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Jinsung LeeView profile

Researcher

• Cellular/WiFi Systems
• 5G Standard
• Indoor Localization
• +7 more

Jinsung Lee is a Postdoctoral Research Associate at the University of Colorado and a Senior Staff Engineer at Qualcomm. His research focuses on wireless network design, cellular/Wi-Fi systems, 5G architecture, transport protocols, and deep learning applications in mobile computing. He has contributed to projects like Optimal CSMA (oCSMA) and PERCEIVE, which leverage deep learning for cellular uplink prediction. Key publications include studies on 5G edge computing, energy-efficient transportation mode recognition via sound analysis, and secure LTE network protocols. His work has received Best Paper Awards at ACM MobiSys'19 and IEEE SECON'13. Lee’s research addresses challenges in network performance, security, and resource optimization across cellular, Wi-Fi, and multi-path TCP environments. Roles: Postdoc (University of Colorado), Senior Staff Engineer (Qualcomm) Key Projects: oCSMA Implementation, PERCEIVE, CASTLE over the Air Areas: 5G, Transport Protocols, Deep Learning, Wireless Security

Yudi DaiView profile

Research Fellow

• 3D Computer Vision
• Scene Mapping
• 4D Human Motion Capturing
• +4 more

Yudi Dai is a Postdoctoral Research Fellow at the ETH AI Center, collaborating with Professors Siyu Tang and Christian Holz. He holds a PhD from Xiamen University (2024), where he also completed his B.E. (2016) and M.Sc. (2019). His research focuses on 3D Computer Vision, emphasizing scene mapping, 4D human motion capturing, and human-scene interaction reconstruction. He aims to develop robust methods for understanding human behavior in complex environments. Education: Ph.D. in Computer Science, Xiamen University, 2024 M.Sc. in Computer Science, Xiamen University, 2017-2019 B.E. in Computer Science, Xiamen University, 2016 His research interests include multimodal datasets (e.g., ClimbingCap, SLOPER4D), sensor fusion (IMUs+LiDAR), and human-centric scene capture. Notable contributions include HSC4D for large-scale indoor-outdoor scene reconstruction and HiSC4D for human-scene interaction modeling. His work bridges computer vision, robotics, and sensor technology for applications in AR/VR, autonomous systems, and urban modeling. Publications span CVPR, TPAMI, and IEEE journals, emphasizing scene-aware human motion analysis and dataset development. Collaborations involve institutions like ShanghaiTech University and the Xiamen University Sensing and Computing Lab. His datasets are widely cited for their focus on real-world, challenging environments. Current work explores dynamic scene reconstruction, wearable sensor systems, and human-environment interaction modeling. His lab at the ETH AI Center focuses on advancing AI-driven perception in unstructured environments.

• Tissue Engineering
• Biofabrication
• Computer Vision
• +3 more

Dr. Hao Liu is a Researcher in the Department of Tissue Technology and Biofabrication at ETH Zurich's Institute for Biomechanics. His work intersects tissue engineering, biofabrication, and advanced computational techniques such as computer vision and 3D reconstruction. He focuses on interdisciplinary applications, including robotics and neural radiance fields (NeRF) for biomedical and robotic systems. Institution: ETH Zurich Role: Researcher (Staff of Professorship for Tissue Engineering and Biofabrication) Research interests span tissue engineering, biofabrication, and computational methods like 3D reconstruction, visual localization, and neural networks. His recent work emphasizes robust 3D scene understanding, camera pose estimation, and deep learning for multi-view stereo. Publications highlight advancements in relative pose estimation, neural radiance fields, and line-based geometric analysis, contributing to both robotics and biomedical engineering fields. He has no listed scientific awards, but his research demonstrates significant innovation in computational imaging and biofabrication. No advising or grant details are provided in the available texts. His affiliation with the Institute for Biomechanics suggests involvement in labs focused on biomechanical systems and biofabrication technologies.

Stefan MangoldView profile

Lecturer

• Wireless Communication
• Visible Light Communication
• Internet of Things (IoT)
• +3 more

Dr. Stefan Mangold is a Lecturer in the Department of Computer Science at ETH Zürich, where he has taught courses on Wireless Networking and Mobile Computing since 2007. He is also the founder and General Manager of Lovefield Wireless, focusing on wireless communication protocols and systems for applications like visible light communication (VLC), IoT, and delay-tolerant networks. His academic and industry work spans themes such as wireless LAN optimization, VLC integration with IoT, and networking solutions for under-served regions. Stefan holds a Ph.D. in Electrical Engineering/Telecommunications from RWTH Aachen University. His research emphasizes practical, scalable solutions for wireless systems, including projects at Disney Research, Swisscom, and Philips. Key areas include VLC protocols, IoT connectivity, and ethical challenges in networked systems. His publications cover innovations like EnLighting (VLC using light bulbs), delay-tolerant networks for remote areas, and gesture-based device association. While no specific awards are listed, his work bridges academia and industry, reflecting contributions to both theoretical and applied wireless systems. Labs and teams: Lovefield Wireless, a hub for research and development in VLC and IoT networking.

• Internet of Things (IoT)
• Sensors and Sensor Networks
• Energy Harvesting
• +3 more

Philipp Mayer is a Lecturer at ETH Zürich's Department of Information Technology and Electrical Engineering. His work focuses on IoT, energy-efficient sensors, and embedded systems. He has contributed to advancements in underwater communication, energy harvesting, and wearable sensor technologies. His research includes innovations in low-power sensor networks, RF energy transmission, and AI-driven embedded systems. Notable projects include the design of self-sustaining sensors and high-precision localization systems using UWB and LoRa technologies.

• 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.

• Integrated Circuits
• Wireless Communication
• Signal Processing
• +5 more

Tae Wook Kim is an academic researcher specializing in integrated circuits , wireless communication , and signal processing with a focus on CMOS technology and IR-UWB systems . His work bridges electrical engineering and biomedical applications , including brain-computer interfaces and mobile robotics. Key research areas : Integrated Circuits, Wireless Communication, Signal Processing, CMOS Technology, Biomedical Engineering Research Trends Recent publications highlight advancements in: Low-power UWB transceivers for brain-computer interfaces High-speed analog circuits with linearity improvements AI-driven radar systems for gesture recognition Time-domain signal processing for precision localization Notable Contributions Kim has co-authored foundational papers on: CMOS track-and-hold circuits (IEEE Access 2024) Volterra series analysis for distortion reduction (IEEE Access 2024) Passive IR-UWB localization for UAVs (IEEE Access 2023) Time-domain AI radar systems (IEEE J. Solid State Circuits 2020)

• Telecommunications
• Wireless Communication Systems
• Machine Learning
• +5 more

Jianzhong Zhang is a researcher affiliated with Samsung Research America (Plano, TX, USA) , City University of Hong Kong (Department of Mathematics) , and Nankai University (College of Computer and Control Engineering) . His work focuses on 6G networks , machine learning applications in telecommunications , and advanced wireless technologies like THz/mmWave communication and beamforming . Recent publications highlight his contributions to Large Language Models (LLMs) for wireless network optimization , physics-inspired deep learning frameworks for channel state feedback, and ultra-wideband channel measurements for THz/FR3 bands. He also explores AI-driven CSI prediction , network slicing , and in-context learning for power control. His research integrates signal processing , channel modeling , and deep reinforcement learning to address challenges in next-generation wireless systems . Collaborative work includes joint phase-time arrays for frequency-dependent beamforming and end-to-end THz platform demonstrations .

Peng ChengView profile

Senior Lecturer

• Artificial Intelligence
• Internet of Things (IoT)
• Wireless Communications
• +5 more

Dr. Peng Cheng is a Senior Lecturer and PhD advisor in the Department of Computer Science and Information Technology at La Trobe University, Australia. He is also an Honorary Senior Lecturer at the University of Sydney. His research focuses on AI-driven IoT, wireless communications, and edge computing. He leads the Cisco-LTU Research Centre for AI and IoT and has secured over $1.3M in ARC grants. Notable contributions include pioneering 'wireless AI' research and developing graph-based deep reinforcement learning algorithms for dynamic resource allocation in 6G networks. His work bridges IoT with AI, addressing challenges in edge computing, network optimization, and sensing technologies. Current projects involve WiFi sensing for healthcare, satellite communications, and energy-efficient 5G/6G systems. Education: PhD in Communication and Information Systems from Shanghai Jiao Tong University. Prior roles include Postdoctoral Research Scientist at CSIRO (2014–2017) and ARC DECRA Fellow at the University of Sydney (2017–2020). Awards: ARC DECRA Fellowship (2020–2022) Teaching: Machine Learning, Data Communications, and Satellite Communications Labs: Cisco-LTU AIoT Centre, US-Cisco Collaboration Network

Vishnu Dutt SharmaView profile

Researcher

• Robotics
• Computer Vision
• Deep Learning
• +4 more

Vishnu Dutt Sharma is a Robotics and Visual Computing Researcher at Nokia Bell Labs . His work focuses on robot perception, planning, and deep learning applications in autonomous systems. He holds a PhD in Robotics from the University of Maryland, College Park , advised by Dr. Pratap Tokekar, and earned his MTech and BTech in Electronics and Electrical Communication Engineering from IIT Kharagpur in 2017, with a minor in Computer Science. His research interests span reinforcement learning, computer vision, and interpretable AI, particularly in robotics and natural language processing. Before his PhD, he worked at American Express India on credit risk management using machine learning. He has interned at Nokia Bell Labs, Comcast, and FlytBase Labs. He received the Kulkarni Foundation Summer Research Fellowship in 2020 for his work at UMD. Key contributions include advancements in robot navigation, next-best-view planning, and generative communication in embodied agents. His interdisciplinary work bridges robotics, computer vision, and NLP, with applications in healthcare, autonomous systems, and security.

• Reinforcement Learning
• Model-based Predictive Control
• Parametric Uncertainty
• +6 more

Congcong Sun is an Assistant Professor in Agricultural Biosystems Engineering, specializing in control systems for agricultural environments and water distribution networks. Her research spans predictive modeling, reinforcement learning, and sensor technology integration. Affiliation: Agricultural Biosystems Engineering Academic Rank: Assistant Professor Research Focus: Congcong Sun's work emphasizes: Reinforcement Learning for climate control in greenhouses Model-based Predictive Control (MPC) for water distribution systems Handling Parametric Uncertainty in urban drainage networks Multisensor Fusion for precision agriculture Food Security through advanced agricultural technology Collaborative Projects: Current projects include: Soft and Biodegradable Sensing Technologies for Precision Soil and Crop Management Zero-emission Indoor Horticulture with Renewable Energy Autonomous Plant Factory Light Control using Phenomics SMARTFARMING: Vertical Farming with Self-learning Control Manure Removal Robotics in Dairy Barns Scientific Contributions: Her publications focus on: Deep Learning for Temperature Prediction in Solar Greenhouses Stochastic MPC for Urban Drainage Systems Sliding Mode Control for Nonlinear Systems Safe Stabilization Techniques for Robotic Systems Data-Driven Pipe Life Prognosis in Water Networks Advising & Collaboration: Active advisor for multiple PhD and EngD candidates, collaborating with researchers like E. van Henten, P. Groot Koerkamp, and G. Kootstra on agricultural automation and water system controls.

Le Chung TranView profile

Associate Professor

• Wireless Communications
• Signal Processing
• 5G Systems
• +7 more

Assoc. Professor Le Chung Tran holds a Ph.D. in Telecommunications Engineering from the University of Wollongong, Australia. He is currently an Associate Professor in the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong, with roles including School Coordinator of Equity, Diversity, and Inclusion (EDI). His research focuses on wireless communications, signal processing, IoT, 5G systems, and localization technologies. Tran has extensive experience, including postdoctoral fellowships at the University of Luebeck (Germany) under the Alexander von Humboldt Foundation and academic positions in Vietnam. Education: Ph.D. in Telecommunications Engineering, University of Wollongong, Australia (2006) M.Eng. (Highest Distinction), Hanoi University of Transport and Communications, Vietnam B.Eng. (First Class Honours), Hanoi University of Transport and Communications, Vietnam Research Interests: Tran's work spans 5G, IoT, multiple-input multiple-output (MIMO) systems, ultra-wideband (UWB) communications, localization, and network coding. He has led projects on AI-driven environmental monitoring, non-GPS positioning, and underwater mine detection. Awards: UOW OCTAL Award (2019) Alexander von Humboldt Fellowship (2006-2008) WUS Awards (twice) Grants & Projects: AI-assisted Obstacle Detection System for Vision-Impaired People (2023-2024) Smart Eye: Airborne AI-Driven Sugarcane Assessment (2022) Aus4Innovation-funded Environmental Dashboard for Tram Chim National Park (2021) Labs/Teams: Active in the Wireless Technologies Laboratory and collaborations with industry partners like CSIRO and Agere Systems.

• Wireless Communications
• Signal Processing
• Image Steganography
• +5 more

Dr. Peter Vial is a Senior Lecturer at the School of Electrical, Computer and Telecommunications Engineering, University of Wollongong. He holds a PhD in Electrical Engineering and has over three decades of experience in academia and industry. His academic journey includes a Bachelor's (1987), Master's (1997), and PhD (2009) from the University of Wollongong. Before academia, he worked at BHP Steel (1981-1991) and transitioned into teaching roles from 1992, becoming a Lecturer in 2004 and Senior Lecturer in 2022. Research Interests: Dr. Vial’s work focuses on wireless communication systems, including MIMO, spread spectrum, and ultra-wideband (UWB) technologies. He also explores signal processing applications in medical imaging (e.g., capsule endoscopy), steganography, energy harvesting, and robotics. His research integrates machine learning and embedded systems for practical solutions in healthcare, telecommunications, and sensor networks. Teaching & Supervision: Dr. Vial teaches courses in telecommunications, digital signal processing, and electronics. He has led laboratory development in embedded systems and supervised over 15 postgraduate students, including current PhD candidates in Delay Tolerant Networks (DTN), 60GHz Ad Hoc networks, and UWB channel measurements. His supervision spans topics like AI-driven sign language recognition, steganography, and energy-efficient generator design. Grants & Funding: He led an internal grant (2008-2009) developing an immersive learning platform for microcontroller education. His research has been supported by institutional funding, with outputs published in journals like Neurocomputing , Signal Processing , and IEEE Sensors . Labs & Teams: Dr. Vial has developed engineering laboratories in embedded Internet systems, telecommunications, and digital signal processing. He collaborates with industry partners and interdisciplinary teams on projects combining hardware/software systems for healthcare, energy, and robotics.

• Wireless Sensing
• Signal Processing
• Machine Learning
• +4 more

Wenda Li is an Assistant Professor at the Institute of Sensors, Signals & Systems , part of the University of Heriot-Watt 's School of Engineering & Physical Sciences. His research focuses on wireless sensing technologies, signal processing, and their applications in infrastructure monitoring, healthcare, and communication systems. He leads projects involving WiFi-based passive radar, RIS-aided networks, and machine learning-driven optimization strategies. Research interests include: Non-contact human activity recognition using WiFi signals Indoor localization and multi-user tracking Infrastructure assessment via 3D surface analysis Reconfigurable intelligent surface (RIS) optimization for IoT networks Machine learning integration in communication systems Recent work highlights include: A novel WiFi-based tracking system achieving sub-meter accuracy Automated 3D crack assessment for pavement maintenance RIS-aided semantic communication strategies Collaborations span global institutions, with active projects in China, UK, and Europe. His work has produced 9+ peer-reviewed publications since 2024, emphasizing practical applications of wireless sensing in healthcare and infrastructure domains.

James MountView profile

Part-Time Lecturer

• Robotics Engineering
• Autonomous Vehicles
• Computer Vision
• +3 more

Dr. James Mount is a robotics engineer, STEM communicator, and sessional academic at Queensland University of Technology (QUT). He specializes in designing robotic systems for real-world applications and promoting technology education through engaging demonstrations. His research focuses on autonomous vehicles, visual localization, and robotics engineering. Mount holds an Honours Degree in Mechatronics Engineering and is awaiting his PhD Final Thesis Examination reviews from the Australian Centre for Robotic Vision. Education: Bachelor of Mechatronics Engineering (Honours) PhD Candidate in Robotics (pending final thesis review) Research interests include autonomous vehicle infrastructure readiness, vision-based robotics, and STEM education outreach. Notable projects include assessing Australian road infrastructure for autonomous vehicles and developing the Carlie mini-autonomous vehicle platform. He has presented at international conferences such as ICRA and IEEE Robotics and Automation Letters. Mount has delivered interactive STEM presentations to large audiences, including guest appearances on children’s TV shows like Brain Buzz and Juiced TV. His work bridges technical innovation with public engagement, emphasizing ethical AI considerations through interactive demonstrations like the Robotronica Trolley Problem exhibit. Labs/Teams: Core contributor to QUT’s Centre for Robotics and the Australian Centre for Robotic Vision. Lead engineer on the Carlie project and BBot Kickstarter initiative.