David Palma is an Associate Professor at the Department of Information Security and Communication Technology , Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His research focuses on next-generation networking paradigms, including intent-based networking , knowledge-driven management , and IoT device automation . Key research interests include: Human-centric Internet of Things (IoT) Ontology-based network management Cloud/Edge/Fog computing for IoT Arctic and satellite communication 5G integration in smart grids His recent publications (2024-2019) highlight trends in knowledge graphs for network compliance, XR applications in critical sectors, UAV-based emergency networks , and 5G-enabled smart grid protection . Articles also explore Arctic connectivity via satellite swarms and energy-efficient IoT management.
Zahra Ebrahimi is a researcher in the field of approximate computing, reconfigurable accelerator design, and embedded systems. She joined the Chair of Embedded Systems at Ruhr University Bochum in April 2024, following her PhD research associate role at the Center for Advancing Electronics Dresden (Cfaed) from 2018 to 2024. Her work focuses on energy-efficient hardware/software co-design for edge-to-cloud computing, with applications in neural networks and bio-signal processing. She leads the BMBF-funded project X-DNet , collaborating with Huawei Research Center Munich. Education : B.Sc. and M.Sc. in Electrical Engineering from Sharif University of Technology, Iran Key Projects : ReAp (DFG), Re-Learning (ESF), X-ReAp (DFG), X-DNet (BMBF) Research Interests : Approximate computing, reconfigurable accelerators, embedded systems, SW/HW co-design, energy-efficient edge/cloud computing. Recent Trends : Zahra’s research emphasizes applying approximation techniques to neural networks for 5G/6G applications and designing specialized hardware like CGRAs for bio-signal processing and distributed computing. Collaborations : Academic-industry partnership with Huawei Research Center Munich.
Angelo Feraudo is a Research Fellow at the Department of Computer Science and Engineering of the University of Bologna. He is currently pursuing a PhD in Computer Science, focusing on vehicular computing and service continuity in vehicular networks. His academic path includes a Master's degree in Computer Engineering (University of Bologna) and research experience at the Computer Laboratory of Cambridge University. PhD Researcher (Nov 2021 - present) Research Fellow (Apr 2021 - Oct 2021) Visitor at University of Cambridge (Sept 2020 - Jan 2021) His research intersects emerging technologies like vehicular ad hoc networks (VANETs), vehicular cloud computing, 5G/Mobile Edge Computing (MEC), O-RAN standards, and IoT security. Key projects include: Vehicular computing research (2021-present) Resilient Water Emilia-Romagna dashboard (2021-present) MUD standard extensions for IoT security (2020) Distributed federated learning at Cambridge (2019-2020) Bluetooth vulnerability analysis (2018-2019) Technical expertise spans multiple programming languages ( Java, C, Python, C++, JavaScript ) and systems engineering. Publications address critical IoT security frameworks, federated learning implementations, and device fingerprinting methodologies.
Fabio Favoino is an Associate Professor at the Department of Energy (DENERG) at the Polytechnic of Turin, Italy, and a member of the FULL Interdepartmental Center - Future Urban Legacy Lab. His academic career focuses on building physics and energy systems, with particular expertise in building envelope technologies, energy efficiency, and sustainable building design. Research Interests Professor Favoino's research spans multiple areas of building science and technology, with a strong emphasis on energy performance and sustainable design. His primary research interests include building energy performance and nearly zero-energy buildings, advanced building envelope systems and facade technologies, building insulation materials and responsive building elements, smart glazing and electrochromic window systems, double-skin facades with integrated thermal storage, building simulation and performance assessment methodologies, integration of renewable energy systems in buildings, and thermal comfort and indoor environmental quality. Publication Trends Professor Favoino's recent publications demonstrate a clear focus on advanced building envelope technologies, particularly responsive and adaptive systems. His work increasingly integrates multi-domain analysis, combining thermal, acoustic, and daylight performance assessment. There is a strong emphasis on experimental validation of novel technologies like electrochromic windows, double-skin facades with phase change materials, and smart ventilation systems. His research also shows growing interest in living lab methodologies, sensor networks for building performance monitoring, and the integration of IoT infrastructure for building management systems. Professional Recognition Editorial Board Member for Building and Environment (2022-present) Editorial Board Member for Glass Structures & Engineering (2018-present) Effective Member of the Italian Thermotechnical Association (2018-present) Effective Member of CIBSE, United Kingdom (2016-present) Founding Partner of IBPSA Italy (2012-present) Effective Member of REHVA, European (2011-present) Effective Member of AICARR, Italy (2011-present) Research Leadership Professor Favoino actively supervises PhD students working on cutting-edge building technologies and leads several significant research projects including MIRABLE (2023-2025) on measurement infrastructure for healthy and zero-energy buildings, and the PRIN-funded iclimabuilt project (2021-2025) on functional and advanced insulating materials for climate adaptive building envelopes. He has also led commercial research projects on high-performance glazing systems and participated in the Cost Action TU1403 - Adaptive Facade Network (2014-2018) as coordinator. Research Infrastructure Professor Favoino's work with the FULL Interdepartmental Center - Future Urban Legacy Lab and involvement with the HIEQLab facility provide platforms for interdisciplinary research on sustainable urban development, building technologies, and human-centered environmental quality assessment.
Aurelio Soma is a Full Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, where he has served as a faculty member since at least the early 2000s. His academic career spans over two decades, with continuous involvement in teaching Mechanical Engineering doctoral programs since the 19th cycle (2003/2004). He teaches courses including Machine Construction and Product and Process Design with Numerical Methods. Professor Soma's research spans several critical areas of mechanical engineering with a strong focus on sustainable technologies. His primary research interests include Battery Electric Vehicles, Hybrid Electric Vehicles, Internet of Things (IoT) applications, MEMS technology, Railway Vehicles, and Working Vehicles. His work aligns with Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable cities and communities), and 13 (Climate action), demonstrating his commitment to environmentally conscious engineering solutions. His research bridges theoretical mechanical design with practical industrial applications, particularly in energy harvesting and sustainable vehicle technologies. The analysis of his recent publications reveals a strong trend toward sustainable transportation systems, with particular emphasis on lithium-ion battery technology, energy harvesting for railway diagnostics, and electrification of agricultural machinery. His work consistently combines mechanical engineering principles with cutting-edge sustainable technologies, focusing on practical implementations that address real-world challenges in transportation and industrial systems. The interdisciplinary nature of his research spans mechanical engineering, materials science, electrical engineering, and environmental science. Professor Soma actively supervises PhD students, currently guiding nine doctoral candidates across multiple cycles of the Mechanical Engineering program. His research is supported by numerous competitive funding sources including PNRR Mission 4 (AGRITECH Spoke 6), national research projects, regional innovation centers, and commercial contracts. He has served as Scientific Manager for over 20 research projects spanning from 2006 to 2027, demonstrating sustained research productivity and leadership. His research group focuses on the Design and Experimentation of Industrial and Railway Systems and Microsystems (DIMEAS), developing innovative solutions for energy harvesting, vehicle diagnostics, and sustainable machinery. Professor Soma's work has resulted in numerous patents related to hybrid working vehicles, railway diagnostics systems, energy harvesters, and lithium battery applications, translating academic research into practical industrial solutions.
Tor-Morten Grønli serves as Professor at the Department of Technology, School of Economics, Innovation and Technology, Kristiania University College (Norway). He is also a Visiting Research Scholar at Copenhagen Business School's Department of Information Technology Management and an affiliate of the Center of Business Data Analytics (cbsDBA). Education PhD in Computer Science from Brunel University, London (2011) Master of Technology (with distinction) from Brunel University, London (2007) Research Focus Grønli leads research in context-aware systems, mobile/pervasive computing, and Internet of Things (IoT). He founded/directs the Mobile Technology Lab at Kristiania and has co-authored 70+ publications. Core expertise includes: IoT architecture and applications Machine learning for transport systems Mobile computing frameworks Blockchain-security integration Edge-cloud computing paradigms Publication Trends Recent works (2023-2025) demonstrate strong focus on converging IoT, blockchain, and AI technologies, particularly for intelligent transport and healthcare systems. Dominant themes include federated learning implementations, privacy-preserving architectures, and sustainable edge computing solutions, with increasing emphasis on real-world applications in medical diagnostics and public infrastructure. Professional Activities Founder/Director of Mobile Technology Lab General Chair: Norwegian Conference on ICT Co-organizer: International Conference on Mobile Web Editorial Board: International Journal of Pervasive Computing, Journal of Online Information Review, Computers & Electrical Engineering TPC Member for IEEE BigData, Percom, HICSS, COMPSAC Guest Editor for special issues in Future Generation Computer Systems
Ramy H. Gohary is an Assistant Professor in the Department of Systems and Computer Engineering at Carleton University. His research focuses on advanced wireless communication systems, including machine-to-machine communications, Internet-of-Things (IoT), MIMO systems, convex optimization, differential geometry applications in signal processing, and information-theoretic aspects of multiuser systems. Principal Investigator for Ericsson-Carleton Strategic Partnership projects on 5G+ wireless networks Research emphasizes fairness, cross-layer design, cooperative communications, and jamming-resistant detection techniques His recent work explores direction-of-arrival estimation, non-coherent communication in jamming environments, and distributed MIMO architectures. Publications highlight applications of differential geometry, optimization techniques, and sparse signal recovery in modern wireless systems. He actively contributes to advancements in channel modeling, beamforming, and energy-efficient network design.
Dr. Yomna Abdelrahman is a Professor of Usable Security and Privacy at the Bundeswehr University Munich . Her research focuses on thermal imaging for security and privacy , virtual reality usability, eye tracking , and biometric authentication . She collaborates with researchers like Florian Alt and Albrecht Schmidt on projects exploring how thermal sensing can enhance user identification and privacy awareness . Key Research Areas : Thermal Imaging, Human-Computer Interaction, Virtual Reality, Password Security, Biometric Authentication, Eye Tracking, Privacy Her recent publications address VR emotion detection , password usability , and privacy implications of thermal imaging . She has contributed to conferences like CHI, MUM, and INTERACT, often examining security-privacy trade-offs and non-invasive biometric systems . Notably, her work on thermal attacks reveals vulnerabilities in mobile authentication through thermal residue. She holds a PhD in Computer Science from the University of Stuttgart (2018), where her thesis, "Thermal Imaging for Amplifying Human Perception," laid the foundation for her later work on thermal-based interaction and cognitive load estimation . Her collaborations span diverse domains, from smart home notifications to industrial worker assistance , reflecting her interdisciplinary approach to usable security and interactive systems .
Rafael Duarte Pereira da Silva is a Research Fellow at the Research Group on Engineering and Intelligent Computing for Innovation and Development (GECAD) within the School of Engineering of the Polytechnic Institute of Porto. He is currently pursuing his doctoral degree in Artificial Intelligence and Intelligent Systems Engineering while developing AI-driven solutions for energy communities and smart buildings. His academic background includes: Master's degree in Artificial Intelligence Engineering (2025) from the Polytechnic Institute of Porto, with a dissertation on "Distributed Intelligent Management of Citizen Communities" (grade: 19/20). Bachelor's degree in Computer Engineering (2022) from the Polytechnic Institute of Porto, featuring a green computing project for smart building energy management. Currently enrolled in a doctoral program in Artificial Intelligence and Intelligent Systems Engineering (since 2025). Rafael's research spans Artificial Intelligence, Distributed Computing, and Energy Communities, focusing on human-centric intelligent systems for resource optimization. He develops virtual energy communities using IoT devices and AI models to enable dynamic interaction with community members while managing distributed resources. His work emphasizes sustainability through green computing principles and integration of renewable energy sources in smart infrastructure. His 2024-2025 publications reveal a cohesive research trajectory centered on the Caravels framework for decentralized energy management. Key themes include container-based infrastructure for community resource sharing, peer-to-peer energy trading with storage optimization, and AI-driven demand response systems. His work bridges theoretical AI models with practical IoT implementations, particularly in energy forecasting and HVAC control, demonstrating significant contributions to sustainable consumption in smart buildings and community-scale energy systems. Advising Co-supervised three undergraduate thesis projects at the Polytechnic Institute of Porto on IoT notification systems, Linux-based single-board computer configuration, and open-source IoT devices for smart buildings. Grants and Projects Sa4CPS: Secure situational awareness for critical cyber-physical systems (COMPETE2030-FEDER and ITEA) NGS: New Generation Storage (PRR initiative) TIoCPS: Trustworthy and Smart Communities of Cyber-Physical Systems (P2020 and ITEA programs) As an active GECAD member, Rafael contributes to server infrastructure management and organizes scientific events, including public demonstrations that secured the group's "Excellent" FCT evaluation rating. He has coordinated annual team energizing events for two consecutive years and participated in developing virtual energy community systems that showcase GECAD's research impact in distributed computing and IoT integration.
Jian Liu is an Associate Professor at the School of Computing, University of Georgia, leading the Mobile Sensing and Intelligence Security (MoSIS) Lab. Previously, he served as an Assistant Professor at the University of Tennessee, Knoxville. He holds a Ph.D. from Rutgers University and focuses on Trustworthy AI, Computational Sensing, and Human-Computer Interaction. Current Affiliation: School of Computing, University of Georgia Prior Affiliation: University of Tennessee, Knoxville Education : Ph.D. from Rutgers, The State University of New Jersey Dr. Liu’s research spans Trustworthy AI , Computational Sensing , and Intelligent Fitness Technologies , with publications in top venues like IEEE S&P/Oakland, ACM CCS, and ICML. His work addresses security vulnerabilities in mobile systems, federated learning, and acoustic-based sensing. Recent publications include HarmonyCloak (2025) for AI music copyright protection and mm-RunAssist (2025) for mmWave-based fitness analysis. Research trends emphasize AI ethics , privacy-preserving techniques , and innovative sensor applications . Scientific Awards : IChemE Biochemical Engineering Award (2023) ACM SIGMOBILE Research Highlights (2022) Professional Promise in Research and Creative Achievement Award (2025, University of Tennessee) Best Paper Awards at IEEE SECON (2017) and IEEE CNS (2018) Recognized in Stanford’s Top 2% Most Cited Scientists Dr. Liu mentors students in the MoSIS Lab, with lab members like Yi Wu joining the University of Oklahoma as a Tenure-track Assistant Professor. His research is supported by multiple NSF grants, including CSR and SaTC proposals, and industry partnerships like NVIDIA and Google. Media coverage includes BBC News , MIT Technology Review , and IEEE Spectrum , highlighting his work on AI-driven music protection and smart wearable technologies.
Igor Bisio is a Full Professor at the Department of Naval, Electrical, Electronics, and Telecommunications Engineering (DITEN) at Università di Genova. His research focuses on IoT-driven structural health monitoring, microwave imaging for biomedical applications, and wireless surveillance systems. Teaches courses on telecommunications, IoT, and machine learning Pioneers low-cost IoT solutions for SHM and post-stroke rehabilitation Develops microwave tomography techniques for pediatric stroke diagnostics Advances privacy-preserving Wi-Fi-based crowd monitoring Recent research trends include edge AI integration, compressive sensing for vibration analysis, and multi-class object tracking in aerial scenes. He also explores UAV-based monitoring systems, WiFi fingerprinting for localization, and Banach space inversion models for electromagnetic imaging. His work spans interdisciplinary domains combining electrical engineering, biomedical applications, and wireless network security.
Dr. Xiaoyan Hong is an Associate Professor in the Department of Computer Science at The University of Alabama's College of Engineering. Her research focuses on mobile/wireless networks, vehicular networks, and delay-tolerant systems. Ph.D., Computer Science, University of California-Los Angeles (2003) M.S., Computer Science, Zhejiang University (2000) Research spans Internet of Things (IoT) , Connected Vehicles , and Underwater Wireless Networks . Key projects include NSF-funded underwater robot communication infrastructure and smart traffic light systems. Recent work explores Named Data Networking (NDN) in vehicular environments, Task Synchronization for autonomous vehicles, and V2I Communication for traffic optimization. NSF Research Experience for Undergraduates (REU) grant recipient $1.5M NSF grant for underwater robotics networking Her research integrates with multiple engineering centers, including the Center for Advanced Vehicle Technologies and Center for Transportation Operations .
Chen Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. His research focuses on energy-harvesting embedded systems, low-power computing, and IoT network optimization through machine learning techniques. Ph.D. from University of Pittsburgh Specializes in transient computing for batteryless devices Develops reinforcement learning solutions for UAV-assisted IoT systems Chen's recent publications emphasize energy-aware scheduling, non-volatile memory optimization, and sustainable communication protocols. His work intersects spatiotemporal modeling, fault tolerance, and resource-constrained AI execution across heterogeneous architectures.
Professor Vasilis Katos is a Professor in Cyber Security at Bournemouth University, specializing in digital forensics, incident response, and intellectual property security. With over 160 publications and extensive industry experience as an Information Security Consultant, he serves as an expert witness in criminal courts in both the UK and Greece. His research has significant impact in digital forensics for intellectual property infringement investigations through collaborations with the EU Intellectual Property Office (EUIPO) and UNICRI. Professor Katos holds a Diploma in Electrical Engineering from Democritus University of Thrace, an MBA from Keele University, and a PhD in Computer Science (network security and cryptography) from Aston University. He is a certified Computer Hacking Forensic Investigator (CHFI) with extensive practical experience in cybersecurity. His research primarily focuses on digital forensics and incident response, with recent emphasis on intellectual property infringement investigations, IoT security, blockchain applications, and traffic prediction systems. His work bridges academic research with practical security applications, particularly in intellectual property protection and smart city security frameworks. Professor Katos has coordinated significant research projects including Illegal IPTV in the European Union and IP Infringement on online trading platforms, funded by the EUIPO Observatory. His extensive publication record shows strong trends in digital forensics, cybersecurity for intellectual property protection, IoT security, and increasingly in smart city security frameworks. The research demonstrates a progression from foundational cybersecurity work to specialized applications in intellectual property protection and circular economy security models, with recent incorporation of AI and machine learning techniques for threat intelligence and traffic prediction. Certified Computer Hacking Forensic Investigator (CHFI) Editorial Board Member of Computers & Security Journal Professor Katos has successfully supervised multiple PhD students including Amalia Damianou (Digital Forensics in Smart, Circular Cities), Christos Iliou (Machine Learning Based Detection and Evasion Techniques for Advanced Web Bots), and Mohammed Al Qurashi (Intrusion Detection for IoT). He has secured significant research funding including the ECHO project (European network of Cybersecurity centres), IDEAL-CITIES, and multiple EUIPO-funded initiatives focusing on intellectual property protection. His work connects with the Centre for Intellectual Property Policy & Management (CIPPM) at Bournemouth University, where he contributes to research on intellectual property in emerging technologies. Professor Katos maintains active collaborations with international organizations including UNICRI and the EU Intellectual Property Office, focusing on practical applications of digital forensics in intellectual property protection.
Maurizio Bevilacqua serves as a Full Professor in the Department of Industrial Engineering and Mathematical Sciences at the University of Ancona (Università Politecnica delle Marche). His academic focus falls under the scientific sector IIND-05/A - Impianti industriali meccanici (Mechanical Industrial Plants). Based at the university's Engineering faculty located at Via Brecce Bianche in Ancona, Italy, Professor Bevilacqua maintains an active research profile with numerous publications spanning industrial engineering, digital transformation, and smart manufacturing technologies. Professor Bevilacqua's research interests center on cutting-edge industrial engineering topics including Digital Twin technology, Industry 4.0 implementation, smart retrofitting of industrial machinery, maintenance engineering, and robotics applications in manufacturing. His work demonstrates particular expertise in applying these technologies to challenging sectors such as oil and gas, food manufacturing, and maritime transportation. His research bridges theoretical innovation with practical industrial applications, as evidenced by his numerous case studies across different manufacturing sectors. An analysis of his recent publications (2023-2025) reveals a strong emphasis on digital transformation in industrial settings, with particular focus on Digital Twin implementations across various sectors. His work shows a progression from foundational Industry 4.0 concepts toward more sophisticated applications including Digital Triplet frameworks and human-machine integration approaches that anticipate Industry 5.0 paradigms. Many of his studies combine multiple advanced techniques such as machine learning, fuzzy cognitive maps, and association rule mining to solve complex industrial problems. Professor Bevilacqua's research demonstrates strong industry collaboration, with numerous case studies conducted in real industrial settings across multiple sectors including oil and gas, food manufacturing, and maritime transportation. While specific grant information isn't provided in the available materials, his extensive publication record suggests active participation in research projects that bridge academic theory with practical industrial implementation. His work frequently addresses challenges related to legacy system modernization, operational resilience, and sustainable manufacturing practices. Though specific laboratory affiliations aren't detailed in the available information, Professor Bevilacqua's research appears to focus on industrial applications of digital technologies, suggesting collaboration with industrial partners and possibly university research centers focused on manufacturing innovation, robotics, and industrial IoT. His work on smart retrofitting solutions indicates involvement with projects that transform conventional machinery into intelligent systems capable of integration within modern digital manufacturing ecosystems.
