Assessment of drag free control systems for L3 gravity wave observatory (2018-2019), and Guidance, Navigation and Control algorithms for in-Orbit servicing (2020-2021). Her international collaborations include institutions in the USA, Japan, and Germany. She is actively involved with the Flight Dynamics, Control and Simulation research group at DIMEAS and the DRAFT (DRones Autonomous Flight Team) at PoliTo, where she mentors students in developing autonomous flight capabilities for various applications.
Paolo Trunfio is a Professor of Computer Engineering at the University of Calabria, Italy, and co-founder of DtoK Lab S.r.l., an academic spin-off focused on data analysis and distributed systems. He holds a Ph.D. and is affiliated with the DIMES Department, specializing in big data, cloud computing, and high-performance computing (HPC). His research emphasizes scalable data analysis frameworks, edge-cloud continuum solutions, and machine learning applications for social media and disaster monitoring. Trunfio serves as an Associate Editor for ACM Computing Surveys and Journal of Big Data , and is on the editorial boards of several journals including Future Generation Computer Systems . He has authored four influential books, including Programming Big Data Applications (2024) and Data Analysis in the Cloud (2015). His work spans distributed systems, IoT-based smart objects, and exascale computing. Notable projects include the EU-funded eFlows4HPC and ASPIDE initiatives, which focus on HPC workflows and exascale programming models. Trunfio’s publications (over 200 papers) address topics like social media analytics, energy-efficient P2P networks, and parallel data mining. He leads research in urgent computing for disaster response, edge-cloud integration for urban mobility, and AI-driven data analysis. His contributions to cloud frameworks (e.g., JS4Cloud, ParSoDA) and HPC libraries (e.g., DCEx) highlight his expertise in bridging theory and practice in distributed computing ecosystems.
