Rob Frampton is a Lecturer in Software Engineering at Manchester Metropolitan University's Computing and Mathematics department. He splits his time between academic teaching and industry work, focusing on modern software engineering practices such as serverless architecture and infrastructure as code. Recently, he has explored large language model (LLM) prompt engineering and integration of AI into business applications. Affiliation: Computing and Mathematics Department, Manchester Metropolitan University Location: John Dalton Building His research interests bridge academia and industry, emphasizing practical applications of cutting-edge technologies. While no specific publications are listed, his work reflects current trends in cloud-native development and AI-driven solutions.
MEKKI Mohamed is a Research Fellow at EURECOM's Communication Systems department, focusing on cutting-edge research in networking, cloud computing, and artificial intelligence. His work emphasizes zero-touch network management, AI-driven infrastructure optimization, and edge-cloud continuum systems. Mekki holds a PhD in 'Enabling Zero-Touch Cloud Edge Computing Continuum Management' (2024). His research spans network slicing, microservices architecture, and 6G technologies. He has published extensively in top-tier journals and conferences, addressing challenges in distributed systems, resource management, and trustworthy network automation. Mekki collaborates with industry leaders to develop scalable frameworks for 5G/6G testing and vertical service deployment. Key contributions include XAI integration for network management, proactive lifecycle management for microservices, and performance analysis of cloud-native configurations. His work is supported by collaborations with EURECOM's research teams in Digital Security and Data Science domains.
Maciej Malawski is an Associate Professor at the Department of Computer Science, AGH University of Science and Technology, and Research Team Leader at the Sano Centre for Computational Medicine in Kraków, Poland. He holds a PhD in Computer Science (2009) and MSc degrees in Computer Science (2001) and Physics (2004). His research focuses on parallel/distributed computing, cloud technologies, and biomedical applications, with contributions to federated learning, serverless computing, and scientific workflow optimization. Education: PhD in Computer Science, AGH University (2009) MSc in Physics, Jagiellonian University (2004) MSc in Computer Science, AGH University (2001) Research Interests: Parallel computing, cloud infrastructures, serverless systems, federated learning, biomedical data analysis, and workflow scheduling. His work bridges computational methods with healthcare, such as federated learning for medical imaging and sensitivity analysis in cardiovascular models. Publications & Awards: Over 50 international publications, including top-tier venues like SC, IPDPS, and IEEE Cluster. Recognitions include the 2018 AGH Rector’s Award, Publons Peer Review Award (2018), and 1st place in the Executable Paper Grand Challenge (2011). Labs & Affiliations: Director of Sano, senior researcher at ACC Cyfronet AGH, and collaborator with CERN. Leads projects on HPC/Cloud integration and scientific computing for medicine.
Bartosz Baliś serves as Associate Professor at the Institute of Computer Science, AGH University of Science and Technology, and holds a Senior Postdoctoral position in Extreme-scale Data and Computing at the Sano Centre for Computational Medicine. He actively contributes to the CERN ALICE experiment as a core team member. His educational foundation includes dual degrees from AGH University and Jagiellonian University, culminating in both PhD and DSc (habilitation) qualifications in Computer Science from AGH University. These advanced credentials underpin his specialized expertise in computational methodologies. Dr. Baliś pioneers research in scientific workflows, data science, and distributed computing systems , with particular emphasis on cloud-native architectures and e-Science infrastructures. His innovative work bridges theoretical computer science with practical applications in large-scale data processing, advancing methodologies for scientific computing environments through novel workflow management solutions. Recent publications demonstrate a clear trajectory toward serverless computing paradigms and comprehensive observability frameworks for scientific applications, reflecting his commitment to solving scalability challenges in modern computational research through cutting-edge cloud technologies. He has secured substantial research funding through participation in major European initiatives including CrossGrid, CoreGRID, K-Wf Grid, ViroLab, Gredia, UrbanFlood, PaaSage, and WATERLINE across FP5-FP7 and Horizon 2020 programs. His leadership extends to conference organization as Euro-Par 2020 General Co-chair, HPCS 2018-19 Tutorials Co-chair, and IEEE/ACM SC committee roles. Within institutional frameworks, Dr. Baliś contributes to the Extreme-scale Data and Computing research group at Sano Centre while maintaining active collaboration with the international CERN ALICE collaboration, where his computational expertise supports high-energy physics research infrastructure.
Antoine Kaufmann is a Tenure-Track Faculty Member (W2) at the Max Planck Institute for Software Systems (MPI-SWS) and serves as Adjunct Faculty at TU Munich's Chair of Distributed Systems and Operating Systems. He leads the operating systems research group focusing on post-Moore systems – specialized architectures integrating hardware and software components. Education includes a PhD from University of Washington's Allen School of Computer Science and Engineering, and Bachelor/Master degrees from ETH Zürich. His work bridges systems research and practical implementations, resulting in publications at top-tier venues including SOSP, OSDI, and EuroSys. Research Interests: Centers on the interplay of software and hardware in specialized systems. Key areas include network communication architectures, machine learning systems, and methodologies for efficient post-Moore systems with manageable complexity through reusable components. Publications: Focus on network systems, operating systems, and distributed computing, with recent work on simulation frameworks (SplitSim), virtualized network stacks (Virtuoso), and reconfigurable packet processing (Kugelblitz). Awards: Distinguished Artifact Award for Clockwork at OSDI 2020. Teaching & Service: Developed courses on Operating Systems and Accelerating Applications with Specialized Hardware. Regularly serves on program committees for EuroSys, MobiSys, and other top conferences.
Paarijaat Aditya is a faculty member at the Max Planck Institute for Software Systems (MPI-SWS) , focusing on interdisciplinary research at the intersection of theoretical computer science and applied systems. Their work spans algorithms, programming languages, security, and distributed systems , with a particular emphasis on serverless computing, federated learning, and privacy-preserving urban sensing. Research Areas : Machine Learning Security (e.g., model hijacking, federated learning) Serverless Computing (e.g., GPU sharing, high-performance frameworks) Privacy Engineering (e.g., urban sensing, image capture, mobile compliance) Distributed Systems (e.g., NFV, peer-assisted content delivery) Their publications reflect a trajectory of advancing scalable AI training, secure collaborative systems, and privacy-compliant infrastructures. Recent works address challenges in public cloud adaptability, while earlier contributions explore mobile privacy risks and secure resource management.
Matthew Lentz is an Assistant Professor in the Department of Computer Science at Duke University, where he joined in Fall 2021. Prior to his faculty position at Duke, he was a Postdoctoral Researcher at VMware Research Group and continues to collaborate with them as an Affiliated Researcher. His academic journey includes a PhD in Computer Science from the University of Maryland, where he was advised by Bobby Bhattacharjee. Dr. Lentz's research focuses broadly at the intersection of systems, networking, and security. His current research directions include: (1) introducing new abstractions and tools for building secure, trustworthy software systems, and (2) building systems that improve performance for modern networking and machine learning applications. His work spans diverse areas including network performance measurement, security threat analytics, and privacy-preserving systems. His research often addresses practical challenges in real-world systems while maintaining strong theoretical foundations. As an educator, Dr. Lentz teaches courses including Operating Systems (CPS 510), Introduction to Operating Systems (CPS 310), and Secure Software Systems (CPS 585). He is actively involved in mentoring graduate students, currently advising three PhD candidates: Alexander Du (with Danyang Zhuo), Chenyang Liu (with Kartik Nayak), and Luka Duranovic. His recently graduated students include Yongji Wu (now a postdoc at UC Berkeley), Yalu Cai (at CertiK), Nathan Ostrowski (at Octane Security), and Andres Montoya-Aristizabal (at Two Sigma). Dr. Lentz has served on numerous program committees for top-tier conferences including IEEE S&P (2024-2026), MobiSys (2023-2024), MobiCom (2023-2024), CCS (2021-2022), EuroSys (2021), and HotMobile (2021). He also served as Proceedings Chair for SOSP 2023, one of the most prestigious conferences in systems research.
Patrizia Scandurra is a Professor at the University of Bergamo, Italy. She has held significant roles including Co-chair of the Doctoral Symposium-track at ECSA 2020, Program Co-Chair for multiple conferences, and is a member of steering and organizing committees for various international software engineering events. Her research focuses on Software Architecture, Formal Methods, Self-adaptive Systems, and Cyber-Physical Systems. She has contributed to frameworks like ASMETA and RAMSES, emphasizing rigorous system design and safety assurance. Her work spans formal specification, model-based testing under uncertainty, and adaptive systems for diverse applications such as automotive systems, medical devices, and smart cities. She has published extensively on topics like self-adaptation under model uncertainty, microservices resilience, and anomaly detection in urban infrastructure. Patrizia’s contributions include conference organization across ECSA, ASE, ICSE, and SEAMS, reflecting her leadership in advancing software architecture theory and practice. Her research bridges formal methods with real-world applications, addressing challenges in autonomous systems and digital twin integration.
Stefan Niederberger is a Senior Researcher at the Institute of Electrical Engineering IET iHomeLab, part of the Lucerne School of Engineering and Architecture at Lucerne University of Applied Sciences and Arts (HSLU). His research focuses on AI-driven systems for smart environments, trustworthy AI, and hybrid modeling. He holds a Doctorat d’Automatique from Université de Haute-Alsace and advanced degrees in Mechatronics and Automation from Fachhochschule Nordwestschweiz. Education: Doctorat d’Automatique, Université de Haute-Alsace (2012) Master of Science in Mechatronics & Automation, FHNW (2015) Bachelor of Science in Industrial Automation, FHNW (2012) CAS in DevOps Leadership and Agile Methods (2021) CAS in Cloud and Platform Management (2021) His research projects include KI im Gebäude (AI for smart buildings), RecoveryFun (AI-driven tele-rehabilitation), and CleverGuard (contextual anomaly detection for elderly care). He specializes in digital twin architectures, explainable AI, reinforcement learning, and cloud-native systems . Professional competencies span Agile methodologies, DevOps, and full-stack development. Key research themes: Hybrid modeling for real-time optimization, trustworthy AI systems, and scalable digital twin platforms. His work bridges academic research with industrial applications in energy systems, healthcare, and automation. Advising and grants: While no formal advisees are listed, his roles in multi-year research projects (e.g., PRECARRHD, Smart Maintenance 4.0) imply collaborative research leadership. Active in grant-funded initiatives focusing on AI integration in infrastructure and healthcare. Labs/Teams: Head of the iHomeLab’s AI for Smart Environments division, collaborating with industry partners like Belimed AG and academic networks in mechatronics and control systems.
Filipe Augusto da Luz Lemos is a Courtesy Research Professor at Syracuse University's Department of Forensic Science within the College of Arts and Sciences. His research bridges forensic science, electronic engineering, and artificial intelligence to address security and operational challenges in distributed systems, critical infrastructure, and industrial environments. Educational Background Ph.D. in Electrical Engineering and Industrial Informatics (Federal University of Technology Paraná, Brazil) M.S. in Forensic Science (Syracuse University, USA) B.S. in Industrial Electrical Engineering with an emphasis in Electronics and Telecommunications (Federal University of Technology Paraná) Specialization in Safety Engineering (Federal University of Technology Paraná) Research Interests Dr. Lemos focuses on digital forensics (including network security, memory analysis, and evidence processing), cybersecurity (threat detection in critical infrastructure and SDN security), and forensic linguistics (authorship attribution and cybercrime language analysis). He also develops forensic education programs integrating AI tools and real-world case studies. In technical domains, his work spans DevOps/SRE (Infrastructure as Code, CI/CD automation), cloud-native architectures , and IoT-based industrial telemetry , emphasizing predictive maintenance and anomaly detection in mining/petroleum/gas sectors. Advising & Grants No formal advisees or grant information is listed in the provided text. His contributions emphasize curriculum development and hands-on training methodologies in forensic science. Labs/Teams His work is associated with interdisciplinary teams focused on cloud infrastructure resilience, forensic AI tool development, and industrial sensor network optimization, though specific lab names are not mentioned.
Dr. Yazhuo Zhang is a researcher at the Department of Computer Science, ETH Zürich, focusing on distributed systems, caching algorithms, and cloud infrastructure. Their work addresses core challenges in resource elasticity and web performance optimization. Key research contributions include: Advancing cache eviction algorithms beyond traditional LRU Developing causal models for latency analysis in distributed systems Improving elasticity in cloud-native environments Recent publications explore cache optimization using FIFO queues (2023), causal latency modeling (2023), and cloud-native resource management (2025).
Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.
Marios Kogias is an Assistant Professor in the Department of Computing at Imperial College London, conducting research at the intersection of operating systems, networking, and distributed systems with a focus on tail-tolerance, fault-tolerance, and confidentiality using emerging datacenter hardware. He holds a PhD in Computer Science from EPFL (2020) supervised by Ed Bugnion, where his thesis won the Dennis M. Ritchie Award and received an Honourable Mention for the Eurosys Roger Needham PhD Award. His undergraduate degree in Electrical and Computer Engineering is from NTUA. His research spans operating systems, networking, distributed systems, cloud computing, and confidential computing, with significant contributions to microsecond-scale computing, serverless architectures, and eBPF applications. Current work emphasizes tail-latency optimization in datacenter environments and confidential computing security. Recent publications reveal strong trends in microsecond-scale systems, confidential computing, and serverless workflows, demonstrating expertise in leveraging hardware innovations for performance and security guarantees across distributed environments. His scientific awards include: Dennis M. Ritchie Award (2021) Honourable Mention for the Eurosys Roger Needham PhD Award (2021) IBM Fellowship Microsoft Swiss JRC Fellowship Distinguished Artifact Award (ASPLOS 2021) Best Student Paper Award (Eurosys 2020) Dr. Kogias actively mentors PhD students including Konstantinos Prasopoulos (co-advised with Ed Bugnion), Scofield Liu, Farzad Mohammadi, and Adel Sefiane. He teaches Networked Systems, Computer Networks and Distributed Systems, and Object-Oriented Design at Imperial, supported by fellowships from IBM and Microsoft during his doctoral research. His collaborative work spans EPFL, Microsoft Research Cambridge, and Imperial College London, focusing on systems architecture for next-generation datacenter applications.
Youki Kadobayashi is a Professor at NAIST, specializing in cybersecurity and network engineering. He holds a Ph.D. in Computer Science from Osaka University. His research focuses on Internet infrastructure evolution, cybersecurity education, and standards development. He collaborates with industry and academia on projects like creating technologies for failure recovery and infrastructure co-creation. Research interests include: Internet engineering, web security, cybersecurity education standards, and privacy-preserving systems. His work addresses challenges in smart home security, federated learning for fraud detection, and secure edge computing. Publications span topics like differential privacy, federated learning frameworks, and authentication protocols. He contributes to consortia such as WIDE and organizes competitions like CDMC and Hardening Zero. His lab explores advanced cybersecurity tools and datasets.
Matteo Trentin is a PhD student at the Department of Mathematics and Computer Science (University of Southern Denmark), focusing on Artificial Intelligence , Cybersecurity , and Programming Languages . His research intersects Serverless Computing , Resource Allocation , and Static Program Analysis , with a strong emphasis on Scheduling Policy and Domain-Specific Languages . Current affiliation: Department of Mathematics and Computer Science, University of Southern Denmark Research themes: Cloud systems, runtime environments, cost-aware scheduling, and formal methods Collaborations: International work with researchers in software architecture and formal verification His publications address topology-aware allocation , affinity-aware function scheduling , and formal specification of FaaS policies , often leveraging domain-specific languages and static analysis to optimize serverless platforms . Key trends include cloud resource management , operational semantics , and service platform engineering .
