Dr. Krishnendu Guha is an Assistant Professor and CONNECT Funded Investigator at the School of Computer Science and Information Technology, University College Cork. His research bridges embedded systems, cybersecurity, and quantum-safe hardware design with AI and bio-inspired strategies. PhD: University of Calcutta (Department of Science and Technology, Government of India) Postdoctoral: University of Florida Past Roles: Research Fellow at Intel India, Visiting Scientist at Indian Statistical Institute, Temporary Assistant Professor at NIT Jamshedpur His research focuses on embedded systems security , real-time security mechanisms , and quantum-safe hardware . He integrates AI (e.g., neural networks) and bio-inspired strategies (e.g., gecko crypsis behavior) into security frameworks for FPGAs and edge platforms. Recent publications highlight trends in blockchain for supply chains , quantum machine learning , secure FPGA architectures , and distributed AI systems . His work addresses energy efficiency, fault detection, and decentralized security in hardware. As a CONNECT Centre member, Dr. Guha contributes to advanced research in reconfigurable systems and cybersecurity. Grants and collaborations span quantum-safe design, cloud FPGA security, and hardware trojan mitigation.
Maurizio Martina is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He is a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center and serves as an Associate Editor for the IEEE Transactions on Circuits and Systems I (2018-2023). His research focuses include: Digital circuits and signal processing Machine learning hardware architectures RISC-V extensions and post-quantum cryptography VLSI design for edge computing and IoT Recent publications emphasize cryptographic hardware implementations (CHIMERA, Keccak co-processors), RISC-V integration methodologies, and privacy-preserving neural network frameworks. His work spans VLSI architectures for video processing, bio-inspired electronics, and error correcting codes, with applications in cybersecurity, robotics, and biomedical systems. Scientific Recognition : Premio Nazionale Innovazione (2013) Premio dei Premi (2014) He supervises 12 PhD students across cycles 35-40 in Electrical, Electronics and Communications Engineering, including: Valeria Piscopo (2024-in progress) Alessandra Dolmeta (2022-in progress) Luigi Giuffrida (2022-in progress) Walid Walid (2019-2023) As part of the VLSILAB Group , his research explores hardware accelerators for machine learning, post-quantum cryptography on RISC-V, and bio-inspired embedded systems. Teaching activities include courses on Integrated Systems Architecture and Hardware & Wireless Security at Politecnico di Torino and Università di Pavia.
Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.
Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.
Hoda Hassan is an Associate Professor in the Department of Information Sciences and Technology at George Mason University. Her work bridges theoretical and applied domains in computer networks, Mobile Ad Hoc Networks (MANETs), and the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Education : PhD in Computer Engineering from Virginia Tech (2010), MSc and BSc in Computer Science from the American University in Cairo (2005, 1992). Research Interests focus on designing intelligent and secure network systems. She has developed innovative frameworks, including a BLE computer-aided design toolkit for MANETs, which was commercialized by Skymind Malaysia. Her work spans AI integration in IoT, anomaly detection, cloud computing models, and network architecture evolution. Academic Leadership includes pioneering roles in founding The Knowledge Hub (TKH) Universities in Egypt and leading the Computing and Computer Science Program at Coventry University's UK offshore branch (2019–2022). Her research has been supported by a significant 2 million Egyptian Pound grant from ITAC Egypt (2015–2017).
Professor Rajkumar Roy is Executive Dean at City St George's, University of London, following leadership roles at Cranfield University including Director of Manufacturing (2014–2019) and Professor (2005–2019). He holds a PhD in Computing from the University of Plymouth (UK) and BEng/MEng in Production Engineering from Jadavpur University (India). His career spans engineering at Tata Motors, pioneering research in Through-life Engineering Services (TES) with Rolls-Royce, BAE Systems, and others, and founding the TES Centre at Cranfield. PhD: Computing, University of Plymouth (1997) MSc: Intelligent Systems, University of Plymouth (1993) MEng: Production Engineering, Jadavpur University (1992) BEng: Production Engineering, Jadavpur University (1987) Professor Roy’s research focuses on Through-life Engineering Services , Cost Engineering , and Product-Service Systems . He advocates design thinking in higher education, co-founded the C4D Centre with £5.5m funding, and explores digital twins, augmented reality in maintenance, and evolutionary algorithms for optimization. His work bridges engineering, technology, and management to enhance industrial competitiveness. Recent publications highlight trends in the Industrial Metaverse , Augmented Reality for remote assistance, and Uncertainty Quantification in engineering systems. His 2023–2025 articles address laser peening for turbine alloys, self-engineering turbines, and AR authoring tools. Scientific recognition includes: Fellow of CIRP, Institution of Engineering Designers, and Higher Education Academy President of ACostE (2008–2010) Founding Editor-in-Chief of Elsevier's Applied Soft Computing Leadership in EPSRC, EU, and industry-funded projects totaling over £50m Professor Roy has supervised 28 PhD/MSc students, including Nicolau Morar (2018) and Evaggelos Lavdas (2008), and secured major grants like the EPSRC Platform Grant on TES (£1.3m cash + £1m in-kind) and the Atkins-Cranfield Chair (£250K). His work with Rolls-Royce, BAE Systems, and MoD underscores his industry impact.
Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.
Prof. Dr. Renato Negra is a faculty member at RWTH Aachen University, serving as the Chair of High Frequency Electronics within the Faculty of Electrical Engineering and Information Technology. His research is centered on advanced electronic systems with a focus on reconfigurable and low-power architectures for real-time applications. Research Interests: His work spans high frequency electronics, neuromorphic computing, embedded systems, and cyber-physical systems. He develops FPGA-based and edge-computing solutions for computer vision, robotics, and smart infrastructure, particularly in elderly monitoring and autonomous navigation. His research integrates deep learning with hardware optimization for energy efficiency and real-time performance. The recent publications highlight a strong trend toward event-based vision , neuromorphic sensors , and low-power embedded AI , applied in domains such as smart cities, healthcare, and robotics. There is a consistent emphasis on real-time processing, reconfigurable systems, and the deployment of neural networks on constrained hardware platforms. Scientific Awards: No awards or honors were mentioned in the provided text. Advising and Grants: While no specific students or advising roles are listed, the volume and depth of publications suggest active supervision or collaboration within research projects. Although no grants are explicitly named, involvement in EU-level initiatives (e.g., FitOptiVis ECSEL Project) and national R&D programs (e.g., BIO-PERCEPTION) can be inferred from the research topics and publication contexts. Labs and Teams: Prof. Negra leads the research activities in High Frequency Electronics at RWTH Aachen. While not directly linked to the Computer Vision and Robotics Lab (CVR-Lab) mentioned in the text, his work aligns closely with neuromorphic and CPS research themes, suggesting potential interdisciplinary collaboration.
Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.
Stephanie Forrest is a Professor of Computer Science at Arizona State University and serves as Director of the Biodesign Center for Biocomputation, Security and Society . She holds affiliations with the School of Computing and Augmented Intelligence , Global Futures Laboratory , and Santa Fe Institute External Faculty . Education: B.A. from St. John's College M.S. and Ph.D. in Computer Science from the University of Michigan Research Interests: Forrest specializes in the intersection of biology and computation , with key contributions to cybersecurity (anomaly detection, instruction-set randomization), automated software repair (evolutionary methods), and biological modeling (immune systems, SARS-CoV-2 spread). Her work bridges complex adaptive systems , AI/ML , and defense applications . Scientific Contributions: 2020 IEEE S&P Test of Time Award 2019 ICSE Most Influential Paper Award 2011 ACM/AAAI Allen Newell Award NSF Presidential Young Investigator (1991) IEEE Fellow Evolutionary Computation Pioneer award Publications & Grants: Her research appears in top venues (ICSE, IEEE S&P, PNAS) and is funded by the National Science Foundation , DARPA , Air Force Research Lab , and Santa Fe Institute . Key projects include Crispy (CRISPR-inspired DoS defense), GenProg (automated bug correction), and SIMCoV-GPU (agent-based pandemic modeling).
Dr. Olakunle Olayinka is a Senior University Teacher and Deputy School Director of Education in the Department of Computer Science at the University of Sheffield. He is a member of the Institute of Coding and has over a decade of experience in IT and academia. Prior to his current role, he worked as a Research Assistant at the University of Gloucestershire, teaching Cybersecurity and related modules. He holds a PhD in Computing from the University of Gloucestershire, alongside an MSc in Computer Forensics and BSc in Computer Science from Babcock University and University of Glamorgan respectively. His research focuses on Cybersecurity & Gamification , Digital Forensics , and Information Systems Adoption . He has contributed to studies on generative AI in education, cybersecurity training methodologies, and digital transformation strategies for small businesses. Recent work includes exploring AI-driven approaches to web application security and IoT threat hunting using bio-inspired models. His publications span journals like Sustainability and Journal of Applied Learning & Teaching , with a focus on bridging educational technology gaps and enhancing cybersecurity practices. He has co-authored book chapters on digital transformation in Nigerian enterprises and cybersecurity frameworks. Teaching responsibilities include Cybersecurity Team Project, Cyber Threat Hunting, and Software Engineering modules. He advocates for project-based learning approaches, as demonstrated in his work on Agile methodologies in software projects.
Shahid Hussain is an Associate Professor at King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical and Mathematical Sciences and Engineering Division. His research spans multiple domains including electric vehicle infrastructure, blockchain technology, and intelligent systems for IoT applications, with a focus on practical implementations of computational intelligence techniques. Dr. Hussain's research interests include: Electric Vehicles and Smart Grid Integration Fuzzy Logic and Intelligent Decision Systems Blockchain Applications for Security and Privacy Machine Learning for IoT and Healthcare Applications Energy Management Systems Smart City Infrastructure Development His recent publications demonstrate a strong focus on applying hybrid computational approaches to solve complex engineering problems, particularly in transportation systems and healthcare applications. Dr. Hussain has published extensively in IEEE journals, with particular emphasis on innovative approaches to electric vehicle charging infrastructure, secure IoT systems, and blockchain-enabled solutions for real-world challenges. Dr. Hussain maintains active collaborations with researchers globally, particularly with Reyazur Rashid Irshad, Young-Chon Kim, and Subhasis Thakur. His work bridges theoretical advancements with practical implementations, as evidenced by his research on fuzzy integer linear programming for EV charging stations and blockchain-enabled security frameworks for medical IoT systems.
Dr. Rand Raheem is a Lecturer in Computer Science at Middlesex University's Faculty of Science and Technology, London. She specializes in wireless communication systems, vehicular networks, and interference management in future mobile networks. Her research focuses on improving performance in safety-critical wireless sensor networks and 4G/5G/6G technologies. Education: Diploma in Electrical and Computer Engineering (Dhofar University, Oman, 2009) BSc Computer Communication Engineering (Dhofar University, 2010) MSc Telecommunication Engineering (Middlesex University, 2011) PhD on interference management in small cell networks (Middlesex University, 2016) Research Interests: Mobile and vehicular network optimization Bio-inspired algorithms for network scheduling Dependable safety-critical systems Cooperative femtocell technology Machine learning applications in wireless networks Teaching: CST4540 - Network Management CST3570 - Network Management and Disaster Recovery CST2560 - Project Management and Professional Practice CST2531 - Compliance and Project Management CST1500 - Computer Systems Architecture and Operating Systems Key Research Trends: Recent work emphasizes bio-inspired algorithms (bird flocking, bat algorithms) and neural networks for enhancing wireless sensor network dependability. Earlier research focused on femtocell interference management in LTE networks and vehicular environments. Advising & Grants: No specific grants or supervisees listed. Active collaborator with researchers like Dr. A. Lasebae on multiple projects. Labs/Teams: Participates in network research initiatives at Middlesex University's Computer Science Department, though specific lab affiliations are unspecified.
Lei Wang is the F. L. Castleman Associate Professor in Engineering Innovation at the University of Connecticut's School of Engineering, Department of Electrical and Computer Engineering. He holds a PhD from the University of Illinois at Urbana-Champaign (2001), an MS (1996) and BS (1992) from Tsinghua University, China. His research focuses on cyber-physical systems , embedded computing with renewable energy , and nanoscale integrated circuit design . Key areas include microbial fuel cells , memristor-based hardware security , and low-power signal processing architectures . Recent work trends involve Quantum-dot transistor applications for in-memory computing Adaptive LDPC decoder optimization Hardware security leveraging memristor properties Energy-efficient power management systems for underwater sensors Scientific recognition includes National Science Foundation CAREER Award (2010) F. L. Castleman Term Professorship in Engineering Innovation Professional roles encompass editorial and committee positions at IEEE and ACM journals. His work spans interdisciplinary domains in renewable energy integration , VLSI design , and bio-inspired computing systems .
Nane Kratzke is a Professor at Lübeck University of Applied Sciences, specializing in cloud computing and cloud-native applications. His research addresses practical challenges in container orchestration, cloud security, and vendor lock-in for small and medium enterprises. He holds a Diplom in Computer Science and a Doctorate in Natural Sciences, though specific institutions are not documented in available sources. Research interests include cloud-native architecture design, Kubernetes orchestration, moving target defenses for cloud security, and cost modeling of cloud services. His work bridges academic research and industry needs, particularly for SMEs seeking cloud portability through multi-cloud strategies and runtime transferability. Analysis of recent publications (2022-2024) reveals a strategic shift toward AI-driven cloud management techniques like prompt engineering, building on foundational contributions in cloud observability, security mechanisms, and transferability frameworks established between 2016-2021. Key recurring themes include mitigating vendor lock-in and enabling seamless application migration across cloud environments. No scientific awards are documented in the provided information sources. Details regarding graduate student advising, research grants, and laboratory facilities are not specified in current datasets, though his publications on programming assessment tools indicate engagement with computer science education.
