Morten Opprud Jakobsen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University. His research focuses on condition monitoring of rotating machinery , with special emphasis on lubrication condition detection using acoustic signals and implementation of Embedded Machine Learning for predictive maintenance. Primary Research Areas: Embedded Systems, Tiny Machine Learning, Machine Diagnostics Key Techniques: MEMS sensors, Acoustic Emission Analysis, LoRa/Bluetooth Low Energy communication Recent work demonstrates technical expertise in developing low-cost condition monitoring systems that combine vibration and acoustic analysis for early failure detection in mechanical components. Publications in Mechanical Systems and Signal Processing and Tribology International highlight interdisciplinary applications of machine learning in mechanical engineering. Contact: morten@ece.au.dk | +45 21 36 70 53
Mennan Selimi is a Full Professor at the Faculty of Contemporary Sciences and Technologies at South East European University (SEEU). His research focuses on Federated Learning, Edge Computing, Wireless Mesh Networks, and Decentralized Systems, with applications in IoT and resource-constrained environments. Position: Full Professor Affiliation: Faculty of Contemporary Sciences and Technologies, SEEU Contact: m.selimi@seeu.edu.mk Research Trends: Professor Selimi's work emphasizes adaptive machine learning frameworks for low-capacity devices, integration of LoRa technology in mesh networks, and decentralized edge infrastructure management. His recent publications highlight innovations in federated learning algorithms for wireless environments and experimental platforms for distributed AI research. Collaborations: He actively collaborates with researchers like Felix Freitag, Leandro Navarro, and Joan Miquel Sole across institutions in Italy, Montenegro, and Spain. His projects include the CityLab Testbed and LightKone Reference Architecture.
Dr. Timenko Artur Valentynovych serves as Senior Lecturer at Zaporizhia National Technical University's Department of Computer Systems and Networks within the Faculty of Computer Science and Technologies. Holding a specialist degree in Computer Systems and Networks (2010), he maintains active roles in both teaching and research. His educational background includes graduation from Zaporizhia National Technical University in 2010 with specialization in Computer Systems and Networks. Professional development is evidenced through continuous research output and curriculum development activities. Research focuses on Internet of Things , computer networks , and neural networks , with particular emphasis on protocol verification, device interoperability, and embedded system optimization. Recent work explores semantic chatbots for IoT management, air quality monitoring systems, and MQTT protocol compatibility analysis. His methodology integrates formal verification techniques with practical hardware implementation. Publication trends from 2020-2024 reveal consistent contributions to IoT infrastructure (45%), network protocols (30%), and AI applications (25%). Key journals include Shipbuilding & Marine Infrastructure and Scientific Notes of Vernadsky University. Research demonstrates strong industry relevance with applications in smart homes, environmental monitoring, and critical systems. Teaching responsibilities encompass Python programming basics, computer network design, IoT fundamentals, and wireless technologies. His laboratory guidelines for Embedded Computer Systems and IoT disciplines reflect practical, hands-on pedagogy. Current projects involve developing automated temperature control systems and network anomaly detection using hybrid neural networks. Professional activities include active participation in Ukrainian academic conferences and international collaborations through ZNTU's research infrastructure. His work contributes to the university's strategic focus on digital innovation and sustainable technology development.
Susanna Pirttikangas (D.Sc.(tech.), M.Sc.(math)) is a Research Director at the University of Oulu's Faculty of Information Technology and Electrical Engineering , affiliated with the Center for Ubiquitous Computing and the Interactive Edge research group. She actively collaborates across research units within her faculty. Research Interests : Edge Intelligence Machine Learning Artificial Intelligence Smart Environments 6G Wireless Networks Scientific Contributions : Her recent work focuses on 6G-enabled AI architectures , federated learning , privacy-preserving systems , and urban well-being analytics . Articles highlight applications in industrial metaverse integration, fault diagnosis, and mobility-as-a-service.
Professor Sema Kayhan is a faculty member at Gaziantep University , Faculty of Engineering , Department of Electrical and Electronic Engineering . She has held the academic rank of Professor since 2021, after progressing from Associate Professor (2016-2021) and Assistant Professor (2007-2015). Her career began at Gaziantep University as a Research Assistant (1996-2005) and includes administrative roles as Vice Head of Department (2013-2015). Doctorate: 1999-2005, Gaziantep University Institute of Science, Electrical and Electronic Engineering Degree: 1996-1999, Gaziantep University Faculty of Engineering, Electrical and Electronic Engineering & Computer Science Licence: 1991-1996, Gaziantep University Faculty of Engineering, Electrical and Electronics Engineering (English) Her research spans Image Processing , Compressive Sensing , Machine Learning , and Biomedical Signal Processing , with notable contributions to ECG signal reconstruction , chaotic encryption , and FPGA-based hardware acceleration . She has supervised 23 theses, including PhD works on hybrid deep learning for schizophrenia detection , sparse extreme learning machines , and smart home solar energy systems . Her 15 most recent publications (2015-2022) demonstrate expertise in image watermarking , encryption , antenna design , and ECG signal processing , often leveraging chaos theory , extreme learning machines , and FPGA implementations . She has also contributed to NLP for Arabic-alphabet languages and retinal blood vessel segmentation via transfer learning. Professor Kayhan's projects include renewing jacquard control systems (2007-2008) and ECG signal reconstruction via compressive sensing on FPGA (2016-2017). She co-designed a smart home model using solar energy (2017) and has been a Member of the Chamber of Electrical Engineers since 1997.
Dr. Mandar Gogate is a Senior Research Fellow at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. He actively contributes to the Centre for Artificial Intelligence and Robotics, focusing on multimodal signal processing and AI applications. Research Themes: Audio-Visual Speech Enhancement, Green AI, Data Privacy, Hearing Aid Technology, Climate Modeling Collaborations: Prof. Amir Hussain, Dr. Kia Dashtipour, Prof. Ahmed Al-Dubai His research explores audio-visual speech enhancement for hearing aids, leveraging deep learning and fuzzy logic. He investigates green AI techniques like neural network pruning for energy efficiency and develops privacy-preserving frameworks using thermal imaging. His work spans climate data analysis with partial least squares and underwater image enhancement via dimension decomposition transformers. Recent publications include 2026 surveys on ensemble malware detection and 2025 studies on cognitive load-driven speech enhancement . He has contributed to federated learning for market surveillance and multimodal hearing aid projects. As a second supervisor for Idrees Hasan's research on COG-MHEAR hearing aids, he mentors emerging scholars. His grants include £3.25M from EPSRC for the COG-MHEAR project (2021-2026) and £12k from Royal Society for multilingual speech enhancement studies. He works with the Centre for Artificial Intelligence and Robotics and Centre for Distributed Computing , integrating 5G-IoT systems into assistive technologies. His technical background includes compiler design, embedded systems, and wireless sensor networks from earlier projects like gesture mice and Hadoop-based rule mining.
John Bush Idoko is an Assistant Professor in the Computer Engineering Department at Near East University, North Cyprus. He earned his BSc in Computer Science from Benue State University, Nigeria (2010), followed by MSc (2017) and PhD (2020) in Computer Engineering at Near East University. His research focuses on machine learning , computer vision , and signal processing , with applications in IoT systems , healthcare devices , and smart infrastructure . He has contributed to 15 recent publications spanning topics such as UAV routing protocols, image encryption, and medical prosthetics. BSc: Computer Science, Benue State University (2010) MSc: Computer Engineering, Near East University (2017) PhD: Computer Engineering, Near East University (2020) His work emphasizes deep learning optimization , chaotic encryption , and intelligent sensor networks . Recent projects include IoT-based infant monitoring, sleep apnea detection, and voice-controlled prosthetics. He is affiliated with the Applied Artificial Intelligence Research Centre and leads the Cyber Security Engineering Department.
Bin Li is an Associate Professor at the School of Electrical Engineering and Computer Science. His research focuses on wireless networks, network scheduling, sufficient dimension reduction, and statistical inference. NSF-funded projects: EAGER: TaskDCL, CAREER: Wireless Collaborative Mixed Reality Networking, CNS Core: Scalable Algorithms for Virtual Reality Over Wireless Networks. Grants include foundational work in AI-driven task training, geospatial digital twins, and joint communication-computation-learning systems. His research spans wireless scheduling algorithms, data freshness optimization, and nonlinear sufficient dimension reduction. Recent work explores Fréchet regression, functional graphical models, and kernel-based hypothesis testing. Articles highlight interdisciplinary applications in computer science, statistics, and mathematics. Statistical methods dominate his contributions, including Bayesian credible sets, copula models, and additive independence frameworks. Collaborations extend to multi-source genomic data analysis and immersive educational platforms via augmented reality. With an h-index of 16 and 74 research outputs, Bin Li’s expertise intersects wireless network optimization and statistical learning. His work addresses challenges in edge computing, cloud offloading, and cyber-physical systems through algorithmic innovation and theoretical rigor.
Eduardo Alonso Rivas is an Assistant Professor (Doctor Level 3) in the Department of Electronics, Automation and Communications at the Higher Technical School of Engineering (ICAI) , Comillas Pontifical University , Madrid. With 13 years of teaching experience and recent appointment as Professor in 2023, he specializes in electronic systems, biomedical engineering, and instrumentation. Education PhD in Industrial Engineering, Comillas Pontifical University, 2023 MSc in Engineering Systems Modeling, Comillas Pontifical University, 2015 Industrial Engineering Degree, Comillas Pontifical University, 2009 Degree in Automation & Industrial Electronics, Comillas Pontifical University, 2009 Research Interests Dr. Alonso Rivas focuses on the convergence of electronics and biomedical applications. His work spans intraoperative neurophysiological monitoring systems , fast-settling biosignal amplifiers , skin–electrode interface characterization , and portable impedance-based health monitors . He also investigates power-line communication strategies for smart-grid metering, illustrating a dual expertise in clinical electronics and industrial communications. Publication Focus Across seven peer-reviewed articles (2014-2022), his research follows two dominant threads: (1) biomedical instrumentation —designing wireless, low-noise devices for real-time neural monitoring and diagnostics; (2) power-line communications —optimizing latency and polling strategies in PRIME-based smart-meter networks. This duality underscores a commitment to both cutting-edge healthcare technology and sustainable energy systems. Scientific Awards No specific awards or fellowships are listed in the provided text. Teaching & Supervision Since 2010 he has taught Electronics , Digital Electronics , Microprocessors , Electronic Systems , and Biomedical Instrumentation , as well as supervising Master theses. He progressed from Assistant Collaborator to full Professor in 2023, demonstrating sustained pedagogical commitment. Laboratory & Team Affiliation While explicit laboratory names are not provided, his affiliation with the Department of Electronics, Automation and Communications and prior research at the Technological Research Institute suggests active participation in instrumentation and communications laboratories within ICAI.
Fabrice Theoleyre is a Research Director at CNRS , affiliated with ICUBE UMR 7357 at the University of Strasbourg . He leads the Network Team , focusing on wireless networking, Industrial IoT, and cybersecurity. His research spans low-power networks, 6TiSCH protocols, and AI-driven network design. Education: Habilitation à Diriger des Recherches (HDR) in 2014. Students: Supervised 8 PhD/Master’s students, including Fatemeh Stodt, Amine Falek, and Rodrigo. Research Interests include Industrial IoT, wireless mesh networks, digital twins, and cybersecurity. His work addresses network reliability, protocol optimization, and non-terrestrial communication (e.g., satellite, UAVs). Recent projects involve ANR DONUTS (2024) and an International Emerging Action (2025) with South Korean collaborators. Scientific Awards : Best Paper Award at Adhocnow'16 Elevation to IEEE Senior Member in 2016 Service & Leadership: Organized summer schools, workshops (e.g., EWSN 2020), and special issues. Co-chaired conferences like IEEE ISCC 2024 and GDR RSD (2024). Actively recruits PhD/postdoc candidates for projects on AI-powered mesh networks and IIoT security.
Karl-Erik Årzén serves as Professor and Head of the Department of Automatic Control at Lund University's Faculty of Engineering. He concurrently holds the position of Co-director for the Wallenberg AI, Autonomous Systems and Software Program (WASP) and maintains Fellow status with the Royal Swedish Academy of Engineering Science (IVA), alongside advisory roles at SMaRC and Aalto University. His research operates at the convergence of control theory and computer engineering, specializing in dynamic feedback-based resource management (feedback computing) for embedded systems and cloud infrastructures. Additional expertise spans embedded control, real-time systems, cyber-physical systems, and domain-specific programming languages for control applications, demonstrating consistent bridging of theoretical control frameworks with computational implementation challenges. Recent publication trends reveal intensive focus on applying control methodologies to distributed resource allocation, evidenced by works on real-time application offloading, auction-based storage allocation, and reinforcement learning-driven cloud auto-scaling. These contributions critically address scalability and performance challenges across computer science, telecommunications, and next-generation 6G network architectures. Scientific recognition includes: Best Paper Award (2018) for Predictability and Cloud Application research Best paper award (2018) Best Paper Award at RTNS 2016 Best Paper Award at RTCSA 2004 With 35 supervised students to date, Årzén currently serves as primary supervisor for PhD candidate Ahmed Al Bayati. His research portfolio includes active leadership in the Vinnova-funded AORTA project (2023-2025), Robust and Secure Control over the Cloud initiative (2021-2026), and the long-term WASP program (2015-2029), alongside completed projects like AutoDC and Testing Autonomous Control-Based Software Systems. He actively contributes to the ELLIIT research environment and shapes Lund University's AI and Digitalization profile area alongside LU's Natural and Artificial Cognition initiative.
Chenyang Lu is a Professor in the Department of Computer Science and Engineering at Washington University in St. Louis, serving as Editor-in-Chief of ACM Transactions on Sensor Networks and Associate Editor of Real-Time Systems. He has held leadership roles as Program Chair for IEEE Real-Time Systems Symposium (RTSS 2012), ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2012), and ACM Conference on Embedded Networked Sensor Systems (SenSys 2014). His academic credentials include: Ph.D. in Computer Science from University of Virginia (2001) M.S. in Computer Science from Chinese Academy of Sciences (1997) B.S. in Computer Science from University of Science and Technology of China (1995) Research focuses on real-time systems, wireless sensor networks, cyber-physical systems, and Ambient Assisted Living, with over 100 publications generating 10,000+ citations and an h-index of 45. His work bridges theoretical foundations with practical applications in sensor network deployments and cyber-physical infrastructure. Professional service includes editorial leadership in top-tier publications and conference organization, reflecting significant community impact. No information regarding student advising or grant funding was provided in the source material.
Prof. Sooksan Panichpapiboon is a Full Professor at King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand. With a Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, she has pioneered research in Intelligent Transportation Systems and Vehicular Ad Hoc Networks. Her work bridges theoretical advancements with real-world applications, including mobile traffic sensing and urban mobility optimization. Bachelor Degree: B.S. in Electrical and Computer Engineering, Carnegie Mellon University, USA Master Degree: M.S. in Electrical and Computer Engineering, Carnegie Mellon University, USA Doctoral Degree: Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University, USA Her research focuses on Intelligent Transportation Systems , Mobile Sensing , and Wireless Network Design , with applications in taxi supply-demand modeling, lane change detection, and vehicular connectivity. She has published extensively on these topics in IEEE journals and conferences. Her publications demonstrate leadership in urban mobility analysis , sensor network optimization , and data-driven transportation solutions , with recent works on taxi distribution modeling and spatiotemporal analytics. Scientific Awards: Siew Karnchanachari Award 2015 Teaching Excellence Award 2013 Dissertation Award 2011 (National Research Council of Thailand) Best Paper Award from ECTI-CON 2021 She has mentored multiple graduate students and served on technical program committees for IEEE conferences including VNC, VTC, and ICC. Her lab at KMITL (Room 534) focuses on vehicular networks and mobile sensing technologies.
Douglas A. Buchanan, Ph.D., P.Eng., FCAE, is Professor of Electrical and Computer Engineering at the University of Manitoba and a founding member of the Microelectronics and Nanotechnology Research Group. A Canada Research Chair (Tier II) in Microelectronic Materials from 2003-2013, he also served as Acting Dean of the Faculty of Engineering (2010-11) and Vice-President Commercialization at Innovate Manitoba (2012-14). His career combines 16 years at IBM Watson Research Center with two decades of academic leadership in Winnipeg. Education Ph.D. in Applied Physics & Electronics, University of Durham, U.K., 1986 M.Sc. in Electrical Engineering, University of Manitoba, 1982 B.Sc. in Electrical Engineering, University of Manitoba, 1981 Research Interests Prof. Buchanan’s work spans nano-scale CMOS gate dielectrics, high-κ metal oxides (HfO₂, ZrO₂, Al₂O₃), defect chemistry, quantum tunnelling, and dielectric reliability. Since 2010 his group has pioneered MEMS capacitive micromachined ultrasonic transducers (CMUTs) with multiple moving membranes for low-frequency, air-coupled imaging and NDT, as well as floating-gate MOS chemosensors functionalized with conducting polymers for olfactory applications. His publications reveal two dominant waves: the 1990s-2000s focus on ultra-thin SiO₂/high-κ stacks and the ITRS gate-stack roadmap, followed by a 2010s-2020s surge in CMUT design, anemometry, and polymer-based sensor arrays, demonstrating continuous adaptation from fundamental materials physics to applied micro-systems. Honours & Awards University of Manitoba Students’ Teacher Recognition Award – 2007 IBM Research Division Award – 1988 IBM Outstanding Technical Achievement Award – 1992 IBM Microelectronics Division General Manager’s Teamwork Award – 1997 Fellow, Canadian Academy of Engineering Senior Member, IEEE Professional Service & Grants He co-founded SEMATECH’s Gate Stack Engineering Working Group (1992-2000) that authored the ITRS gate-stack roadmap, co-chaired multiple MRS and IEEE Semiconductor Interface Specialists Conferences, and edited special issues of IBM J. Res. Dev. and MRS Proceedings on ultra-thin dielectrics. Grant support has included NSERC Canada Research Chair, CFI, and industrial partnerships with IBM, SEMATECH, and Manitoba HVDC Research Centre. Labs & Teams He leads the Microelectronics & Nanotechnology Research Laboratory within the University of Manitoba’s Faculty of Engineering, supervising graduate researchers in clean-room micro-fabrication, electrical characterization, and MEMS prototyping for ultrasound and chemical sensing systems.
Tanesh Kumar is a Researcher in the Department of Information and Communications Engineering specializing in artificial intelligence and edge computing applications for next-generation networks and healthcare systems. His work bridges theoretical innovation with practical implementations in constrained environments. Core research domains include: Artificial Intelligence (100% fingerprint relevance) Internet of Things (63% relevance) Edge Computing & Edge Intelligence (57%/52% relevance) Medical Imaging applications (52% relevance) Anomaly Detection and Machine Learning (52% relevance) Recent publications demonstrate a strategic focus on three convergent thrusts: (1) AI-native 6G architectures integrating device-network intelligence, (2) distributed edge AI for medical tomography enhancing diagnostic accessibility, and (3) lightweight security frameworks for IoT endpoints. His approach consistently emphasizes resource efficiency in edge deployments while maintaining robust performance. Kumar maintains active international collaborations across telecommunications and healthcare domains, with co-authorship spanning European and Asian research institutions. His contributions directly advance UN Sustainable Development Goals through technology-enabled healthcare improvements and sustainable network infrastructure development.
