Anders Enqvist is a doctoral student at KTH Royal Institute of Technology in the Department of Communication Systems. His research focuses on energy-efficient wireless networks, particularly leveraging Intelligent Reconfigurable Surfaces (RIS) to optimize transmission efficiency. PhD Candidate, KTH Royal Institute of Technology (Current) Master of Science in Electrical Engineering, KTH His research interests span Wireless Communication , Energy Efficiency , RIS Deployment , and Radio Network Optimization . He investigates how dynamic surfaces can reshape wireless environments for sustainable connectivity. Recent publications highlight trends in RIS for short-packet transmission , bit-rate optimization , and physical layer algorithms . His work bridges theoretical modeling (e.g., scaling behaviors) with practical deployments. Anders serves as an assistant for courses such as Degree Project in Computer Science (DA240X, II142X) and Radio Networks (IK2510) , contributing to software engineering and wireless network education. He is based in Kista, Sweden, and can be contacted at enqv@kth.se .
Dr. Teerachot Siriburanon is an Assistant Professor in the School of Electrical and Electronic Engineering at University College Dublin (UCD), Ireland. He joined UCD as a Post-Doctoral Researcher in 2016 under the prestigious Marie Skłodowska-Curie Individual Fellowship Program and was promoted to Assistant Professor in 2019. His educational background includes: B.E. degree in Telecommunications Engineering from Sirindhorn International Institute of Technology (SIIT), Thailand, 2010 M.E. and Ph.D. degrees from Tokyo Institute of Technology, Japan, 2012 and 2016 respectively Dr. Siriburanon's research focuses on CMOS wireless transceiver systems and clock/frequency generation for both wireless and wireline communication applications. His work spans millimeter-wave circuit design, phase-locked loops, low-noise amplifiers, and quantum computing circuitry. He has made significant contributions to the field of ultra-low jitter frequency synthesis and has developed innovative techniques for flicker phase noise reduction in oscillators. His research demonstrates expertise in advanced CMOS technologies including 22-nm FD-SOI and 28-nm processes. His recent publications reveal a strong trend toward addressing critical challenges in 5G/6G communications, quantum computing hardware, and high-precision frequency synthesis. The research spans both theoretical modeling and practical circuit implementations, with particular emphasis on optimizing performance metrics such as phase noise, jitter, and power efficiency in high-frequency applications. Dr. Siriburanon has received numerous prestigious awards including: Marie Skłodowska-Curie Individual Fellowship (2017) Science Foundation Ireland (SFI) Frontiers for the Future Project (2021) IEEE Solid-State Circuits Outstanding Reviewer (2024) IEEE CASS Best Reviewer for TCAS-I (2022) Multiple best paper awards at international conferences As an educator and research leader, Dr. Siriburanon serves as a thesis supervisor for PhD students and coordinates courses in Circuit Theory and Electronic Circuits. He currently leads active research projects including 'Massive Array of mm-Wave Transmitters with Digital Pencil Beam Steering for 5G Communications and Radars' and 'Smart RFDAC with AI-Assisted Calibration'. His service to the academic community includes membership on the Technical Program Committees of the IEEE Radio Frequency Integrated Circuits Symposium (RFIC) and the IEEE International Solid-State Circuits Conference (ISSCC), as well as editorial roles for IEEE Transactions on Circuit and Systems.
Adrien F. Vincent is an Associate Professor at IMS Bordeaux (Laboratory of Integration, Material to System) under Université de Bordeaux, Institut Polytechnique de Bordeaux, and CNRS. He leads research in neuromorphic computing with a focus on spiking neural networks and memristive devices. His team, 2HC Production Engineering, develops energy-efficient hardware for real-time event-based data processing. Key research areas: Neuromorphic systems, Low-power electronics, Memristor technology Recent publications explore spintronic neural networks, STDP plasticity, and energy optimization His work addresses hardware-friendly learning algorithms and co-integration of analog silicon neurons with memristive arrays. Projects include ULPEC (Ultra-Low Power Event-Based Camera) and MIRA2015 (Memristive Architectures).
David Bol is a Lecturer at Université catholique de Louvain (UCLouvain), affiliated with the Louvain Polytechnic School (EPL) and the Electrical Engineering Center (ELEN) within the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM). His academic work spans both teaching and research in electrical engineering, with a particular focus on low-power circuit design and sustainable electronics. Dr. Bol's research interests center around low-power electronics , integrated circuit design , and sustainable approaches to electronic systems . His work bridges traditional electrical engineering with environmental considerations, particularly through life cycle assessment of electronic devices and IoT systems. He has developed expertise in ultra-low-power circuit design, analog and mixed-signal circuits, energy harvesting techniques, and the environmental impact of semiconductor manufacturing and wireless technologies. His recent publications demonstrate a consistent focus on energy efficiency in electronic systems, with significant contributions to current reference design, biomedical amplifiers, and near-sensor computing architectures. A notable trend in his work is the integration of environmental sustainability considerations into traditional circuit design, particularly through life cycle assessment methodologies applied to IoT devices and communication infrastructure. Dr. Bol teaches multiple electrical engineering courses including Analog Electronic Systems (LELEC2532), Synthesis of Digital Integrated Circuits (LELEC2570), and Sustainable Development and Transition (LEPL1804), reflecting his dual expertise in circuit design and sustainability.
Mahsa Shoaran is a Tenure-Track Assistant Professor at EPFL (École Polytechnique Fédérale de Lausanne) jointly appointed at the Center for Neuroprosthetics and the Institute of Electrical Engineering . She is also the founding director of the Integrated Neurotechnologies Laboratory (INL) and contributes to several doctoral programs and teaching missions across EPFL’s School of Engineering (STI). Education: PhD in Electrical Engineering, EPFL (2015) M.Sc. & B.Sc., Sharif University of Technology Postdoctoral Fellow, California Institute of Technology (Caltech) (2015-2017) Former Assistant Professor, School of Electrical and Computer Engineering, Cornell University (2017-2019) Research Focus: Dr. Shoaran’s work sits at the convergence of integrated circuit design , machine learning , and neuroscience . Her group develops ultra-low-power, miniaturized system-on-chips (SoCs) capable of real-time neural recording , pathology detection , and closed-loop therapeutic intervention such as adaptive neurostimulation. Machine-learning algorithms running on-chip enable precise symptom detection in neurological and psychiatric disorders, while advanced circuit techniques guarantee energy efficiency suitable for long-term implantable or wearable neural interfaces. Scientific Awards & Grants: ERC Starting Grant 2021 Google Faculty Research Award in Machine Learning 2019 Swiss NSF Postdoctoral Fellowships NSF Award for Young Professionals – Smart & Connected Health MIT EECS Rising Star 2015 Doctoral Advising & Service: She currently supervises 14 PhD students in the Integrated Neurotechnologies Laboratory. Additionally, she serves on the PhD program committees for Electrical Engineering (EDEE), Microsystems & Microelectronics (EDMI), and contributes to the Neuro-X and SEL teaching programs. She is a Technical Program Committee member for IEEE CICC and serves on the Student Research Preview committee for ISSCC. Laboratory & Collaborative Teams: The Integrated Neurotechnologies Laboratory (INL) at EPFL Campus Biotech in Geneva hosts her interdisciplinary team of circuit designers, machine-learning researchers, and neuroscientists. The lab collaborates closely with clinicians to translate innovations into real-world neuroprosthetic and diagnostic devices.
Dr. Cheng Yin is a Lecturer in Computing Science at Ulster University's School of Computing, based at the Belfast campus (Room BB-02-010, 2-24 York Street). She specializes in wireless communications and security, with research encompassing AI-driven network optimization, reconfigurable intelligent surfaces (RIS), and UAV-assisted systems. Her work bridges theoretical models and practical implementations in 5G/6G networks. Education: PhD from Queen's University Belfast (2016-2019), focusing on Physical Layer Security for Wireless Networks under Unreliable Backhaul Connections . Research Focus: Wireless Security & Privacy: Developing anti-eavesdropping techniques and secure backhaul solutions. AI for Networks: Machine learning applications in resource allocation and signal optimization. Reconfigurable Intelligent Surfaces: Enhancing IoT medical devices and UAV communication reliability. Semantic Communication: Efficiency improvements in next-generation networks. Publications: Her recent work (2021-2025) emphasizes AI-enhanced security, RIS applications in healthcare IoT, and robust resource allocation for UAV networks, reflecting interdisciplinary collaboration across computing, engineering, and biomedical fields. Awards: Best Student Paper Award at INISCOM'19 (2019) Projects & Activities: Leads the 'Resource Management for 6G Internet-of-Vehicles Networks' project (2024-2025). Active in peer review (IEEE journals), invited talks (e.g., Dronetastic Workshop), and promotes women in engineering. Recognized as a Fellow of the Higher Education Academy (FHEA).
Professor Hyung Seok Kim is a distinguished academic at Sejong University, currently serving as Professor in the Department of AI and Robotics. He also holds significant administrative positions including Dean of the College of Software Convergence at Sejong University. Professor Kim leads the MINES LAB (Mobile Intelligent Embedded Systems Lab), located in Room 211, Chungmu Hall at Sejong University, where he directs research in cutting-edge AI and embedded systems technologies. Professor Kim's educational background includes: Bachelor of Engineering: Department of Electrical Engineering, Seoul National University Master of Engineering: Department of Electrical and Computer Engineering, Seoul National University Doctor of Engineering (Ph.D.): Department of Electrical and Computer Engineering, Seoul National University Professor Kim's research spans multiple domains at the intersection of artificial intelligence and embedded systems. His work focuses on AI robots, wearable AI devices, Large Language Models (LLMs), and on-device AI technologies . His research group develops innovative solutions for emotion recognition, medical imaging analysis, and IoT applications. The MINES LAB specifically targets the integration of AI with embedded systems to create efficient, low-latency solutions for real-world problems ranging from healthcare monitoring to industrial applications. Analysis of Professor Kim's recent publications reveals a strong focus on medical AI applications, federated learning for IoT networks, and multimodal emotion recognition . His work demonstrates consistent innovation in applying deep learning techniques to medical imaging (particularly ophthalmology and cardiology), developing efficient edge-AI solutions for wearable devices, and creating novel network optimization approaches for industrial IoT. The publications show a clear trajectory toward more integrated, privacy-preserving AI systems that can operate effectively on resource-constrained devices. While specific awards to Professor Kim aren't detailed in the provided information, his research group has achieved notable recognition: Dr. Song Seung-hwan, a Ph.D. candidate at the lab, received the Presidential Industrial Service Medal Professor Kim has mentored an extensive number of students throughout his career, with alumni pursuing diverse career paths at leading organizations worldwide. His former students have secured positions at major technology companies including Samsung Electronics, LG Electronics, Kakao, and Amazon, as well as academic positions at universities globally. The MINES LAB currently supports multiple graduate students, post-doctoral researchers, and research assistants working on various AI and embedded systems projects. Professor Kim's research appears to be well-funded, with connections to industry partners including Hyundai Motor Company and Samsung Electronics, though specific grant details aren't provided in the text. The MINES LAB serves as the central hub for Professor Kim's research activities, focusing on AI robots, wearable AI devices, and LLM applications. The lab maintains active collaborations with industry partners and has produced numerous commercial applications through its alumni network. Current research directions include developing low-latency emotion recognition systems, medical imaging analysis tools, and efficient network protocols for IoT applications. The lab environment appears highly collaborative, with both full-time and part-time researchers contributing to various projects across the AI and embedded systems spectrum.
Kazuhiko Tamesue is an Associate Professor at the Faculty of Science and Engineering , Waseda University , with a focus on Terahertz Communication , Artificial Intelligence , and IoT Network Security . His academic journey spans academia and industry, including long-term roles at Panasonic Corporation (1991–2009) and current leadership in advanced wireless systems since 2022. Research Areas : Telecommunications, Wireless Hardware, Data Science, Machine Learning, Atmospheric Sensing Key Contributions : Pioneering 300GHz OFDM transceivers, AI-driven GNSS spoofing detection, and dual-frequency THz radar for cloud physics Projects : NICT-funded terahertz networks (2023–2024), ultra-low-latency systems (2022–2024), and security-enhancing radar technologies His 15 most recent publications (2024–2008) demonstrate expertise in (1) terahertz propagation for NTN/HAPS platforms, (2) machine learning for security (LSTM, GANs), and (3) next-generation IoT protocols (LPWAN, distributed ledger). He holds 20+ patents in antenna design, direct-conversion receivers, and power line communication. As an IEEE and IEICE member, he contributes to standards in 5G/6G and EMC.
Dr. Tri Nhu Do is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, where he conducts cutting-edge research at the intersection of wireless communications and artificial intelligence. His academic journey spans institutions across Vietnam, South Korea, the United States, and Canada, bringing a global perspective to his work. Dr. Do is affiliated with the Advanced Microwave and Space Electronics Research Center (POLY-GRAMES) and contributes to the 'New Frontiers in Information and Communications Technologies' center of excellence. Dr. Do's research focuses on wireless communications systems, artificial intelligence applications in telecommunications, and integrated sensing and communication technologies. His work addresses critical challenges in next-generation wireless networks, particularly in security, resource allocation, and performance optimization. Recent research demonstrates a strong emphasis on applying deep learning, generative AI, and federated learning techniques to solve longstanding problems in wireless communications. His publication record shows remarkable productivity and impact, with numerous articles in top IEEE journals including IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Letters. The research trends indicate a strategic shift toward integrating AI with traditional communication theory, particularly in security applications, reconfigurable intelligent surfaces, and UAV communications. Dr. Do teaches advanced courses in signal detection and estimation, communication theory, and digital transmission, sharing his expertise with the next generation of electrical engineers. His teaching reflects his research interests, providing students with both theoretical foundations and exposure to cutting-edge developments in the field.
Yves Audet is an Associate Professor in the Department of Electrical Engineering at Polytechnique Montréal, affiliated with three centers of excellence: Industry of the Future and Digital Society (primary), Energy, Water and Resources , and Transport and Sustainable Infrastructure . He holds a B.Sc. and M.Sc. from Sherbrooke University and a Ph.D. from Simon Fraser University. His research bridges integrated circuit design , energy systems , and microsensor technology , with focus areas including: Ultra-low power CMOS circuits for energy harvesting High-speed isolated data communication (capacitive/inductive) Spectral imaging sensors and Hall-effect isolators Radiation-hardened avionics systems Photovoltaic and RF power conversion Analysis of his 62 publications reveals strong emphasis on: Optimizing power-delay tradeoffs in communication systems (7 recent papers on isolators) Advancing start-up circuits for sub-50mV energy harvesters Developing miniaturized sensor interfaces for IoT/industrial applications Improving fault tolerance in FPGAs and avionics He has supervised 19 graduate students (6 PhD, 13 Master's) working on projects spanning CMOS spectrometers, UAV wireless charging, and radiation-hardened circuits. No awards are documented, but he holds 2 patents in CMOS photodetection technology.
Professor Gaetano Gargiulo is a Research Professor in the Biomedical Engineering and Neuroscience research program at MARCS Institute, Western Sydney University. He received his MS equivalent degree in Electronic Engineering (biomedical specialization) from University of Naples in 2006 and his Ph.D. from University of Bologna in 2010. With extensive experience in medical device design and commercialization, he has contributed to two startup companies in bio-instrumentation and holds seven PCT patents. His research focuses on cardiovascular monitoring, inverse modeling, brain/machine interfaces, and biomedical instrumentation. A key aspect of his work involves improving electrocardiography (ECG) by addressing systematic errors while preserving current clinical practices. He has developed ultra-high input impedance amplifiers for passive dry electrodes enabling unobtrusive patient monitoring. Professor Gargiulo's publication record shows consistent output with 137 publications and over 133,000 reads. His recent work (2022-2025) spans forcecardiography, soft robotics, biomedical sensors, and wearable monitoring technologies. His research demonstrates strong translational potential for clinical applications in cardiac diagnostics and monitoring. His research achievements include: Development of novel forcecardiography (FCG) techniques for simultaneous cardiac and respiratory monitoring Advancements in HASEL actuator technology for soft robotics applications Innovations in wearable sensor design for cardiovascular and respiratory monitoring Fundamental contributions to understanding and correcting systematic errors in ECG Development of low-cost diagnostic tools for peripheral vascular diseases Professor Gargiulo's work bridges theoretical modeling of bio-signal sources with practical development of diagnostic tools, maintaining a strong focus on clinical applicability while advancing fundamental understanding of cardiovascular mechanics and monitoring.
