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David WentzloffView profile

Professor

• RF integrated circuits
• Ultra-low power design
• Wireless communication
• +4 more

David Wentzloff is a Professor of Electrical Engineering and Computer Science at the University of Michigan, where he has been on faculty since 2007. He directs the Wireless Integrated Circuits and Systems (WICS) group and serves as Director of the Michigan Integrated Circuits Laboratory (MICL). Wentzloff received his B.S.E. from the University of Michigan in 1999 and his S.M. and Ph.D. from MIT in 2002 and 2007. Wentzloff's research focuses on low-power integrated circuits for wireless communication in energy-constrained and volume-constrained applications. His work spans three primary areas: Synthesizable all-digital radios and radio building blocks Wireless body sensor networks (channel modeling, radios, and antennas) Radios and interfaces for millimeter-scale computing devices His innovations have significantly reduced power consumption in wireless communication for IoT devices, impacting standards including Bluetooth, WiFi, and 5G/6G. Analysis of his publication record reveals a consistent focus on ultra-low power wireless communication, with recent work emphasizing back-channel communication techniques that achieve 500x power reduction compared to conventional approaches. His research bridges theoretical circuit design with practical applications in healthcare, industrial monitoring, and consumer electronics. Wentzloff's scientific recognition includes: 2024 University of Michigan Faculty Recognition Award NSF CAREER Award (2012) DARPA Young Faculty Award (2009) Multiple best paper awards Two-time recipient of the Eta Kappa Nu Professor of the Year Award As an educator and mentor, Wentzloff has graduated 21 doctoral students and mentored dozens of master's and undergraduate researchers. He has taught courses ranging from introductory circuits to advanced graduate topics in analog and RF design. His entrepreneurial impact includes co-founding three successful companies: Everactive (formerly PsiKick), Movellus, and CubeWorks, which commercialize technologies developed in his research lab. Wentzloff leads the WICS research group, which operates within the Michigan Integrated Circuits Laboratory. His team develops cutting-edge technologies for batteryless sensing systems, millimeter-scale computing platforms, and ultra-low power wireless communication solutions that enable the next generation of Internet of Things applications.

• Analog and Mixed-Signal Circuit Design
• RF Energy Harvesting
• Neuromorphic Hardware
• +5 more

Pengcheng Xu is a Researcher at the Technical University of Munich's Chair of Circuit Design under Prof. Ralf Brederlow, specializing in analog and mixed-signal circuit design. His work spans energy harvesting systems, neuromorphic hardware, and wireless sensor technologies, with strong industry connections including prior roles at Huawei and Fraunhofer EMFT. Education: Bachelor of Physics, Shanghai Normal University (2013) Master of Integrated Circuit Engineering, Tongji University (2016) Ph.D. in Electrical Engineering, Université catholique de Louvain (2021) Exchange Student, University of Erlangen-Nuremberg (2015) Xu's research focuses on practical applications of circuit design including RF energy harvesting for battery-less IoT sensors, neuromorphic accelerators for edge computing, and precision analog systems for electrochemical/ mechanical stress sensing. His work bridges theoretical circuit innovation with real-world implementation in semiconductor processes from 28nm FDSOI to emerging memory technologies. His publications demonstrate consistent high-impact contributions to IEEE journals and conferences including JSSC, ISSCC, and ESSCIRC, with particular expertise in impedance-aware rectifier design and low-power circuit architectures. Xu holds a pending European/US patent for RF energy harvesting systems. Awards and Recognition: Shanghai Outstanding Graduate Award (2013, 2016) Chinese Government Award for Outstanding Self-Funded Students Abroad (2020) Chinese National Scholarship (2012, 2014, 2015) Meritorious Winner, Mathematical Contest in Modeling (2013) Xu actively contributes to the academic community as IEEE Young Professionals Germany Chair (2023-2024), IEEE Design Automation Conference TPC member (2022-2024), and reviewer for multiple IEEE journals. He supervises student theses in analog circuit design and neuromorphic hardware through TUM's Chair of Circuit Design, which maintains strong industry partnerships with semiconductor companies.

• Trustworthy computing
• Energy harvesting
• Intelligent system design
• +7 more

Youhua Shi is a full Professor in the Faculty of Science and Engineering at Waseda University, Japan. He obtained his Doctor of Engineering from Waseda in 2005 and is an active member of IEICE, IPSJ, IEEE, and two Japanese academic societies. His research portfolio integrates trustworthy computing, hardware security of AI accelerators, energy-harvesting interface circuits for triboelectric nanogenerators, and low-power VLSI design-for-test methodologies. Education: Doctor of Engineering, Waseda University (2005) Graduate studies, Waseda University, Division of Engineering (completed 2005) Research Interests: Prof. Shi pursues trustworthy and secure silicon systems, spanning hardware Trojans in automated AI-accelerator flows, radiation-hardened latch design for soft-error resilience, and power-efficient CNN accelerators exploiting zero-gating and data-reuse techniques. Parallel work targets energy-autonomous IoT through advanced interface circuits for triboelectric nanogenerators, achieving record energy-per-cycle beyond the classical CMEO limit. Publication Trends: Recent articles (2024-2025) emphasize two thrusts: (i) security of AI/FPGA accelerators—proposing stealthy hardware-Trojan frameworks embedded within design-space-exploration flows that can misclassify up to 97% of inputs—and (ii) power electronics for triboelectric harvesters—introducing dual-output rectifiers and Bennet-doubler biasing that multiply output power >150× over conventional full-wave rectifiers, enabling battery-free IoT nodes. Scientific Awards: APCCAS Best Student Paper Award – 2020 IEEK Best Paper Award – 2012 Students & Collaboration: He has mentored numerous doctoral and master’s scholars, including Yirui Su, Chao Guo, Jinghao Ye, Lin Ye, Saki Tajima, and Masaru Oya, many of whom serve as first authors on his high-impact publications, indicating an active and productive advising role. Labs & Teams: While the text does not name a specific laboratory, his continued affiliation with Waseda University’s Faculty of Science and Engineering and his extensive project output imply he leads a research group focused on secure & energy-efficient VLSI systems, collaborating closely with colleagues such as Prof. Masao Yanagisawa and Prof. Nozomu Togawa.

Yuan YaoView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Power and Thermal Management
• +7 more

Yuan Yao serves as an Assistant Professor in the Department of Information Technology at Uppsala University, Sweden. His academic role spans teaching and research within the Computer Systems division, focusing on cutting-edge computer architecture and parallel computing systems. He maintains active collaborations across international institutions, particularly in energy-efficient hardware design and emerging computing paradigms. His educational journey includes: B.S. in Micro-electronics from Northwestern Polytechnical University, China (2009) M.S. in System-on-Chip Design from KTH Royal Institute of Technology, Sweden (2014) Ph.D. in Electrical Engineering and Computer Science from KTH Royal Institute of Technology (2019) Yao's research centers on power and thermal management for chip multi-processors, Network-on-Chips (NoCs), and GPUs. He pioneers hardware/software co-design for high-performance computing, coherency mechanisms for emerging memory technologies, and performance analysis of on-chip networks. Recent work expands into neural network acceleration and battery-less Internet of Things architectures, reflecting a trajectory toward energy-constrained specialized systems. His methodology integrates formal modeling with practical implementation for real-world impact. Publication trends reveal consistent innovation in energy efficiency across parallel architectures. From foundational DVFS techniques for NoCs (2016-2018) to recent breakthroughs in battery-less IoT (2023-2024), his work demonstrates evolutionary progression toward novel computing domains. Key thematic threads include thermal-aware optimization, memory consistency protocols, and hardware acceleration for AI workloads, with applications spanning data centers to embedded systems. Scientific recognition includes: Best paper candidate at IEEE International Symposium on High Performance Computer Architecture (HPCA) 2018 for in-network packet generation research Yao actively supervises graduate researchers and leads collaborative projects in computer architecture. His grant portfolio supports work on battery-less IoT systems and neural network accelerators, though specific funding details aren't publicly enumerated. Current projects emphasize sustainable computing through novel architectures for energy-harvesting environments. He operates within Uppsala University's Computer Systems division, contributing to research groups focused on hardware acceleration, embedded systems, and networked architectures. His lab environment fosters interdisciplinary work bridging computer architecture, energy harvesting, and machine learning for next-generation computing platforms.

• Power Management Systems
• Wearable Electronics
• Implantable Electronics
• +4 more

Kaung Oo Htet is a Lecturer in Electronic and Electrical Engineering at the University of Hertfordshire's Department of Engineering and Technology within the School of Physics, Engineering & Computer Science. He holds a PhD and focuses on ultra-efficient power management systems for low-power wearable and implantable electronics. His research emphasizes inductor-less switched-capacitor DC-DC converters, energy harvesting interfaces, and self-powered systems. His work aims to create compact, battery-free devices through innovative circuit architectures. Key research areas include SIMO converters, wearable/implantable electronics, and energy-efficient start-up circuits. He has contributed to Springer publications and actively develops cutting-edge solutions in integrated power electronics. Dr. Htet’s research spans multi-output power converters, biomedical energy harvesting, and precision power management systems. His recent work (2025) explores next-generation SIMO converters for biomedical devices. He accepts PhD students for 2025 admissions and maintains an ORCID profile (0000-0002-8958-5310). No scientific awards are explicitly listed. His advising record and grants are not detailed here, but his publications indicate sustained research activity in power electronics for medical and wearable applications.

Tolga SoyataView profile

Associate Professor

• Cyber-Physical Systems
• Digital Health
• FPGA-Based High-Performance Computing
• +5 more

Tolga Soyata is an Associate Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering, specializing in cyber-physical systems, digital health, and high-performance computing. Previously, he served as an Associate Professor at SUNY Albany (2016-2019), Senior Lecturer at Johns Hopkins University (2020), and Assistant Research Professor at the University of Rochester (2008-2016). PhD, Electrical and Computer Engineering, University of Rochester (2000) MS, Electrical and Computer Engineering, Johns Hopkins University (1992) BS, Electrical and Communications Engineering, Istanbul Technical University (1988) His research focuses on cyber-physical systems , digital health applications , and FPGA/GPU-based high-performance computing , with significant contributions in medical cyber physical systems, brain-computer interfaces, and energy harvesting for embedded systems. His work bridges hardware design with healthcare applications and smart city infrastructure. Analysis of his recent publications reveals a strong emphasis on medical applications of cyber-physical systems (30%), smart city infrastructure (25%), energy-efficient computing (20%), and security/privacy frameworks (15%). His work consistently integrates hardware innovation with real-world applications, particularly in healthcare monitoring and urban systems. BEST PAPER AWARD (UEMCON18-BCI) PRESIDENTIAL AWARD FOR UNDERGRADUATE RESEARCH BEST PAPER AWARD (HONET-ICT'15) IEEE Senior Member (2016-present) ACM Senior Member (2016-present) As an advisor, he has mentored 12 PhD students (including 2 who completed degrees under his supervision) and over 25 master's/undergraduate researchers, many of whom received awards for their work. His TTIP directorship at GMU focuses on expanding graduate education in computer engineering through innovative recruitment and bridge programs. His research has been supported by industry partnerships with NVIDIA, Xilinx, and MOSIS. He leads research in medical cyber physical systems and energy-harvesting architectures, with lab facilities supporting FPGA development, GPU programming, and medical sensor integration for digital health applications.

• Computer Networking
• Machine-to-Machine Communications
• IoT (Internet of Things)
• +4 more

Gianluca Rizzo is an Adjunct Professor at HES-SO Valais-Wallis, affiliated with the Higher School of Management (Haute Ecole de Gestion) and the Internet of Things (IoT) department linked to EPFL. He holds a Computer Science Bachelor's degree from UC3M University in Madrid. His research focuses on IoT, vehicular communications, AI-driven network optimization, and disaster-resilient systems. Education: Computer Science BSc (UC3M University, Madrid) Affiliations: HES-SO Valais-Wallis, EPFL IoT Group, RECODIS (Post-Disaster Communications Lab) His work spans energy-efficient networking, opportunistic content dissemination (Floating Content), and distributed learning techniques. Key contributions include optimizing multi-agent systems in dynamic environments, developing gossip learning frameworks for urban trajectory prediction, and analyzing SWIPT (Simultaneous Wireless Information and Power Transfer) in vehicular networks. He also explores emergency networks for post-disaster scenarios, leveraging technologies like UAVs and floating content for situational awareness. Recent publications emphasize AI-native vehicular communications, edge computing orchestration, and infrastructure savings via moving base stations. Collaborative projects include V-Edge (virtual edge computing) and the NOSE nomadic sensing ecosystem. His work bridges theoretical models (e.g., stochastic geometry) with practical implementations, addressing challenges in 5G/6G, smart cities, and industrial IoT.

• Energy Harvesting
• Embedded Systems
• Internet of Things (IoT)
• +4 more

Sebastian Bader is an Associate Professor at Mid Sweden University 's Department of Computer and Electrical Engineering (DET). His research focuses on energy harvesting for autonomous sensor systems, particularly in Industrial IoT contexts. He leads the Master of Science in Electrical Engineering programs and teaches Embedded Systems Programming , Embedded Machine Learning , and Sensor Networks . Diplom-Ingenieur (Information Technology), University of Applied Sciences, Wilhelmshaven Licentiate of Technology (2011) and Doctor of Technology (2013), Mid Sweden University Research interests include: Self-powered systems utilizing ambient energy sources Tiny Machine Learning (TinyML) on resource-constrained devices Variable Reluctance Energy Harvesting Sustainable IoT architectures Acoustic Emission Analysis for industrial monitoring Scientific roles and honors: Senior Member of IEEE PSMA Energy Harvesting Committee member Associate Editor for Sustainable Computing: Informatics and Systems Topic Editor for MDPI Sensors Journal IEEE Sensors Applications Symposium steering committee His work spans energy-autonomous embedded systems, with applications in rotating machinery, environmental monitoring, and structural health assessment. He supervises PhD students and collaborates internationally with institutions like CSIRO and University of Southampton.

• Ultra-low power digital circuit design
• Energy-efficient AI processor design
• Compute-in-memory
• +5 more

Dr. Longyang Lin is an Assistant Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech) in Shenzhen, China. He received his Ph.D. from the National University of Singapore in 2018 and has been with SUSTech since May 2021. His research focuses on cutting-edge integrated circuit design with emphasis on ultra-low power systems, hardware security, and cryogenic circuits. Education: Ph.D., National University of Singapore, 2018 M.Sc., Lund University, 2011-2013 B.Sc., Umeå University & B.Eng., Shenzhen University, 2007-2011 Dr. Lin's research spans ultra-low power digital circuit design , energy-efficient AI processor design , compute-in-memory , on-chip sensor fusion , and cryogenic CMOS circuit design . His work addresses critical challenges in battery-less systems, hardware security, and energy-efficient computing. He has developed innovative solutions for self-powered sensor nodes, widely energy-scalable VLSI systems, and secure integrated circuits that operate across extreme temperature ranges. His recent publications demonstrate a strong focus on hardware security, ultra-low power design, and biomedical applications. The research shows a clear progression toward more integrated systems that combine sensing, processing, and communication in highly energy-constrained environments. Many papers address the challenge of maintaining performance while drastically reducing power consumption, with several targeting sub-nanowatt operation. Scientific Awards: Takuo Sugano Award for Outstanding Far-East Paper, ISSCC 2022 ISSCC Demonstration Session Certificate of Recognition, 2022 ISSCC Demonstration Session Certificate of Recognition, 2020 IEEE SSCS Singapore Chapter Award, 2017 & 2018 ISSCC Student Travel Grant Award, 2017 Dr. Lin actively mentors students and researchers, with openings for Research Assistant Professors, Postdoctoral Fellows, Research Assistants, and Graduate Students. His research group benefits from ample funding and regular tape-out opportunities (3-5 per year across different process nodes). He serves as Associate Editor for IEEE Transactions on VLSI Systems and has authored or co-authored over 40 publications, including 10 in IEEE JSSC, 5 in ISSCC, and 12 in VLSI Circuits conference. His laboratory focuses on integrated circuit design for ultra-low power applications, with specialized expertise in hardware security, energy harvesting systems, and cryogenic electronics. The research environment supports tape-outs in advanced CMOS processes, enabling practical validation of theoretical designs.

Samer LahoudView profile

Associate Professor

• Wireless Communications
• Internet of Things (IoT)
• Low Power Wide Area Networks (LPWAN)
• +12 more

Samer Lahoud is an Associate Professor and University Research Chair at the Faculty of Computer Science, Dalhousie University, where he leads research in wireless networks, IoT, and resource optimization. He joined Dalhousie in 2023 after holding academic positions at the University of Rennes and Saint Joseph University in Lebanon, and working as a researcher at IRISA laboratory and as a research engineer at Nokia Bell Labs Europe. His educational background includes a PhD in Computer Science and Networks from IMT Atlantique, Rennes (2006), and a Habilitation (HDR) from Université Paris-Saclay (2023), both in France. Dr. Lahoud's research focuses on enhancing wireless network performance through adaptive and energy-efficient solutions. Key areas include radio resource allocation in cellular and LPWAN, full-duplex communications, and game-theoretic models for multi-operator networks. His work bridges theoretical models with real-world applications in smart agriculture, smart cities, and industrial IoT. He has made significant contributions to LoRaWAN propagation modeling and developed open-source simulators for RL-based resource allocation. His recent publications reflect a strong trend toward intelligent, learning-based, and game-theoretic approaches for optimizing LPWANs, 5G RAN slicing, and full-duplex networks. The articles emphasize energy efficiency, reliability, and scalability in IoT systems, with increasing integration of machine learning and federated learning paradigms. University Research Chair - Established Scholar, Dalhousie University Dr. Lahoud has co-supervised over 10 students, including postdoctoral researchers, PhD, and master's candidates. He has led multidisciplinary research projects funded by national and international agencies, particularly in LPWAN for smart agriculture. He also played a key role in establishing a joint IoT master's program between Saint Joseph University and Paris-Saclay University. He leads a research team focused on wireless systems and has developed open-source tools such as the LoRa-MAB simulator and FDOFDMA-Simulator. His lab fosters international collaboration between institutions in Canada, France, and Lebanon, promoting student mobility and knowledge transfer.

K.G. LangendoenView profile

Professor

• Computer Science
• Wireless Communication
• Sensor Networks
• +3 more

K.G. Langendoen is a Professor in the Department of Embedded Systems within the Faculty of Electrical Engineering, Mathematics and Computer Science, specializing in cutting-edge research at the intersection of communication systems and sustainable computing. His research portfolio centers on: Wireless Communication protocols (Visible Light Communication, LoRaWAN) Energy-constrained sensor networks and battery-less systems Embedded systems design for intermittent power environments Scalable IoT architectures and spectrum utilization Recent publications (2020-2023) reveal a strategic focus on passive communication techniques and energy-harvesting systems. His work bridges theoretical modeling with practical implementations, particularly in VLC and LoRaWAN optimization, addressing critical challenges in sustainable networking infrastructure and pervasive sensing. Langendoen has supervised 22 research students through thesis projects and collaborative works, significantly contributing to advancements in energy-efficient communication protocols. His academic service includes editorial roles for ACM publications and Lecture Notes in Computer Science, along with peer review activities. As a core member of the Embedded Systems research group, he drives initiatives in pervasive computing and sustainable networking architectures, with ongoing projects focused on passive sensor networks and spectrum-aware communication systems.

• Wireless Networks
• Internet of Things (IoT)
• Energy Harvesting
• +4 more

Jeroen Famaey is a Professor at Ghent University's Department of Information Technology, Faculty of Engineering and Architecture. His research focuses on wireless communication systems, IoT, energy-efficient networking, and millimeter-wave technologies. He leads projects in 5G/6G enablers, battery-less devices, and extended reality (XR) networking. Key contributions include energy-aware scheduling algorithms for industrial IoT, mmWave-based VR systems, and standardization efforts in nanoscale localization. His work spans over 200 publications across journals like IEEE Internet of Things Journal and conferences such as GLOBECOM. He collaborates extensively with industry partners, advancing practical applications of wireless technologies in smart cities and industrial automation. Current research emphasizes zero-energy devices and terahertz communication for future networks.

Mimi XieView profile

Assistant Professor

• IoT Systems
• Non-Volatile Memory Optimization
• Energy Harvesting
• +3 more

Mimi Xie is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), part of the College of Sciences. She holds a Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh, with prior degrees from Chongqing University. Her research focuses on optimizing energy-efficient, secure, and intelligent IoT systems, particularly in non-volatile memory, energy harvesting, and tiny AI. She leads interdisciplinary efforts addressing challenges in data processing for IoT devices and sustainable computing solutions. Dr. Xie has received the prestigious NSF CAREER Award and co-PI roles in TranSET projects on traffic risk prediction and reinforcement learning. Her work appears in top venues like DAC, ICCAD, DATE, and IEEE journals. She advises multiple Ph.D./Master’s students (Sahidul, Jishnu, Wei, Shyamala) and actively serves on technical program committees for GLSVLSI, IEEE Cloud Summit, and ISVLSI. Key research themes include intermittent systems, energy-efficient AI inference, and secure embedded computing. She has developed frameworks like ELIXIR for self-powered IoT and Autotile for battery-less embedded systems. Over 60+ publications span smart healthcare systems (PPG-based health monitoring), environmental adaptive neural networks, and reinforcement learning for traffic networks. Her academic service includes organizing workshops (e.g., TinyML at DAC 2023), mentoring initiatives like the EDA Job Fair at ICCAD, and outreach efforts such as a virtual summer camp for students with disabilities. She balances technical leadership with educational advocacy, winning the GHC Faculty Award 2020.

Eli De PoorterView profile

Professor

• IoT
• Wireless Network Protocols
• Wireless Testbeds
• +4 more

Eli De Poorter is a Professor at the Faculty of Engineering and Architecture , Ghent University , and affiliated with IMEC since 2017. His work bridges experimental wireless research with machine learning techniques, focusing on IoT , UWB localization , and self-learning networks . Received his PhD in 2011 from Ghent University’s Department of Information Technology FWO Postdoctoral Research Grant recipient Co-author of over 100 international publications Active in program committees of multiple conferences His research spans wireless protocol optimization , interference detection , and system configuration dependencies . Notable projects include UWB-based healthcare monitoring , industrial IoT networks , and animal wearables for agriculture . Develops energy-aware tinyML models for battery-less devices Explores Transformer networks and self-supervised learning for wireless systems Investigates multi-modal industrial IoT and Wi-Fi/UWB hybrid synchronization Scientific Awards FWO Postdoctoral Research Grant Labs & Teams Internet Technology and Data Science Lab (IDLab) IMEC research institute