Adrián Pekár is a Senior Research Fellow at the Department of Networked Systems and Services, Budapest University of Technology and Economics (BME). He holds a PhD in Computer Networks from the Technical University of Kosice (TUKE), Slovakia (2014), and a bachelor's degree from TUKE (2011). His research focuses on optimizing network traffic measurement, monitoring platforms, and enhancing techniques for traffic classification, data reduction, and visualization in traditional and software-defined networks. Adrian's career includes roles as a Virtual Infrastructure Network Engineer at TUKE's Institute of Computer Technology (post-PhD), a Postdoctoral Fellow at Victoria University of Wellington (2016–2019), and an Assistant Professor (Lecturer) at BME (2019–2022). His current work continues to address challenges in network measurement and monitoring systems, leveraging both academic and practical expertise. No scientific awards or grants are explicitly mentioned in the provided text. His research interests emphasize interdisciplinary approaches to network efficiency and scalability.
Juha Kontinen is a Professor and Docent in the Department of Mathematics and Statistics at the University of Helsinki. He serves as a Supervisor for the Doctoral Programme in Mathematics and Statistics. His research focuses on mathematical logic, theoretical computer science, computational complexity, and formal methods. Key areas include dependence logic, team semantics, and their applications in areas like database theory and artificial intelligence. He has been actively involved in numerous research projects funded by organizations such as the Magnus Ehrnrooth Foundation and the Academy of Finland. His work spans foundational studies of computational complexity, formal logics for team semantics, and interdisciplinary applications. Recent projects include exploring discrete differential equations for circuit complexity and probabilistic team semantics with Boolean negation. Kontinen has authored/co-authored over 90 publications, including articles in prestigious journals and conference proceedings. His work bridges theoretical foundations with practical computational challenges, addressing topics like neural network training complexity and hyperproperties in temporal logics. He has also contributed to conference organization, peer review, and doctoral supervision, highlighting his role as a leader in the academic community.
José Manuel Arco Rodríguez is an Associate Professor in the Department of Automation at the Universidad de Alcalá (Spain). He holds a Doctorate from the same institution for his thesis Propuesta de optimización de la interconexión de redes con calidad de servicio para aplicaciones multimedia (2000), supervised by Dr. Daniel Meziat Luna. His research focuses on intelligent networks, software-defined networking (SDN), edge computing, and telecommunication systems, with particular emphasis on network optimization, protocol design, and AI integration in network architectures. He leads the NetIS research group (Networks and Intelligent Systems), exploring cutting-edge solutions for scalable, reliable data center networks and industrial IoT ecosystems. Key research areas include in-band network control, hybrid SDN architectures, multi-hop edge computing with IoT devices, and low-latency bridging protocols. His work spans theoretical frameworks to hardware implementations (e.g., NetFPGA), with a strong emphasis on practical applications in data centers, campus networks, and industrial environments. Recent contributions address AI-driven network management, plug-and-play routing protocols, and zero-configuration network architectures. Publications highlight advancements in SDN topology discovery (TEDP), scalable addressing (GA3), and path exploration protocols (ARP-Path, All-Path Bridging). His research bridges theoretical networking principles with real-world deployment challenges, often emphasizing interoperability between legacy and modern systems. Arco Rodríguez has collaborated on EU-funded projects and contributed to international standards for network protocols. His lab (NetIS) actively develops open-source tools for network analysis and simulation. Current research trends include AI-enhanced softwarized networks and cloud-edge continuum architectures for IIoT.
Diego López Pajares is a Lecturer at the Department of Automática within the Universidad de Alcalá, Spain. His research focuses on Software-Defined Networking (SDN), Internet of Things (IoT), edge computing, and AI-driven network optimization. He is a member of the NetIS research group (Networks and Intelligent Systems), which explores programmable networks and intelligent systems for future communication infrastructures. He earned his Doctorate in Engineering from Universidad de Alcalá with a thesis titled Nuevos conmutadores de red para redes integradas con SDN (New SDN-enabled network switches for integrated networks), supervised by Dr. Juan Antonio Carral Pelayo and Dr. Elisa Rojas Sánchez. His work addresses challenges in scalable network protocols, hybrid SDN architectures, and AI applications in telecommunications. Key research trends include edge computing IIoT systems, fault prediction in smart grids, and 6G-enabling technologies. His publications emphasize in-band control protocols, disjoint path algorithms, and P4-programmable switches. Collaborative efforts with industry and academia aim to bridge theoretical advancements with practical network deployments. Notable contributions include the BareFlow protocol for Layer-2 plug-and-play routing, MDTA algorithm for dynamic environments, and eHDDP for heterogeneous network discovery. These innovations address scalability, security, and interoperability in modern network ecosystems. López Pajares' work intersects with military deployable networks, autonomous vehicle traffic systems (SYROPS), and cloud continuum architectures. His research bridges traditional networking domains with emerging paradigms like AIoT and software-defined infrastructures.
Elisa Rojas Sánchez is a tenured professor at the University of Alcalá, affiliated with the Department of Automation within the School of Telematics Engineering. Her primary research focuses on advanced networking technologies including Software-Defined Networking (SDN), edge computing, IoT integration, and network architecture optimization. She leads the NetIS research group (Networks and Intelligent Systems), exploring innovations in programmable networks and intelligent systems for future communication infrastructures. Her academic background includes a doctoral thesis on high-performance transparent Ethernet switch architectures. She has contributed significantly to protocols like ARP-Path and Torii-HLMAC, advancing scalable routing and fault-tolerant data center designs. Her work bridges theoretical networking research with practical implementations in 6G, industrial IoT, and cloud-edge continuum systems. Recent publications highlight breakthroughs in data-driven protocols (e.g., BareFlow), AI-driven fault prediction in smart grids, and topology-aware resource management in dense networks. She also explores educational innovations like gamification in engineering teaching and hybrid SDN methodologies. Key Research Themes: SDN protocols, edge computing frameworks, IoT scalability, network resilience, AI in networking Notable Achievements: Developed open-source tools like NetIDE, contributed to OpenFlow standards, and pioneered multi-path routing algorithms Her work frequently addresses real-world challenges such as latency reduction in programmable ASICs (Tofino P4), secure in-band control channels, and collaborative resource-sharing in edge environments. She actively participates in international conferences as a program chair and continues advancing the next generation of network architectures.
Muhammad Fermi Pasha is a Senior Lecturer at the School of Information Technology, Monash University, Malaysia. He holds a PhD in Brain-inspired Computing from Universiti Sains Malaysia and has been actively contributing to research and teaching since joining Monash. His academic roles include lecturing and serving as Chief Examiner for courses such as FIT1008, FIT2085, FIT9123, FIT5152, and FIT3175, focusing on computer science, business information systems, and usability. PhD in Brain-inspired Computing, Universiti Sains Malaysia (2010) MSc in Computer Science, Universiti Sains Malaysia (2006) BCompSc (Hons) in Software Engineering, Universiti Sains Malaysia (2003) Dr. Pasha's research spans computational neuroimaging, intelligent network security, digital health, and big data analytics. His work integrates artificial intelligence with healthcare applications, including Alzheimer's diagnosis, mHealth platforms, and secure medical data systems. He emphasizes evolving systems, machine intelligence, and neocortex memory modeling. His projects often involve interdisciplinary collaboration and community engagement. The recent publications highlight a strong trend in AI-driven healthcare solutions, secure data systems, and behavioral analysis using deep learning. His work combines computer vision, natural language processing, and cybersecurity to address real-world challenges in medicine and public health. Themes such as microexpression recognition, EHR clustering, blockchain for IIoT, and flood modeling using CNNs reflect his diverse yet cohesive research vision. Awards for software solutions and research projects as team lead or member (specific names not provided) Dr. Pasha supervises multiple PhD students and leads significant research grants, including projects on microexpression recognition, Alzheimer's prediction, and flood modeling. He collaborates with national and international researchers and contributes to UN Sustainable Development Goals, particularly in health and education. His lab work involves developing intelligent systems for medical and environmental applications. He leads or participates in key research labs and teams focused on AI in healthcare, network security, and sustainable computing. These teams develop frameworks for early disease detection, secure data sharing, and environmental resilience using advanced machine learning and blockchain technologies.
A. Kevin Tang is a Professor of Electrical and Computer Engineering (ECE) at Cornell University, affiliated with the School of ECE. His research focuses on computer networks, control systems, optimization, and information theory. He teaches advanced courses such as ECE 5800 (Control and Optimization of Information Networks) and ECE 6960 (Interplay between Economics and Systems). His work bridges theoretical foundations and practical network applications, including network coding, distributed control, and protocol design. Recent contributions address privacy-preserving data sharing, network routing stability, and optimization techniques for heterogeneous systems. Tang’s publications span top venues like NeurIPS, ICML, NSDI, and IEEE Transactions. His research group explores cutting-edge topics in networked systems, with applications to distributed storage, software-defined networking, and multipath communication protocols. He advises on interdisciplinary projects combining control theory, optimization, and networking. His lab collaborates closely with industry partners to translate theoretical insights into real-world network solutions.
Dr. Eric Chiejina is a Senior Lecturer in Computer Science at the University of Hertfordshire, affiliated with the School of Physics, Engineering & Computer Science. He holds a BTech (Hons) in Physics & Electronics, MSc in Computer Science, and a PhD in Computer Networks and Security. He is a Fellow of the Higher Education Academy (FHEA) and specializes in network security, IoT security, and trust management systems. Education: PhD in Computer Networks and Security, University of Hertfordshire (2011–2015) MSc in Computer Science (Intrusion Detection Systems using Machine Learning), University of Hertfordshire (2008–2009) BTech (Hons) in Physics & Electronics, Federal University of Technology, Owerri (2001–2005) PGCert in Education, University of Hertfordshire (2016–2017) Research Interests: IoT Security and Cyber Security Trust and Reputation in Mobile Ad Hoc Networks (MANETs) & Wireless Sensor Networks (WSNs) Software Defined Networks Artificial Intelligence in Intrusion Detection Quantum Computing Network Security Protocols and Performance Evaluation Grants & Projects: Principal Investigator (PI) for Indigo Tree Digital - KEEP+ (2021–2022) Co-Investigator (CoI) for Indigo Tree Digital - KEEP+ (2021–2022) Focus areas: Key Stakeholder Analysis, Customer Requirements, Software Development Labs/Teams: Engaged in collaborative projects involving interdisciplinary teams focused on network security and IoT applications.
Dr. James Xue is a Senior Lecturer in Computing at the University of Northampton, based at the Technology Centre for Advanced and Smart Technologies. He has been a faculty member since 2010 and is actively involved in teaching, research, and PhD supervision. His academic background includes a PhD and MSc in Computer Science from the University of Warwick, and a BSc (Hons) in Computing and Information Systems from the University of Nottingham, where he graduated with First Class Honours. His research spans distributed systems, database technologies, data mining, digital healthcare, and learning analytics. A significant focus of his recent work is on predictive modeling of student performance using machine learning and deep neural networks in higher education contexts. His research integrates software-defined networking, cognitive load reduction, and intelligent systems for student engagement. The trend in his recent publications (2016–2024) reflects a strong shift toward educational data science, particularly in learning analytics, student performance prediction, and intelligent tutoring systems. Earlier works focus on software-defined networking and distributed systems in smart cities, showing a broad technical foundation. His interdisciplinary work bridges computer science with educational innovation. Dr. Xue supervises PhD research, including James Oakes’ work on virtualized computing workloads. He has contributed to research funded or recognized through media coverage, patent references, and Wikipedia citations. His work has been published in Procedia Computer Science, IEEE, Springer, and Wiley journals/conferences, and has garnered significant attention in academic and public spheres. His teaching leadership includes being the programme leader for BSc/HND Computing (general pathway), where he teaches core modules such as Databases (Levels 6 and 7), Group Project (Level 5), and Computing Dissertation (Level 6), shaping the curriculum and student research experience.
Peter Alexander is a Lecturer at the School of Informatics and Cybersecurity within TU Dublin, where he has taught since 2016 after 11 years as a Cisco technical solutions specialist in industry. A graduate of TU Dublin himself, he holds both bachelor's and master's degrees from the institution and emphasizes "giving back" through education while mentoring students in technical disciplines. His educational background includes: M.Sc. in Computing (Advanced Software Development) - 2:1 Honours, TU Dublin (City Centre Campus) Honours Bachelor of Computer Engineering - First Class Honours, TU Dublin (Blanchardstown Campus) Cisco Certified Network Associate (CCNA) Cisco Networking Academy Certified Instructor – CCNA1, CCNA2, CCNA3, CCNA Security, IT Essentials Specializing in networking, network security, and virtualisation, his research spans cloud computing, Internet of Things (IoT), Software-Defined Networking (SDN), and data visualisation. He has taught diverse courses including databases, web development, and computer systems while supervising multiple master's projects in cyber security, reflecting his industry-academic bridge approach to technical education. No scientific awards were documented in the provided materials. He has supervised several master's projects in cyber security but no research grants or external funding sources were referenced. His teaching philosophy emphasizes student engagement with academic challenges as foundational to professional growth in technology fields.
Jedidiah McClurg is an Assistant Professor in the Department of Computer Science at Colorado State University, with prior faculty appointments at Colorado School of Mines and the University of New Mexico. He received his Ph.D. in Computer Science from the University of Colorado Boulder in 2018, where he was a member of the CUPLV research group under the supervision of Pavol Cerny. His research focuses on programming languages, program synthesis, verification, and their applications in networking, compilers, and distributed systems. His educational background includes an M.S. in Computer Science from Northwestern University (2013) and a B.S. in Electrical Engineering from the University of Iowa (2009). He has completed internships at Microsoft Research (RiSE Group, 2014) and Rockwell Collins (2011, 2013, 2004). McClurg’s research interests include programming languages, formal verification, software synthesis, software-defined networking, compilers, and system security. His work aims to develop tools and techniques that help programmers write more secure, reliable, and efficient code, especially in safety-critical domains. He has led multiple NSF-funded projects, including FMitF and CRII grants, totaling over $1 million in funding. His recent publications span high-impact venues such as PLDI, CAV, DISC, and SOSR, with topics ranging from neural network optimization and regular expression synthesis to network program verification and FEC code generation. These works reflect a consistent trend toward automating correctness, improving performance, and enabling scalable solutions in systems and networking. NSF CRII: SHF: Foundations for Stateful Network Programming ($175,000) NSF FMitF: Game Theoretic Updates for Network & Cloud Functions ($355,000 for him) NSF FMitF: Robust Enforcement of Customizable Resource Constraints ($250,000 for him) NSF GRFP (awarded to student Lauren Baker) He has advised multiple graduate and undergraduate students, many of whom have secured positions at leading tech companies such as Google, Apple, and Amazon. He is actively involved in academic service, having served on program committees for PLDI, SOSR, CAV, and others, and as a reviewer for journals like IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Software Engineering (TSE). He also contributes to open-source research via GitHub and maintains a strong online academic presence.
Louise Helen Crockett is a Senior Lecturer in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. She completed both her undergraduate and postgraduate studies at the same institution and has been a member of the academic staff since 2007, progressing from Research Fellow to Senior Lecturer in 2025. She is an active member of the Strathclyde Software Defined Radio (StrathSDR) research group, where she leads a team of researchers and PhD students, and contributes to multiple industry-facing research projects. Her educational background includes a Doctor of Philosophy (PhD) in Code Division Multiple Access Applied to SpeckNets and a Master of Engineering (MEng) in Electronic & Electrical Engineering with Business Studies (with distinction), both from the University of Strathclyde. Louise's research is centered on the hardware implementation of Digital Signal Processing (DSP) systems for wireless communications, with a focus on Field Programmable Gate Arrays (FPGAs), System on Chip (SoC) devices, and AMD/Xilinx RFSoC technologies. She also works on design methodologies and tools for FPGA-based systems. Her teaching encompasses Hardware Description Language (HDL) design, Simulink-based workflows, and FPGA programming, with an emphasis on practical industry-relevant skills. She has co-authored several books, including Software Defined Radio with Zynq UltraScale+ RFSoC (2023), and develops training materials for broader academic and professional use. Her recent publications reflect a strong trend in FPGA-accelerated signal processing, 5G/6G physical layer implementation, RFSoC applications, and machine learning for modulation classification. These works demonstrate a consistent focus on bridging theoretical algorithms with real-world hardware deployment, particularly in advanced wireless systems and spectrum utilization. She has received the Best Student Paper Award on 29 May 2018. This award was shared with her advisees, highlighting her role in mentoring high-impact research. Louise supervises final-year undergraduate, MSc, and PhD students, and is actively involved in research projects funded by EPSRC, including the Industrial CASE Account and initiatives on spectrum sharing for 5G/6G. She also leads professional training activities, such as short courses on RFSoC and PYNQ, further extending her impact beyond the university. She leads a research team within the StrathSDR group, which focuses on SDR, FPGA-based DSP, and next-generation wireless systems. Her team collaborates on open innovation platforms and contributes datasets and codebases to support reproducible research.
Dr. Wencong Su is a Professor and Chair of the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn , where he leads research in power systems, transportation electrification, and cyber-physical systems. He earned his B.S. (2008) from Clarkson University, M.S. (2009) from Virginia Tech, and Ph.D. (2013) from North Carolina State University. Research Interests: Power and energy systems, renewable integration, electric vehicles, machine learning, and smart grid technologies. Editorial Roles: Associate Editor for IEEE Transactions on Smart Grid , IEEE Access , and IEEE DataPort . His research focuses on optimizing power electronics, enhancing grid stability with distributed energy resources, and applying AI to energy systems. Recent work includes surrogate modeling for converter design, safe reinforcement learning in power grids, and cyber-physical solutions for digital substations. Publications span topics like second-life battery applications, high-frequency AC microgrids, and AI-driven energy management. His articles emphasize machine learning, optimization, and robust control in renewable integration and transportation electrification. Awards include IEEE Fellowships, Top 2% Scientist recognition (Stanford), and multiple IEEE best paper awards. He has secured grants from NSF, DoE, Ford, Toyota, and DTE Energy. Labs operate in the Institute for Advanced Vehicle Systems (IAVS-2060, IAVS-1060, ELB-1026, ELB-1042), focusing on power electronics, smart grid validation, and electrified transportation.
Junho Hong is an Associate Professor at the University of Michigan–Dearborn's Department of Electrical and Computer Engineering, College of Engineering and Computer Science. He holds a PhD in Electrical Engineering from Washington State University (2014), with prior roles at ABB (2014–2019) and Ford Motor Company (2021 sabbatical). His research spans cybersecurity of energy delivery systems, AI applications in power systems, and cyber-physical systems. Doctor of Philosophy in Electrical Engineering, Washington State University, USA (2014) Master of Science in Electrical Engineering, Myongji University, South Korea (2010) Bachelor of Science in Electrical Engineering, Myongji University, South Korea (2008) Junho's research focuses on securing critical energy infrastructure through machine learning, anomaly detection, and advanced cybersecurity frameworks. His work includes projects on substation automation, HVDC systems, smart grids, and high-power EV chargers. He has secured grants from the U.S. Department of Energy, NSF, Ford, and South Korean institutions. Recent publications highlight his contributions to generative AI for anomaly detection, SDN-based cyber restoration, and physics-informed models for secure grid operations. His students have received notable awards, including the Rackham Predoctoral Fellowship and IEEE Best Paper Session. Senior Member, IEEE (2022–present) Associate Editor, IEEE ACCESS (2022–present) 13 US patents in energy cybersecurity He leads the Cyber-Physical System Lab for Energy Delivery Systems, which focuses on grid resilience, renewable integration, and advanced diagnostics.
Prof. Gabi Dreo Rodosek is a full professor and founding director of the CODE research institute at Universität der Bundeswehr München , specializing in Communication Systems and Network Security. She has held leadership roles, including Executive Director of CODE from 2017 to 2021, and her research spans cyberattack detection, AI-driven security solutions, and quantum communication. Executive Director, CODE Research Institute (2017–2021) Chair of Communication Systems and Network Security Co-founder of the Network Security research group Research Focus: Her work addresses advanced persistent threats (APTs), network-based moving target defense, machine learning applications in 5G/6G and IoT security, situational awareness, and software-defined networks. Recent projects emphasize AI-driven risk management and quantum communication resilience. Scientific Recognition: 2025: Full Member, Sigma Xi (Scientific Research Honor Society) 2020: Silver Medal of the City of Neubiberg 2019: Recognized among Europe’s 50 most influential women in cybersecurity 2016: Europe Medal (awarded by State Minister Dr. Merk) 1997: Doctoral Award, Ludwig Maximilian University of Munich Doctoral Supervision: In 2024, she successfully guided three PhD students— Julius Hermelink , Klement Streit , and Nils Rodday —to completion. Nils Rodday earned a joint PhD from Universität der Bundeswehr München and the University of Twente, defended before nine examiners.
