Diala Naboulsi is a Professor at the École de technologie supérieure (ÉTS) in the Department of Software Engineering and IT. Her research focuses on mobile networks, wireless systems, and cybersecurity, with a strong emphasis on machine learning applications in network optimization. She holds an M.Eng. from the Lebanese University and M.Sc. and Ph.D. degrees from INSA Lyon. Research Units: Summit Tech Research Chair, LASI Lab, Imagin Lab Expertise: Network virtualization, resource allocation, mobility management, UAV-based computing Her work spans resilience in wireless backhaul networks, energy-efficient frameworks in RAN slicing, and federated learning for privacy-aware traffic forecasting. She has advised numerous doctoral students, including Ahmed Abdelmoaty, Hnin Pann Phyu, and Philippe Lavoie. Key contributions include deep reinforcement learning approaches for network topology optimization and UAV-assisted MEC systems for Industry 5.0. Recent publications highlight advancements in 6G networks, network slicing, and edge computing. Her research aligns with strategic initiatives in sustainable and secure communication systems.
Dr. Jie Wu is the Laura H. Carnell Professor and serves as Director of the Center for Networked Computing at Temple University's Department of Computer and Information Sciences. He has held leadership roles including Chair of the department (2009-2016), Associate Vice Provost for International Affairs (2015-2017), and Director of International Affairs in the College of Science and Technology. Previously, he was a distinguished professor at Florida Atlantic University (1989-2009) and held NSF program director roles (2007-2009). Education: PhD in Computer Engineering, Florida Atlantic University (1989) MSc in Computer Science, Shanghai University of Science and Technology (1985) BSc in Computer Science, Shanghai University of Science and Technology (1982) Research focuses on mobile/wireless networks, cloud computing, distributed systems, and cybersecurity. He has led NSF-funded projects and pioneered protocols for ad hoc networks. His work integrates machine learning with network trust mechanisms. Awards highlight his contributions: AAAS/IEEE Fellowships, ACM Distinguished Membership, and multiple best paper recognitions. He chairs major conferences like IEEE MASS and ICDCS, and serves on editorial boards for IEEE Transactions on Mobile Computing and others. Professional service includes leadership roles in IEEE Technical Committees, CCF (Chinese Computer Federation), and organizing over 20 international conferences. His lab at Temple University drives innovations in networked systems and distributed computing.
Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).
Feng Hao is a Professor of Security Engineering and Head of the Systems & Security research theme at the Department of Computer Science, University of Warwick. He holds a PhD from the University of Cambridge, supervised by Ross Anderson and John Daugman. His research focuses on real-world security problems, including cryptographic protocols, electronic voting systems, and IoT security. He has contributed to standards like ISO/IEC 11770-4 and RFC 8236 (J-PAKE), and his work has been recognized with grants such as the ERC Starting Grant (2012) and the ERC Proof of Concept Grant (2015). Education: PhD in Security Group (Computer Laboratory), University of Cambridge. Research interests include designing secure protocols for e-voting, password authentication, and privacy-preserving systems. Notable contributions include DRE-i, DRE-ip, and J-PAKE protocols. Professional service includes roles as a journal editor (IEEE Security & Privacy, Journal of Information Security and Applications), standards committee member (ISO/IEC), and grant reviewer (EPSRC, EU). Teaching includes advanced computer security and cryptography courses at both undergraduate and postgraduate levels. Awards and grants highlight his impactful work: top Google Scholar paper in Computer Security & Cryptography (2017), 3rd place in Economist Cybersecurity Challenge (2016), and significant ERC funding. His research team has conducted trials in e-voting systems, such as the Gateshead local elections (2019), demonstrating real-world application of his protocols.
Brian Mitchell is a Teaching Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He brings over two decades of combined industry and academic experience, transitioning fully into academia in 2022 after serving as a Distinguished Engineer at a Fortune 15 company. His work bridges cutting-edge research and practical innovation in software systems. Drexel University, College of Computing & Informatics, Department of Computer Science Education: PhD in Computer Science, Drexel University MS in Computer Science, Drexel University BS in Computer Science, Drexel University ME in Computer & Telecommunication Engineering, Widener University Brian Mitchell's research centers on the intersection of Software Engineering, Software Architecture, Cloud Native Computing, and AI . His early foundational work helped establish the field of Search-Based Software Engineering (SBSE) , particularly in automated software clustering and architecture recovery. Recently, his focus has shifted to modern challenges in cloud-native environments , including misconfiguration detection, malware analysis, and resilient system design. He integrates security, scalability, and intelligent automation into software engineering practices. His recent publications reflect a clear trend toward AI-enhanced cloud-native systems , emphasizing automated analysis, security, and architectural robustness. These works appear in AI and cloud computing venues, showing interdisciplinary engagement. The evolution from source code clustering to cloud-native engineering illustrates his adaptability and leadership in emerging domains. Scientific Awards: Best Paper Award, GECCO'03 Best Paper Award, WCRE'01 Brian is actively involved in mentoring students and encourages research collaboration, particularly with those seeking deeper engagement beyond coursework. He emphasizes hands-on learning and uses modern tools like GitHub and Discord in his teaching. While no specific grants are listed, his industry leadership in digital innovation and open-source contributions suggests strong applied research support. He previously led large engineering teams and drove disruptive technological adoption in enterprise settings. Though no formal lab name is mentioned, his research group appears focused on software architecture, cloud systems, and AI-driven engineering , likely operating under informal or course-based research initiatives. His website and GitHub presence (@ArchitectingSoftware) suggest an active, open, and collaborative environment for student research.
Schahram Dustdar is a Full Professor of Computer Science and head of the Distributed Systems Group at TU Wien (Vienna University of Technology), Austria. He has held significant international academic positions, including Honorary Professor at the University of Groningen (2004–2010) and Visiting Professor at the University of Seville (Dec 2016–Jan 2017) and UC Berkeley (Jan–Jun 2017). His research interests lie at the intersection of distributed computing, cloud services, and intelligent data systems. He actively contributes to advancing the fields of services computing, cloud infrastructure, web technologies, and data-driven financial modeling. His work emphasizes scalable, robust, and knowledge-aware systems, particularly in financial data visualization and transaction network analysis. The most recent publications highlight his focus on modeling financial transaction networks using constraint satisfaction and developing visualization frameworks that incorporate incremental domain knowledge. These works reflect a strong trend toward integrating formal methods with interactive data systems for enterprise and financial applications. ACM Distinguished Scientist (2009) IBM Faculty Award (2012) IEEE Fellow (2016) Elected Member of Academia Europaea Schahram Dustdar has supervised multiple research projects and leads a vibrant research group at TU Wien. He has been involved in editorial leadership as Editor-in-Chief of Computing (Springer) and Associate Editor for top-tier journals such as IEEE Transactions on Cloud Computing, IEEE Transactions on Services Computing, ACM Transactions on the Web, and ACM Transactions on Internet Technology. His editorial roles and international visiting positions indicate extensive collaboration and grant-related activities, though specific grants are not detailed in the text. He leads the Distributed Systems Group at TU Wien, a research team focused on building next-generation distributed computing platforms, cloud services, and intelligent data processing systems with real-world applications in finance, enterprise systems, and large-scale data analytics.
Maryam Mehri Dehnavi is an Associate Professor in the Department of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing and leads the ParaMathics research group. Research focuses on high-performance computing , machine learning , sparse matrix optimizations , and compiler design for heterogeneous systems. Her work develops domain-specific languages , scalable numerical libraries , and auto-vectorization techniques for cloud and GPU platforms. Recent publications address LLM compression , sparse code translation , GPU kernel synchronization , and control flow optimization . Scientific recognition: Ontario Early Researcher Award (2021), NSF CRII Grant, NSERC New Frontiers in Research Fund. Current students: Mushegh Shahinyan , Martin Phan , Maryam Haghifam , and others. Former advisees: Kazem Cheshmi (NJIT), Zachary Blanco (MIT Lincoln Lab), Yuanxi Li (Amazon).
Athinagoras Skiadopoulos is a computer systems researcher at Stanford University's School of Engineering, Department of Computer Science, focusing on the intersection of database systems and operating systems. His work centers around the innovative DBOS (Database-oriented Operating System) project and large-scale machine learning infrastructure, collaborating with prominent researchers including Christos Kozyrakis and Michael Stonebraker. His primary research interests include: Database-oriented Operating Systems (DBOS) Distributed systems for large-scale machine learning Resource management and optimization in data-intensive systems Transaction processing and data governance High-performance networking for accelerated computing Fault tolerance in distributed training systems Skiadopoulos's research trajectory shows a clear evolution from foundational DBOS architecture toward applications in large-scale machine learning systems. His early publications established the DBOS framework for operating system design using database principles, while his recent work addresses critical challenges in distributed training of massive neural networks. Systems like ReCycle and SlipStream demonstrate innovative approaches to pipeline adaptation and failure recovery during distributed training. His most recent 2025 work on accelerating Mixture-of-Experts training represents the cutting edge of efficient large model training infrastructure. Through his research, Skiadopoulos has established himself in both the database and systems research communities, with publications in premier venues including SOSP, OSDI, VLDB, and CIDR. His work consistently bridges theoretical database concepts with practical systems implementations, demonstrating how database techniques can solve real-world systems challenges in modern computing environments.
Bina Ramamurthy is a Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo, affiliated with the School of Engineering and Applied Sciences. With over three decades of experience in STEM education and research, her work focuses on blockchain technology, data-intensive computing, and decentralized systems. PhD in Electrical Engineering, University at Buffalo (1997) Her research centers on blockchain application development, smart contracts, and decentralized finance (DeFi). She directs the Blockchain ThinkLab at UB and developed the SUNY-approved certificate program in Data-Intensive Computing. Her Coursera MOOC specialization on Blockchain (launched 2018) has enrolled over 400,000 learners globally. Selected for prestigious recognition: SUNY Chancellor’s Award for Excellence in Teaching (2019) UB President's Circle Award (2017) She has secured multiple NSF grants, including as Principal Investigator on four HDR/IIS-CISE grants, and co-led six SUNY Instructional Technology grants. Her teaching emphasizes hands-on learning, with in-person lectures and practical exercises in courses like CSE4/506 and CSE4/526.
Andreas Huth is a prominent researcher at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, where he works within the Department of Ecological Systems Analysis. His research focuses on developing and applying computational models to understand complex ecological systems, with particular emphasis on vegetation dynamics, biodiversity patterns, and environmental interactions. Huth is actively involved in several major research initiatives including the FORMIND project, which simulates forest growth using individual-based vegetation models, and contributes to the UFZ's research program on Smart Models and Monitoring. Huth's research interests span ecological modeling, vegetation dynamics, biodiversity assessment, and environmental system analysis. He specializes in developing sophisticated computational frameworks that integrate atmospheric, ecological, and spatial data to model complex environmental processes. His work often involves coupling different modeling approaches, such as connecting atmosphere and vegetation radiative transfer models to study cloud-vegetation interactions. Huth is particularly interested in understanding spatial patterns in ecological systems, species-habitat relationships, and the impacts of environmental change on biodiversity. His methodological expertise includes agent-based modeling, individual-based modeling, and developing digital twin technologies for environmental systems. Analysis of Huth's recent publications reveals a strong focus on integrating different modeling approaches to address complex environmental questions. His work demonstrates a progression from basic ecological modeling toward more sophisticated integrative frameworks that connect atmospheric processes with vegetation dynamics, and social systems with ecological outcomes. A notable trend is his increasing involvement in digital twin technology for environmental systems, particularly for biodiversity monitoring and prediction. His research spans multiple scales - from microbial ecosystems to continental-scale species distributions - while maintaining a consistent methodological thread of spatially explicit modeling and causal analysis in complex systems. Huth collaborates extensively with researchers across the UFZ and internationally, as evidenced by his co-authorship on numerous interdisciplinary publications. While specific awards are not mentioned in the available information, his consistent publication record in high-impact journals and leadership in major modeling initiatives like FORMIND indicate significant recognition within the ecological modeling community. His work has practical applications for environmental policy, conservation planning, and climate change adaptation strategies. Huth is an active member of the Ecological Systems Analysis research group at UFZ, which develops and applies computational models to investigate key mechanisms in ecological systems. He contributes to several major research platforms including FORMIND (an individual-based vegetation model for species-rich forests) and participates in the UFZ's broader research initiatives on environmental monitoring and sustainable technologies. His work is integrated within the UFZ's research program on Smart Models and Monitoring, which aims to develop innovative approaches for understanding and predicting environmental change.
Per-Olov Östberg is an Associate Professor at the Department of Computing Science, Umeå University, and a research leader in the Autonomous Distributed Systems Lab (ADSLab). His work focuses on resource management for distributed cloud environments using AI/ML-based techniques, with a particular emphasis on ethical reasoning integration for responsible AI solutions. Research Themes: Cloud-edge continuum optimization, serverless frameworks, 6G computing challenges, data fabric architectures, and energy-aware systems Projects: COGNIT (cognitive serverless framework), WARA Common Information Bridge (data-driven cloud operations), De facto Center of Excellence in Autonomous Distributed Systems His publications (2011-2024) demonstrate consistent contributions to cloud resource management, including fairshare scheduling, decentralized prioritization, and power-performance tradeoffs. He has collaborated on interdisciplinary projects with institutions across Europe. Scientific Awards: None explicitly stated in provided information.
Magdalini Eirinaki is a Professor and Academic Program Coordinator for the MS in Artificial Intelligence at San José State University's Charles W. Davidson College of Engineering. With a career spanning two decades, her work bridges recommender systems , machine learning , and smart city applications . PhD in Computer Science (2006), Athens University of Economics and Business MSc in Advanced Computing (2000), Imperial College London BSc in Computer Science (1998), University of Piraeus Her research focuses on machine learning and recommender systems with extensions to generative AI , privacy-sensitive algorithms , and social network analysis . Recent publications explore federated learning , multi-resolution diffusion models , and autonomous network defense using reinforcement learning. Current projects include NSF-funded CollaborAIte (2024) EU Horizon/Marie Sklodowska-Curie's MUSIT (2024) IBM SkillsBuild Cloud Credits for Sustainability (2024) She has received multiple teaching and mentorship awards including: Newnan Brothers Award (2019) Applied Materials Award (2017) 5-time SJSU Distinguished Faculty Mentor Award Dr. Eirinaki advises students in AI , ML , and smart city projects, with recent graduates presenting at IEEE CAI (2025) and CSU Conference (2025).
Lars Dittmann is a Professor at the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), leading the Networks Technology and Service Platforms section. His work bridges advanced networking technologies with real-world applications in healthcare and transportation. Academic Role: Professor, Head of Section University: Technical University of Denmark (DTU) Department: Networks Technology and Service Platforms Research Interests: Professor Dittmann specializes in Software-Defined Networking (SDN) , 5G and IoT technologies , and energy-efficient network design , with a focus on applications in telemedicine and transportation systems . His work integrates machine learning for privacy-preserving traffic analysis and explores optical networks for high-bandwidth scenarios. Scientific Contributions: His recent publications emphasize green cellular networks using SDN/NFV/C-RAN, IoT benchmarking for coverage and mobility, and secure edge architectures for railways. Collaborative projects like the Future Patient telerehabilitation program highlight his interdisciplinary impact. Supervision: He supervises PhD candidates such as Radheshyam Singh, focusing on SDN-based IoT security and 5G network optimization. Labs & Projects: Leads initiatives like EXplorative network PLAnnINg and Broadband Trial Integration , addressing challenges in network reliability , emergency communication , and optical data center scaling .
Daniele Bringhenti is a Fixed-term Researcher at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino , and also holds external teaching roles at the University of Eastern Piedmont . His work focuses on Cybersecurity , Network Security , and Security Automation , with expertise in Distributed Systems , Theoretical Computer Science , and Formal Methods . His research integrates ERC sectors PE6_2 (Distributed Systems), PE6_5 (Security, Privacy), and PE6_4 (Theoretical Computer Science). Recent publications address automated cybersecurity management , firewall policy optimization , and intent-based network isolation . Scientific Awards include the ICICS Best Demo Award (2022) . He serves as Guest Editor for the Journal of Network and Systems Management (2024-2025) . His teaching roles span Master's and Bachelor's programs , including courses on Distributed Systems Programming , Threat Intelligence , and Network Security .
David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.
