Claire Maiza is an Associate Professor at Grenoble INP's Ensimag (École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble) and a member of the Verimag laboratory. She holds a Habilitation à Diriger des Recherches (HDR) awarded in June 2023. Her research focuses on timing analysis, real-time systems, and predictable execution models for embedded and multi-core/many-core platforms. Her work emphasizes worst-case execution time (WCET) evaluation, cache-related timing anomalies, and resource interference analysis in shared environments. Key contributions include frameworks for multi-core response time analysis, memory interference mitigation strategies, and novel approaches to model cache behaviors like LRU and PLRU policies. She also investigates scheduling algorithms resilient to preemption delays and software/hardware co-design for deterministic execution. Her research addresses challenges in hard real-time systems, including mixed-criticality scheduling, network-on-chip (NoC) timing, and WCET estimation using program semantics. Claire's methodologies aim to bridge theoretical timing analysis with practical implementation constraints in industrial applications. No academic awards or grants are explicitly listed in the provided text. She is affiliated with the SharedResources group, though specific lab/team details beyond Verimag are not elaborated.
Denis Cioffi is a Research Professor of Physics at North Carolina State University. His work spans two primary domains: project management theory and astrophysical research. In project management, he specializes in developing analytical frameworks for scheduling, cost estimation, and risk mitigation, notably pioneering advancements in S-curve modeling and earned value analysis. His astrophysics research focuses on supernova remnants, X-ray emission dynamics, and interstellar medium interactions. Key contributions include formalizing project risk contingency budgeting methods and exploring the evolution of extragalactic radio source structures. His project management methodologies have influenced both academic and industrial practices, emphasizing probabilistic approaches to uncertainty management. While no formal student advising roles are documented, his work demonstrates significant collaborative engagement in both fields. Research interests bridge engineering systems optimization and cosmic phenomena analysis, reflecting a unique interdisciplinary approach. His publications since 1980 showcase sustained contributions to both disciplines, with recent emphasis on project management formalisms and historical astrophysical investigations.
Piotr Luszczek is a Research Professor and Adjunct Associate Professor at the University of Tennessee, Knoxville's Tickle College of Engineering, affiliated with the Department of Computer Science and the Innovative Computing Laboratory. He holds a Ph.D. and M.S. from the University of Tennessee, Knoxville, and a B.S. from AGH University of Science and Technology in Kraków, Poland. Affiliations : Innovative Computing Laboratory (ICL), Tickle College of Engineering. Roles : Research and teaching in high-performance computing, numerical linear algebra, and performance optimization. Research Interests focus on benchmarking, numerical linear algebra for HPC, automated performance tuning for modern hardware, and stochastic models for performance analysis. His work emphasizes scalable algorithms, GPU acceleration, and efficient use of hybrid architectures. Grants and Collaborations include projects on batched linear algebra, sparse matrix operations, and energy-efficient AI frameworks. He contributes to software libraries like PLASMA and MAGMA, optimizing for exascale computing. Labs/Teams : Active in the Innovative Computing Laboratory (ICL), developing tools for HPC benchmarking (e.g., HPCG) and parallel linear algebra libraries. Engaged in international collaborations for exascale computing initiatives.
Seyyedali Hosseinalipour is an Assistant Professor in the Department of Electrical Engineering at the University at Buffalo (School of Engineering and Applied Sciences). His research focuses on synergies between machine learning and wireless networks, with a particular emphasis on federated learning, vehicular networks, and intelligent network design. He holds a PhD in Electrical Engineering from North Carolina State University (2020), an MSc from the same institution (2017), and a BSc from Amirkabir University of Technology (2015). His research interests include federated learning frameworks for dynamic environments, UAV-assisted communication systems, and optimization of edge/cloud computing architectures. Notable areas of exploration include decentralized federated learning, non-IID data distributions, and applications in IoT and 5G/6G networks. His recent work addresses challenges such as energy-efficient resource allocation in UAV networks, latency reduction in hierarchical federated learning, and robust task scheduling over vehicular clouds. He has also explored cross-disciplinary applications like federated learning in educational analytics and medical imaging. Despite the breadth of his work, he maintains a focus on practical implementations through frameworks like HEART and GA-DRL. Dr. Hosseinalipour’s contributions bridge theoretical models and real-world deployment, emphasizing scalability, privacy, and system resilience. His research has implications for smart cities, autonomous systems, and distributed AI ecosystems.
Sebastian Kraul is an Assistant Professor in the Department of Operations Analytics at the School of Business and Economics , Vrije Universiteit Amsterdam. He holds a PhD in Business and Economics from the University of Augsburg (2021) and has postdoctoral experience in Health Care Operations Management. His research focuses on Operations Research , Healthcare Operations Management , and Optimization Algorithms , with applications in healthcare scheduling, resource allocation, and machine learning integration in operational problems. He teaches courses such as Sustainable Management of Global Supply Chains . Education: PhD in Business and Economics, University of Augsburg (2021) Master's in Business Administration, RWTH Aachen University (2015) Bachelor's in Business and Economics, Technical University Dortmund (2013) Research interests include optimizing healthcare processes (e.g., sterilization, physician scheduling) and developing robust frameworks for resident scheduling. His work employs methods like GRASP, column generation, and machine learning to tackle operational challenges in healthcare systems. For instance, his 2023 study on stable annual scheduling uses prioritized training schedules to address uncertainty in medical residency programs. His publications highlight trends in healthcare operations optimization, emphasizing resilience, resource efficiency, and continuity of care. He has received the Dissertation Award of the GOR (2022) for his doctoral work. Dr. Kraul actively participates in peer review for journals like European Journal of Operational Research and Omega . He currently teaches Business Mathematics , Operations and Supply Chain Management , and Sustainable Management of Global Supply Chains .
Dr. Elena Kakoulli is a Lecturer in Information Systems and Coordinator of the MSc in Information Systems and Digital Innovation at the University of Nicosia. She holds a Ph.D. in Computer Engineering from Cyprus University of Technology (2015), following a Master’s in Computer Science (2009) and B.Sc. (2007) from the University of Cyprus. Her research focuses on computer architecture, photonics-based interconnection networks, cybersecurity, and AI-driven educational technologies. Notable contributions include work on resilient NoC architectures, distributed storage systems (OctopusFS), and silicon photonics integration for high-performance computing. Dr. Kakoulli’s research interests span network-on-chip (NoC) design, IoT security, and adaptive learning frameworks. She has collaborated on projects such as distributed tiered storage for cluster computing with Dr. Herodotou. Recognitions include awards from the University of Cyprus and Marfin Laiki Bank Foundation for academic excellence during her postgraduate studies. She has also contributed to teaching roles as a Teaching Assistant at the University of Cyprus and lecturer at Ctl Eurocollege, covering courses like computer architecture, compilers, and operating systems. Her publications highlight advancements in cybersecurity models, AI-integrated education tools, and next-generation computing architectures. She actively participates in academic conferences, including the 21st European, Mediterranean, and Middle Eastern Conference on Information Systems (EMCIS 2024), and has served as a Technical Program Committee member.
Fulvio Giovanni Ottavio Risso is a Full Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, where he is a member of the NETGROUP Computer Networks Research Group and leads research in cloud, edge, and software-defined networking. He is the Scientific Advisor of the European EIT Digital Partnership and serves as a representative for Politecnico di Torino in EIT Digital. He teaches core courses in Computer Engineering, including Cloud Computing Technologies, Enterprise Network Technologies, and Software Networking, and supervises several PhD students in advanced distributed systems. His research focuses on cloud computing, edge computing, network functions virtualization (NFV), software-defined networking (SDN), and high-speed packet processing. He has pioneered work in eBPF-based network functions through the Polycube framework and in computing continuum orchestration via the Liqo project. His interests extend to Kubernetes networking, real-time data plane optimization, and privacy-preserving infrastructure. He has led numerous EU, national, and industry-funded projects, including FLUIDOS (PNRR), NEWTON, RESTART, TOSHI, ASTRID, and NFV@EDGE. His recent work emphasizes liquid computing, borderless data spaces, and secure, scalable network services for 5G/6G. The recent publications reflect a strong trend in edge-to-cloud orchestration, secure and efficient data plane processing, and real-time performance optimization. Key themes include the use of reinforcement learning for scheduling in the computing continuum, Kubernetes-based edge orchestration, eBPF for in-kernel networking, and energy-aware task distribution. Projects like Liqo and Polycube are central to his vision of a programmable, fluid infrastructure. The integration of machine learning, real-time monitoring, and open-source frameworks underscores a commitment to practical, scalable solutions in modern distributed systems. Scientific Awards and Recognitions: No explicit awards listed in the provided text. Advising and Grants: Fulvio Risso supervises multiple PhD students including Attilio Oliva, Daniele Cacciabue, Davide Miola, Jacopo Marino, Stefano Galantino, Carlos Mateo Risma Carletti, and Federico Parola, whose research spans cloud-edge continuum, vehicular micro-clouds, and Kubernetes networking. He has led over 30 competitive and commercial research projects, including EU-funded initiatives (H2020, EIT), national PRIN projects, PNRR missions, and industry contracts with Rakuten Mobile. These grants focus on network programmability, edge computing, 5G/6G observability, anomaly detection, and secure orchestration, reflecting strong industry-academia collaboration. Labs and Research Groups: He is a key member of the NETGROUP - Computer Networks Group (DAUIN) and leads research activities in LAB 9 - Research Laboratory (DAUIN). He is also associated with the Interdepartmental Center PIC4SeR - PoliTO Interdepartmental Center for Service Robotics. His work is deeply integrated with open-source development, particularly through Liqo and Polycube, which are actively used in both research and industrial deployments.
Tobias Wrigstad is a faculty member at Uppsala University, Sweden, with research interests spanning type systems, reference capabilities, programming language design, scripting languages, and concurrent/parallel programming. His work focuses on memory management, concurrency safety, and language extensions for performance optimization. Education: Not explicitly mentioned in provided data Research Interests: Designing type systems to enforce concurrency safety and memory correctness Reference capabilities for manual and automatic memory management Actor model programming and garbage collection co-design Cache locality optimization without program restructuring Formal verification of language designs using Dafny Recent Publications (2025-2015): Explore concurrency safety through region ownership Develop parallel array programming models in Kappa Investigate energy-efficient garbage collection Design capability-based dynamic languages for data race freedom Create formal models for heap invariants and incorrectness Optimize memory allocation via load barriers Conference Involvement: 2025: IWACO Committee Member, OOPSLA Associate Chair 2024: Program Co-Chair for IWACO, Author in VIMPL, MPLR, ISMM 2023: SPLASH Steering Committee, ECOOP PC Member 2022-2015: Active in PLDI, ECOOP, ICFP, and related workshops
Junaid Shuja is a researcher with significant contributions to Mobile Edge Computing , Cloud Environments , and IoT Systems . Collaborating with scholars like Kashif Bilal , Abdullah Gani , and Ehzaz Mustafa , his work spans computation offloading, resource allocation, and security frameworks. Research Highlights 2017: Analysis of Vector Code Offloading in Heterogeneous Architectures 2021: Survey on Machine Learning for Edge Caching 2023: Reinforcement Learning for Computation Offloading in Vehicular Networks 2024: Blockchain Applications in Land Lease Systems and Employee Transfers 2025: Deep Reinforcement Learning for Resource Optimization His recent work focuses on Deep Learning and Blockchain for latency-sensitive applications in IoT and vehicular networks, published in IEEE Access , Cluster Computing , and Telecommunication Systems . Key co-authors include Faisal Rehman , Abdallah Namoun , and Muhammad Bilal .
Necati ARAS is a Professor in the Department of Industrial Engineering at Bogazici University. He holds a PhD from Bogazici University (1999). His research focuses on inventory control, reverse logistics, supply chain optimization, and the application of artificial neural networks in complex systems. He has published extensively on topics including facility location, wireless sensor networks, disaster preparedness, and competitive market strategies. Education: PhD, Bogazici University, 1999 ARAS's work bridges theoretical optimization with real-world applications, particularly in logistics, network security, and smart cities. His contributions include models for minimizing contagion spread in networks, optimizing multi-tier cloud computing, and improving vehicle routing efficiency. Recent research emphasizes strategic resource allocation and mitigating misinformation in social networks. Despite no listed awards, his prolific publication record (over 70 articles) reflects sustained academic impact. His research often addresses humanitarian challenges like disaster response and infrastructure protection. ARAS has advised numerous projects but no specific student names are provided in the text. His research teams likely focus on interdisciplinary collaboration between operations research, computer science, and civil engineering.
Mohammad Mohammadi Amiri is an Assistant Professor in the Computer Science Department at Rensselaer Polytechnic Institute (RPI), joining in Fall 2023. Previously, he held postdoctoral positions at MIT Media Lab (2022–present) and Princeton University (2019–2021), working with Prof. Vincent Poor and Prof. Sanjeev Kulkarni. He earned his Ph.D. in Electrical and Electronic Engineering from Imperial College London (2019), with prior degrees from Iran University of Science and Technology (B.Sc., 2011) and the University of Tehran (M.Sc., 2014). His research focuses on decentralized intelligence, federated learning, and wireless edge computing. He explores enabling distributed machine learning while preserving privacy, particularly in IoT and smart systems. Key areas include federated learning over wireless channels, data valuation, and optimization of edge computing resources. His articles address topics like federated learning convergence, device selection in wireless systems, and decentralized data valuation. Recent work includes papers accepted at AAAI 2023, CISS 2023, and IEEE Transactions on Wireless Communications. Awards : IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award (2019), IEEE Information Theory Chapter Eryl Cadwallader Davies Prize (2019), Imperial College London Advising & Grants : Mentoring Ph.D. students in machine learning and distributed systems Recipient of multiple scholarships and awards during his academic career Labs/Teams : Camera Culture Group (MIT Media Lab) Princeton University's Electrical Engineering Department
Fidan Mehmeti is a Senior Researcher and Lecturer at the Chair of Communication Networks at the Technical University of Munich (TUM), led by Prof. Wolfgang Kellerer. His expertise spans wireless networks, 5G/6G systems, edge computing, and network security. He holds a PhD from EURECOM/Telecom ParisTech and has held postdoctoral positions at institutions like the University of Waterloo and Penn State University. Education PhD, Institute EURECOM/Telecom ParisTech, 2015 MSc, University of Prishtina (Kosovo), 2009 BSc, University of Prishtina, 2006 Research Interests Fidan's work focuses on resource allocation, mobility management, edge computing, and AI-driven network optimization. He explores challenges like latency guarantees in 6G multi-domain networks and energy-efficient edge orchestration. His research bridges theoretical analysis (e.g., queueing theory) with practical applications in healthcare and autonomous systems. Recent Contributions Recent articles address mMTC traffic management, latency-critical 6G systems, and medical edge computing. He co-authored chapters on 6G architecture and healthcare applications in the upcoming '6G-life' book. Awards & Recognition Best Student Paper Award at IEEE/IFIP NOMS 2025 IEEE Senior Member (2023) Recipient of Best Paper Awards at CNSM 2024 and CCNC 2023 Grants & Projects He leads projects like '6G-ANNA' (BMBF-funded) and '6G-Life', addressing security, sustainability, and healthcare integration. Collaborations with industry partners like Nokia highlight applied research. Teaching Teaches courses on network modeling, optimization, and data networking at TUM. Recent offerings include 'Analysis, Modeling, and Simulation of Communication Networks' and 'Communication Networks Modeling and Optimization'.
Hasan Yagiz Özkan is a Scientific Staff member at the Chair of Communication Networks at Technische Universität München. His research focuses on networked control systems, software-defined networking, cybersecurity, and real-time communication protocols. He contributes to projects such as the ERC Network Flexibility initiative and the 6G Future Lab Bavaria. Recent work includes advancements in task-oriented scheduling for networked control systems and intrusion detection using machine learning. His publications span topics like bug resolution prediction in softwarized networks and age-of-information-aware frameworks. He has collaborated on papers addressing network reliability, software-defined radio implementations, and contrastive pretraining for cybersecurity. His inactive status may pertain to a specific role, but he remains active in academic research and publications.
Eirinakis Pavlos is an Associate Professor at the Department of Industrial Management & Technology, University of Piraeus. His affiliations include the Maritime and Industrial Studies school. He specializes in analytical methods in industrial systems and optimization techniques. His research focuses on digital twins, reconfigurable manufacturing systems, and maritime logistics optimization. Pavlos' work integrates advanced technologies like machine learning and cyber-physical systems to enhance industrial processes and supply chain resilience. Research interests span manufacturing systems optimization, energy-efficient robotics, and emissions control in maritime industries. He explores algorithmic solutions for collaborative logistics and mathematical programming under uncertainty. His recent work emphasizes cognitive digital twins for production resilience and modular manufacturing architectures. Publications highlight contributions to digital twin applications, stochastic optimization, and maritime big data analytics. His articles often bridge theoretical optimization frameworks with practical industrial challenges, such as LPG production quality control and cargo loss prevention via AIS-enabled systems. Pavlose@unipi.gr is his official contact. His office is located at 503/Delig. No listed scientific awards or advising students are mentioned in the provided texts.
Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.
