Hans-Arno Jacobsen is a Professor at Technische Universität München (Faculty of Computer Science, Germany) and the University of Toronto (Department of Electrical and Computer Engineering, Canada). His research spans distributed systems, blockchain technology, and machine learning for energy systems. Research Interests : Blockchain consensus algorithms, federated learning, graph neural networks, quantum computing applications, and energy-efficient distributed systems. Publication Trends : Recent work focuses on decentralized consensus in blockchains, energy-aware language model inferencing, quantum chemistry simulations, and graph neural network scalability. Collaborations : Regularly works with Ruben Mayer, Shashank Motepalli, and Gengrui Zhang on blockchain and machine learning projects.
Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.
Nane Kratzke is a Professor at Lübeck University of Applied Sciences, specializing in cloud computing and cloud-native applications. His research addresses practical challenges in container orchestration, cloud security, and vendor lock-in for small and medium enterprises. He holds a Diplom in Computer Science and a Doctorate in Natural Sciences, though specific institutions are not documented in available sources. Research interests include cloud-native architecture design, Kubernetes orchestration, moving target defenses for cloud security, and cost modeling of cloud services. His work bridges academic research and industry needs, particularly for SMEs seeking cloud portability through multi-cloud strategies and runtime transferability. Analysis of recent publications (2022-2024) reveals a strategic shift toward AI-driven cloud management techniques like prompt engineering, building on foundational contributions in cloud observability, security mechanisms, and transferability frameworks established between 2016-2021. Key recurring themes include mitigating vendor lock-in and enabling seamless application migration across cloud environments. No scientific awards are documented in the provided information sources. Details regarding graduate student advising, research grants, and laboratory facilities are not specified in current datasets, though his publications on programming assessment tools indicate engagement with computer science education.
Prof. Stefan Tai is a full professor and Chair of Information Systems Engineering at Technische Universität Berlin (Germany) since 2014. Previously, he held a full professorship at Karlsruhe Institute of Technology (KIT) and worked as a Research Staff Member at IBM's Thomas J. Watson Research Center. His research focuses on distributed systems, decentralized architectures, cloud service engineering, and privacy-preserving blockchain systems, emphasizing off-chain solutions and scalable technologies. Tai's work bridges academic research with industry applications, particularly in serverless computing, energy-efficient cloud-native systems, and blockchain-based solutions for supply chains and energy grids. He has authored numerous peer-reviewed publications in top-tier conferences and journals, including IEEE ICSA, Future Generation Computer Systems, and IEEE Blockchain. His contributions address challenges in software systems engineering, federated learning, and sustainable computing. Research Interests: Next-generation distributed software systems Decentralized architectures and blockchain systems Cloud and serverless computing Energy-efficient design Data management and privacy-preserving technologies Smart energy grids and IoT integration Key Publications Trends: Tai's recent work explores the intersection of blockchain and federated learning, serverless architectures for big data, and energy-efficient cloud-native applications. His 2025 paper introduced a framework for optimizing cloud-native energy efficiency, while 2024 contributions advanced verifiable decentralized systems and serverless data processing. Earlier research (2020–2021) focused on privacy in local energy grids and serverless computing scalability. Grants & Labs: While specific grants are not detailed, his leadership in TU Berlin's Information Systems Engineering group indicates involvement in EU-funded or industrial projects. Collaborations with institutions like TU Wien and KIT suggest multi-institutional efforts in distributed systems and blockchain. Labs/Teams: Leads the Information Systems Engineering research group at TU Berlin, focusing on software systems engineering, cloud architectures, and blockchain applications.
Qian Li is a researcher working at the intersection of database systems and operating systems, with primary affiliation at DBOS Inc. and academic connections to Stanford University's School of Engineering, Department of Computer Science, and Peking University in Beijing, China. The research focuses on developing the Database Operating System (DBOS) concept, which reimagines operating systems with database technology at their core. Research interests center on database systems, operating systems integration, transaction processing, and serverless computing. The work explores how database principles like ACID transactions can improve system reliability, debugging, and application development, particularly in cloud environments. Key projects include DBOS, Epoxy for cross-data store transactions, and Apiary for transactional serverless computing. The publication record shows a strong trend toward integrating database transaction semantics with modern computing paradigms, particularly serverless architectures. Recent work demonstrates how transactional guarantees can simplify application development, improve debugging, and enable new approaches to cloud-native application design. The research bridges theoretical database concepts with practical systems implementation. As a core contributor to the DBOS project, Qian Li has collaborated extensively with leading researchers including Michael Stonebraker, Matei Zaharia, Christos Kozyrakis, and Peter Kraft. The work has been published consistently in top-tier venues including VLDB, USENIX ATC, and CIDR, reflecting significant impact in the systems research community.
Peter G. Kropf is a Professor in the Department of Computer Science at the University of Neuchâtel, Switzerland, with a distinguished research career spanning over three decades. His academic journey reflects significant contributions to distributed systems, peer-to-peer networks, and cloud computing, with recent focus on IoT analytics and scientific computing applications. Dr. Kropf's research interests center on Distributed Systems , Peer-to-Peer Networks , Cloud Computing , Wireless Mesh Networks , and Scientific Workflows . His work demonstrates a clear evolution from foundational distributed systems research to practical applications in environmental monitoring, IoT analytics, and large-scale scientific computing. He has maintained consistent research productivity throughout his career, with publications appearing regularly from 1990 through 2024. Analysis of his recent publications reveals a strong trend toward real-time data processing for environmental applications, IoT analytics , and cloud-based scientific workflows . His work often bridges theoretical distributed systems concepts with practical implementations, particularly in environmental monitoring and resource management contexts. The interdisciplinary nature of his research connects computer science with environmental science and hydrology. Dr. Kropf has established long-term collaborations with researchers including Gilbert Babin (15 joint publications), Pascal Felber (14 publications), and Sabina Serbu (7 publications), forming a productive research network focused on distributed systems challenges. His work has appeared in prestigious venues including IEEE Internet Computing, Future Generation Computer Systems, and Middleware conference proceedings.
Prof. Dr. Uwe Breitenbücher is a Professor of System Architecture at Reutlingen University's Faculty of Informatics. He holds a Diplom in Computer Science (2011) and a PhD (2016) from the University of Stuttgart. His research focuses on cloud computing, IT systems management, IoT, blockchain, and pattern languages for software architecture. He also leads educational initiatives in software engineering pedagogy, including gamified e-learning platforms like IT-REX and Gamify-IT. Education: Diplom-Informatiker (2011), University of Stuttgart Dr. rer. nat. (2016), University of Stuttgart His research interests emphasize practical deployment solutions for distributed systems, including cross-component issue management, blockchain interoperability, and cloud orchestration using TOSCA standards. He actively develops tools like Variability4TOSCA and Dromi to address challenges in deployment variability and microservice architecture. His recent articles highlight trends in cross-chain smart contract invocations, gamified education systems, and orchestration of heterogeneous deployment technologies. He has contributed to over 50 peer-reviewed publications since 2018, focusing on cloud automation, blockchain integration, and software engineering education. He chairs the Bachelor's Examination Board for Digital Business and collaborates with industry on projects like the 5G-PreCiSe initiative. His work bridges academic research with practical deployment challenges in modern distributed systems.
Ofer Biran is a prominent computer science researcher at the Technion - Israel Institute of Technology, where he serves as a Professor in the Department of Computer Science within the Faculty of Electrical Engineering and Computer Science. With a research career spanning over three decades from 1988 to present, Biran has established himself as a leading expert in distributed systems and cloud computing. His work bridges theoretical foundations with practical systems implementation, evolving from early theoretical distributed computing research to contemporary cloud infrastructure and policy analytics systems. Biran's research interests focus on the critical challenges of modern computing infrastructure. His early work investigated fundamental theoretical aspects of distributed task solvability and round complexity in distributed systems. Over time, his research evolved toward practical systems challenges, particularly in cloud computing environments. His recent work addresses virtual machine placement optimization, network-aware resource allocation, heterogeneous resource reservation, and policy-driven cloud ecosystems. This progression demonstrates his ability to identify and solve increasingly complex problems as computing paradigms shifted from theoretical distributed systems to large-scale cloud infrastructure. An analysis of his publication trends reveals a clear evolution from theoretical computer science toward applied systems research. His recent publications (2016-2023) predominantly focus on cloud computing infrastructure, policy analytics, and data center networking, while maintaining strong theoretical foundations. Biran has made significant contributions to understanding how to optimize resource allocation in heterogeneous environments, particularly through network-aware VM placement strategies that balance performance, reliability, and efficiency requirements in modern cloud systems. Biran has maintained extensive collaborations throughout his career, working with researchers including Yosef Moatti, Dean H. Lorenz, Shlomo Moran, Shmuel Zaks, Erez Hadad, and Richard E. Harper. His role as co-editor of the 2023 SYSTOR conference proceedings in Haifa demonstrates his continued active participation and leadership in the systems research community. His research has consistently addressed practical challenges in computing infrastructure while maintaining strong theoretical rigor, making significant contributions to both academic understanding and real-world system design.
David Breitgand is a senior researcher at IBM Research specializing in cloud computing, networking, and virtualization technologies. With a publication record spanning from 1997 to 2025, he has established himself as a significant contributor to the fields of cloud infrastructure, network management, and distributed systems. His work shows a clear evolution from traditional network management protocols to modern cloud-native architectures, serverless computing, and edge-to-cloud integration. Dr. Breitgand's research interests focus on optimizing resource allocation in distributed systems, with particular emphasis on cloud-edge continuum architectures. His work addresses critical challenges in network function virtualization, service function chaining, and 5G media applications. He has made significant contributions to the understanding of how to efficiently deploy and manage services across heterogeneous cloud environments, from data centers to the network edge. His research combines theoretical foundations with practical implementations, often resulting in systems that have been evaluated in real-world settings. Analysis of his recent publications (2021-2025) reveals a strong focus on edge-to-cloud integration, with particular attention to 5G media applications, service function chaining in distributed environments, and serverless computing paradigms. His work demonstrates consistent innovation in developing algorithms and frameworks that optimize resource usage while meeting service level objectives. The research spans theoretical foundations, system design, and practical implementation, with publications appearing in top-tier venues like INFOCOM, SYSTOR, and IEEE journals. Dr. Breitgand has collaborated extensively with researchers including Danny Raz, Dean H. Lorenz, and Avi Weit, indicating long-term institutional relationships. His work has been instrumental in several European research initiatives, particularly those focused on 5G media applications and cloud networking. While specific awards aren't documented in the available information, his consistent publication record in high-impact venues and sustained research contributions over nearly three decades speak to his standing in the research community. His research has practical implications for the design and operation of modern cloud and edge infrastructure, with applications in media delivery, network function virtualization, and distributed service deployment. Dr. Breitgand continues to be an active contributor to the field, with ongoing research addressing emerging challenges in the convergence of networking and cloud technologies.
Mounir Bensalem is a Ph.D. candidate and research assistant at the Institute of Computer and Network Engineering within the Faculty of Electrical Engineering, Information Technology, and Physics at Technical University of Braunschweig, Germany. His research focuses on integrating machine learning and edge computing into next-generation network architectures, particularly in optimizing LoRa, analyzing reconfigurable intelligent surfaces (RIS), and applying reinforcement learning to edge serverless functions. He holds an Engineering Diploma and M.Sc. in Information Systems from the National Engineering School of Tunis. Education: Master’s Degree in Information System Techniques (2017), National Engineering School of Tunis. Engineering Diploma in Industrial Engineering (2017), National Engineering School of Tunis. Key Research Areas: Machine Learning, Edge Computing, 5G/6G Networks, Network Security, and Reconfigurable Intelligent Surfaces. Projects: EU Horizon MANOLO (2024–2026), H2020 FISHY (2020–2023), DFG FOR 2863 (2019–2022), and H2020 mF2C (2017–2020). Awards: Best Paper Award (2022) for work on IoT buffer size benchmarking. Lab/Team: Part of Prof. Jukan’s group at TU Braunschweig, focusing on communication networks and edge computing.
Jonathan Mace is a tenure-track faculty member heading the Cloud Software Systems Group at the Max Planck Institute for Software Systems and University of Saarland. His research develops systems for operating cloud infrastructure, distributed systems, and serverless architectures. Key projects include Blueprint for reconfigurable microservices, Hindsight for distributed tracing of edge cases, and Clockwork for predictable DNN serving. His work addresses observability, performance predictability, and resource management in cloud environments. Mace has received the Distinguished Artifact Award at OSDI, Facebook PhD Fellowship, and SOSP Best Paper Award. He teaches courses on distributed systems and cloud computing and has supervised multiple PhD students.
Ashwin Rao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI) within the Viterbi School of Engineering. With a research career spanning over two decades, his work bridges computer science, social sciences, and political science, focusing on understanding human behavior through digital footprints. His academic journey began with signal processing research in the 1990s before evolving into network protocols and mobile computing, and most recently into computational social science and AI ethics. Rao's research interests encompass Social Media Analysis, Online Political Polarization, Misinformation Detection, Network Protocols, Mobile Computing, Privacy in Mobile Applications, Natural Language Processing, and AI Ethics. His work demonstrates a consistent trajectory from technical networking research to the societal implications of technology. His most recent publications reveal a strong focus on understanding political discourse online, particularly examining polarization, emotional responses to events, and the impact of social media algorithms on information ecosystems. His interdisciplinary approach combines computational methods with social science theories to address pressing issues in digital society. Rao has published extensively across top venues including ICWSM, WWW, ACL, IEEE Transactions, and numerous conferences in networking and systems. His work often appears with Kristina Lerman, with whom he collaborates closely at USC ISI. His recent research portfolio shows a sophisticated integration of machine learning techniques with social science questions, particularly examining how language models reflect and potentially amplify societal biases. Rao has made significant contributions to understanding privacy issues in mobile applications, network protocols, and social media dynamics. His research has been influential in both technical communities studying network performance and social science communities examining online behavior. His work on BitTorrent performance, mobile privacy, and social media analysis has been widely cited across disciplines. His laboratory work appears to focus on computational social science methodologies, developing tools and frameworks for analyzing large-scale social media data while addressing ethical considerations in AI and data analysis. Recent projects suggest strong connections with public health research through social media analysis during the pandemic.
Malte Kurz is a researcher affiliated with the University of Hamburg Business School, specifically within the Department of Statistics. His primary role was as a Researcher under the Professorship of Statistics with Applications in Business Administration. He holds a PhD in Statistics from Ludwig-Maximilians-Universität München (2018), an M.Sc. in Statistics from LMU Munich (2013), and a B.Sc. in Mathematical Finance from Universität Konstanz (2011). His research interests focus on Machine Learning , Causal Inference , High-Dimensional Statistics , Financial Econometrics , and Dependence Modelling & Copulas . He has contributed to advancements in vine copula theory, distributed machine learning frameworks, and state space models. Key publications include works on vine copula simplifications (2019), low-dimensional Kalman smoothers (2018), and distributed double machine learning architectures (2021). He has developed influential software tools like the pacotest R package for copula hypothesis testing and the DoubleML framework for Python/R. Kurz has also contributed to open-source projects such as the VineCopulaMatlab toolbox and the SSMwLS MATLAB package for state space modeling. His work bridges statistical theory and computational implementation, with applications in finance and econometrics.
Christian Dietrich is a Professor for Reliable Distributed Systems at Technical University of Braunschweig since 2024, where he heads the Reliable System Software research group within the Institute of Operating Systems and Computer Networks, part of the Faculty of Electrical Engineering, Information Technology, and Physics. He previously held positions at Leibniz Universität Hannover where he completed his distinguished doctoral research. Professor Dietrich's research focuses on operating systems, distributed systems, and reliable systems with particular expertise in memory management, real-time systems, and embedded systems. His work spans both theoretical foundations and practical implementations, with significant contributions to system software reliability and efficiency. His research interests include operating system design, memory management techniques, real-time computing, embedded systems, fault tolerance, and compiler optimizations for system software. Dietrich's recent publications demonstrate a strong focus on memory management innovations (particularly for persistent memory), virtualization techniques, and reliability mechanisms for distributed systems. His work frequently bridges the gap between theoretical computer science and practical system implementation, with many contributions finding their way into production systems. The research shows increasing emphasis on hardware-software co-design and adapting operating systems to modern hardware capabilities. His scientific achievements have been recognized with numerous prestigious awards: USENIX ATC Distinguished Artifact Award (2023) for LLFree USENIX ATC Best Paper Award (2017) for cHash RTAS Best Paper Award (2015) for dOSEK Multiple Outstanding Paper Awards at ECRTS, OSPERT, and ISORC Wissenschaftspreis Hannover (2020, awarded in 2024) Professor Dietrich has secured significant research funding including multiple DFG projects (DI 2840/1-1, DI 2840/2-1) and has been involved in collaborative projects with industry partners. His teaching portfolio includes advanced courses on operating systems, programming languages, and compilers, for which he received teaching excellence awards. He leads an active research group focused on reliable system software with ongoing projects including ParPerOS (Parallel Persistency OS) and ATLAS (Adaptable Thread-Level Address Spaces). The Reliable System Software group maintains strong connections with both academic and industrial partners, contributing to open-source projects and collaborating on cutting-edge research in system software reliability. The group's work has practical impact, with some findings leading to more than 100 accepted patches in the Linux mainline kernel.
Dr. Balázs Sonkoly is a researcher specializing in edge computing, serverless applications, and 5G network optimization. His work focuses on latency-sensitive systems, multi-user augmented reality coordination, and cloud-native microservice management across distributed architectures. Key research themes include network function virtualization (NFV), software-defined networking (SDN), and programmable packet-optical networks Recent publications analyze AI-driven autoscaling, P4-assisted serverless migration, and multi-domain service orchestration Article trends reveal expertise in mixed reality frameworks, federated learning for vehicular networks, and edge-cloud convergence. His collaborations span institutions like IEEE, SIGCOMM, and the UNIFY project. Contributions to conferences like NOMS, ISMAR, and GLOBECOM highlight practical implementations of edge cloud platforms, SLAM integration, and latency-constrained resource allocation.
