Olivier Hudry is a Professor at Télécom Paris in the Computer Science and Networks (Infres) department. He is affiliated with the Cybersecurity and Cryptography (C²) research team and the Information Processing and Communication Laboratory (LTCI) . His work bridges combinatorial optimization and graph theory with applications in cybersecurity , voting theory , and network optimization . His research includes identifying codes in graphs , domination problems , and algorithmic complexity in social choice theory. Recent publications focus on q-ary Lee hypercubes , rainbow problems , and unique solutions in graph theory . He has also developed software for computing Slater indices in tournaments . Teaching roles include courses on Data Structures and Algorithms (INF 101), Continuous Optimization (MDI 210), Complexity Theory (MITRO 203), and Combinatorial Optimization (MITRO 205). He has authored textbooks on combinatorial analysis and operations research . Contact: olivier.hudry@telecom-paris.fr | olivier.hudry@telecom-paristech.fr
Theodoros Kaifas is an Assistant Professor at the Department of Electrical and Computer Engineering, Democritus University of Thrace, specializing in microwaves, antennas, and wireless communications. He has held academic positions at Aristotle University of Thessaloniki and International Hellenic University. BA in Physics MA and PhD in Electronic Physics (Radioelectrics), Aristotle University of Thessaloniki His research spans antenna design , microwave engineering , 5-6G wireless networks , and bioelectromagnetic applications . Recent work focuses on advanced antenna arrays, microwave ablation, and computational electromagnetism techniques. Selected research programs include: ARES (2024-2025): Antennas and Reflector Systems ESA-funded projects on multibeam array antennas Thales grants for RFID systems and cognitive radio devices As a senior IEEE member and contributor to international journals, he has taught undergraduate courses in antenna design, mobile communications, and bioelectromagnetism, alongside postgraduate lectures on advanced antenna topics.
Lorenzo Galleani serves as Associate Professor at the Department of Electronics and Telecommunications within the College of Electronic, Telecommunications and Physics Engineering at Polytechnic University of Turin. His academic appointment falls under Scientific Disciplinary Sector IINF-03/A (Telecommunications) in Area 0009 (Industrial and Information Engineering). His research focuses on signal processing for atomic timing in global navigation satellite systems and nonstationary signal processing , with significant contributions to optical time scale development and atomic clock anomaly detection. As principal investigator of the Navigation, Signal Analysis and Simulation (NavSAS) Group, he bridges theoretical signal processing with practical timekeeping applications. Recent publications demonstrate a clear trajectory toward optical clock integration and robust timescale algorithms , particularly through EU-funded projects like ROCIT. His work shows increasing emphasis on sub-nanosecond precision timekeeping for next-generation satellite navigation systems. Galleani has supervised doctoral students in Metrology across ten consecutive academic cycles (2014/15-2022/23) and teaches core courses including Signal Processing: Methods and Algorithms for Communications Engineering. His extensive grant portfolio includes EU Horizon 2020 projects, national PRIN grants, and commercial contracts with space agencies focused on Galileo system support. He leads the Navigation, Signal Analysis and Simulation (NavSAS) research group, which develops signal processing techniques for satellite navigation timing systems. Current projects focus on optical clock integration for international timescales and robust time reference architectures for European GNSS services.
Gabriella Balestra is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Electronics and Telecommunications (DET) and serving as Coordinator of the College of Biomedical Engineering . She leads research in biomedical signal processing, telemedicine, and AI-driven medical software design, with a focus on cardiac monitoring and radiomics. Scientific Collaborator, Polytechnic University of Turin & University of Turin (2023-2027) Member, GEDI Observatory for Gender Equality and Inclusion Research Interests span biomedical informatics, clinical process modeling, and machine learning for healthcare. Her work includes developing digital twins for heart failure patients and AI tools for cancer segmentation and telemedicine applications. Recent publications highlight AI integration in radiomics , phonocardiography , and multi-center clinical studies, with a focus on wearable sensors and medical device software. Supervisor: Silvia Cannone (Digital Twin for LVAD Patients), Gregorio Dotti (Telerehabilitation), Jovana Panic (MRI-based AI for Oncology) She leads the Biolab: Biomedical Engineering Group and contributes to national and EU-funded projects like RECHARGE and ASSOCIATE .
Nikolay Petkov Manchev is a Senior Lecturer at the Department of Communication Equipment and Technologies, Technical University - Gabrovo . With expertise in wireless communication systems and IoT infrastructure, his research focuses on LoRaWAN network planning, radio coverage optimization, and smart city applications. Developed bicycle counting station (2022) and vending machine control systems (2021) Published extensively on LoRaWAN energy performance, signal propagation, and IoT communication protocols Active in international conferences like IEEE CIEES 2024 and Electronics (ET) 2022
Dr. R. Gregory Franks is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University. He holds a Ph.D. from Carleton and is a registered Professional Engineer (P.Eng.). His research focuses on performance modeling of distributed systems, operating systems design, and internet protocol routing. Education: Ph.D. in Systems and Computer Engineering, Carleton University Research Focus: Dr. Franks specializes in performance modeling techniques including layered queueing networks, simulation methods, and Petri Nets. His work addresses distributed software performance, reverse engineering challenges, and operating system optimization. Current investigations include peer-to-peer protocol analysis, system optimization using layered models, and trace-based performance model generation for complex systems. Publication Trends: His research output consistently addresses performance modeling innovations, with recent emphasis on layered queueing network optimizations, automated trace analysis, SIP application server performance, and autonomic software engineering. Work demonstrates strong applications in telecommunications, distributed systems, and parallel computing. Supervision and Labs: Directs the RADS Lab (Real-time and Distributed Systems) and has supervised multiple graduate students in performance engineering. Current projects involve collaborations with industry partners including AT&T and CITO, focusing on internet telephony systems and distributed component architectures.
Michael Menth is a Professor at the Department of Computer Science, University of Tübingen, specializing in communication networks. His work bridges theoretical and applied research in network resilience, software-defined networking (SDN), and time-sensitive networking (TSN). University: University of Tübingen Department: Computer Science Research Interests: Network security, P4 programming, multicast protocols, and congestion control. Recent publications focus on advancements in SDN, TSN, and network security. His team has developed tools like P4TG for high-speed traffic generation and SENSOR for flow monitoring. Key projects include BIER-TE for multicast optimization and OIDC² for secure authentication. His research trends emphasize stateless network architectures, machine learning integration for network management, and automotive/E/E system softwarization. Collaborative work spans eHealth platforms like SSTeP-KiZ and security frameworks for industrial networks. Publications from 2022-2025 demonstrate technical depth in P4-based implementations, TSN scheduling, and resilient multicast protocols. While no explicit awards are listed, his extensive RFC contributions (e.g., RFC 5696, RFC 9262) highlight industry-standard impact.
Bruce Hartpence is a Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He has been at RIT since 1998, focusing on networking, security, and machine learning applications in networked systems. His research spans wired/wireless networks, DNS/DHCP/VoIP services, and neural networks for packet classification and security challenges. Hartpence is actively involved in curriculum development for networking and cybersecurity programs, emphasizing hands-on lab experiences. Education: BS, Rochester Institute of Technology MS, Rochester Institute of Technology Ph.D., Rochester Institute of Technology Research Interests: Hartpence's work centers on network visibility, security protocols, and real-time data quality. His neural network research addresses challenges in packet classification, adversary defense, and quality of service (QoS). He also explores software-defined networking (SDN) and the IEEE 1910.1 Meshed Tree project for faster network convergence. Teaching & Projects: Teaches courses on data communications, routing/switching, VoIP, and network design Runs the RIT SDN testbed and contributes to GENI Author of O'Reilly's Packet Guide series on core network protocols and VoIP Develops open-source virtualization clusters for education Labs & Collaborations: Hartpence's lab focuses on practical networking solutions, including hybrid physical/virtual network performance analysis and secure wireless architectures. He collaborates with industry partners like Wegmans on applied research projects.
Yaling Yang is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. Her research focuses on wireless networks, network security, resource management, and media access control. She holds a Ph.D. from the University of Illinois at Urbana-Champaign (2006) and a B.S. from UESTC (1999). Her work addresses challenges in dynamic spectrum access, GPS spoofing defense, 5G network resilience, and IoT security. Notable contributions include crowdsourcing-based spectrum monitoring, privacy-preserving dynamic spectrum sharing frameworks (e.g., SZ-SAS), and hardware-software co-design for sensor nodes. Recent publications emphasize agile 5G architectures, interference mitigation, and robust GPS security mechanisms using off-the-shelf devices. Her research often bridges theoretical models with practical implementations in maritime communications and cognitive radio networks. Yang’s affiliations include the Virginia Tech Scholar Profile and an affiliated research group. She collaborates on projects involving energy harvesting marine communication systems and database-driven spectrum management.
Roles & Affiliations : Sahil Waqar is a Researcher at the University of Agder (UiA), affiliated with the Faculty of Engineering and Science and the Department of ICT. His research specialization is in Mobile and Wireless Communications, focusing on radar-based human activity recognition (HAR) systems. Education : PhD Candidate, University of Agder (2020–2024), Thesis: "A Simulation-Based Framework for the Design of Direction-Independent Human Activity Recognition Systems Using Radar Sensors" Research Interests : Waqar’s work emphasizes advancing simulation-based approaches for radar sensor systems, particularly in overcoming challenges like multi-directional motion and data scarcity. His innovations include distributed MIMO radar configurations, interference mitigation techniques, and deep learning-based activity classification. His paradigm-shifting research reduces reliance on experimental data, enabling scalable and cost-effective HAR solutions. Supervisors : Main supervisor Prof. Matthias Uwe Pätzold (UiA) and co-supervisor Assoc. Prof. Bjørn Olav Hogstad (NTNU). Labs & Teams : Involved in UiA’s Mobile and Wireless Communications research group, focusing on RF sensing and radar system design.
Prem Prakash Jayaraman is a Professor in the Department of Computer Science and Software Engineering within the Faculty of Science, Engineering and Technology at Swinburne University of Technology. His research focuses on the intersection of Internet of Things, context-aware computing, and distributed systems, with particular emphasis on practical applications in smart cities, digital manufacturing, and healthcare. Dr. Jayaraman's research interests span a wide range of topics within the IoT ecosystem, including context-aware computing, edge and cloud computing integration, digital twins for industrial applications, sensor network optimization, and performance evaluation of IoT middleware platforms. His work often addresses the challenges of real-world deployment, focusing on practical solutions for context management, data quality assurance, and resource optimization in distributed environments. He has made significant contributions to frameworks for context query generation, adaptive context monitoring, and caching strategies that enhance the efficiency of IoT applications. His recent publications demonstrate a strong trend toward applied research with practical implementations in smart city infrastructure, digital manufacturing, and healthcare applications. His work often combines theoretical frameworks with real-world validation, particularly evident in his studies on 5G-enabled smart cities, AI-driven roadside infrastructure monitoring, and digital twin implementations for manufacturing quality control. The research spans multiple sub-disciplines including context-aware systems, performance evaluation methodologies, and resource optimization techniques for distributed IoT environments. Dr. Jayaraman maintains an extensive collaborative network with researchers across Australia and internationally, particularly with Dimitrios Georgakopoulos, Rajiv Ranjan, Arkady Zaslavsky, and Abdur Forkan. His work appears in top-tier journals and conferences including IEEE Transactions on Industrial Informatics, Future Generation Computer Systems, IEEE Internet of Things Journal, and major conferences like MDM, HICSS, and IEEE Cloud.
Sook Shin serves as a Collegiate Assistant Professor in the Department of Electrical and Computer Engineering within Virginia Tech's College of Engineering. Her interdisciplinary work bridges computer science, agricultural engineering, and bioinformatics through innovative applications of machine learning. Education PhD in Computer Engineering, Virginia Tech Master of Information Technology, Virginia Tech B.S. in Computer Science, Virginia Tech Her research centers on AI-driven solutions for precision agriculture , particularly in livestock monitoring systems using depth imaging and wireless sensors. She develops machine learning frameworks for pig behavior classification, weight prediction, and resource optimization, while also contributing to bioinformatics tools for plant modeling and disease subtyping. Recent work emphasizes edge intelligence for power-efficient sensor networks and secure data labeling pipelines. Analysis of her 15 most recent publications (2012-2025) reveals a dominant focus on precision livestock farming (60% of works), with significant contributions to bioinformatics (25%) and educational technology (15%). Her methodology consistently integrates deep learning with domain-specific sensor data, showing increasing sophistication in multi-modal input processing and real-time system optimization from 2022 onward. Teaching Contributions Applied software design Data structures and algorithms Computational thinking Machine learning applications She actively develops scientific web tools including PlantSimLab for plant biologists and contributes to capstone project frameworks that bridge academic theory with industry applications in smart farming systems.
Greg Kulczycki is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech) , part of the College of Engineering . His research focuses on digital education , formal methods in software development, and bridging industry-academia collaboration in software engineering education. He earned his Ph.D. in Computer Science from Clemson University in 2004. Key research interests include: Formal verification of software components and systems Web services and transactional service composition Software reuse and productivity enhancement Industrial practices in web application development Recent publications emphasize formal methods for error detection, education-industry partnerships, and optimizing mobile network performance. His work often intersects computational linguistics (e.g., term extraction) and data-centric service design. No scientific awards or student advising information is listed in the available data.
Hafiz Munsub Ali is a Lecturer at the School of Computing, Binghamton University. He holds a PhD in Engineering Science from Simon Fraser University (Canada) and an MS in Computer Science from Karachi Institute of Economics and Technology (Pakistan). Previously, he served as a postdoctoral researcher at Dakota State University's College of Business and Information Systems and worked in IT industry roles such as Quality Assurance and Software Support Engineer. Education : PhD in Engineering Science, Simon Fraser University (Canada) MS in Computer Science, Karachi Institute of Economics and Technology (Pakistan) Research Interests : His work focuses on combinatorial optimization , network planning , healthcare informatics , and IoT systems . Key areas include designing secure IoT-enabled healthcare infrastructure, optimizing 5G networks using swarm intelligence, and developing efficient clustering protocols for emerging IoT applications. His interdisciplinary approach integrates machine learning, biofilm imaging analysis, and energy-efficient resource allocation strategies. Publications Trends : Recent publications emphasize IoT security , fog computing , and swarm intelligence algorithms . Notable contributions include protocols for secure healthcare IoT networks (ESPINA) and energy-efficient VM placement strategies using hybrid optimization algorithms. Awards : Currently no awards listed. Advising & Grants : No advising or grant details explicitly stated. His academic focus remains on teaching and research in computational systems. Labs/Teams : No specific lab affiliations mentioned, though his contributions involve collaborative research in network optimization and biomedical imaging.
Dr. Spyros Skarvelis-Kazakos is an Associate Professor in Electrical Engineering at the University of Sussex , leading the Critical Infrastructure Resilience Network (CIReN) and managing the Power Systems and Smart Grid sections of the Energy Dynamics Laboratory. He has held academic positions at the University of Sussex (since 2015) and previously at the University of Greenwich (2012–2015). His research focuses on energy network resilience, distributed energy resources (DER) control, multiple energy carriers, and artificial intelligence in smart grids.
