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Le ChenView profile

Researcher

• Robotics
• Machine Learning
• Reinforcement Learning
• +5 more

Le Chen is a Doctoral Researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems and ETH Zurich , advised by Prof. Bernhard Schölkopf and Prof. Dieter Büchler. Previously, he obtained his M.S. in Electrical Engineering and Information Technology from ETH Zurich and gained research experience at Microsoft Mixed Reality & AI Lab, Tencent AI Lab, and Tencent Robotics X Lab. Research Interests: Le Chen focuses on the intersection of robotics and machine learning, with a specific emphasis on reinforcement learning for dexterous manipulation, visual-inertial calibration, and uncertainty-aware robotic perception. His work spans dynamic motion control, policy gradient subspaces, and novel algorithms for 3D/4D reconstruction. Key Contributions: He co-developed the RP1M dataset for bimanual piano manipulation and contributed to tendon-driven robot design ( Safe & Accurate ) and LEAP-VO for robust visual odometry. His research also includes Gaussian splatting dynamics for 4D content creation ( GaussianFlow ). Scientific Awards: Best Systems Paper Finalist at RSS 2024 for the RP1M dataset

• Robotics
• Computer Vision
• Autonomous Systems
• +3 more

Jesus Pestana Puerta is a researcher at the Institute of Computer Graphics and Vision (ICG) within the Faculty of Computer Science and Biomedical Engineering at TU Graz. He holds a PhD in Automation and Robotics from the Technical University of Madrid (UPM). With over 10 years of experience, he specializes in Aerial Robotics, focusing on vision-based solutions for drone navigation, obstacle avoidance, and autonomous systems. His work bridges research and industry, including prototypes for delivery drones (Post AG) and inventory management systems. Key projects include Overview Obstacle Maps for safe drone navigation and Micro Aerial Projector for stabilized aerial displays. Research interests span visual-inertial odometry, GPS-denied navigation, and technology transfer. He has contributed to robotics competitions (IC CEA, IMAV, IARC) and published extensively in journals like Journal of Field Robotics and conferences such as IROS. His recent work explores cloud-based navigation systems, AI for waste sorting, and automotive embedded systems optimization. Collaborations include startups and industrial partners, emphasizing practical applications of robotics. He has supervised projects in sensor degradation detection, redundant systems, and UAV lifecycle monitoring. His contributions are documented in over 50 publications, with a focus on advancing drone autonomy and real-world deployment.

Yasutaka FurukawaView profile

Associate Professor

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +5 more

Yasutaka Furukawa is a Principal Scientist at Wayve and an Associate Professor of Computing Science at Simon Fraser University (SFU). He leads the SFU Visual Computing Group, ranked 12th globally in Computer Vision and Graphics. His research focuses on 3D reconstruction, neural networks, and computer graphics, with notable contributions in multi-view stereo, CAD modeling, and diffusion models. Furukawa holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008) and has held roles at Washington University in St. Louis, Google, and the University of Washington. Education: Ph.D., Computer Science, UIUC, 2008 Postdoctoral Research, University of Washington (with Prof. Steven Seitz and Brian Curless) Research Interests: Furukawa’s work bridges computer vision and graphics, emphasizing robust 3D reconstruction techniques. His projects include PuzzleFusion++ (diffusion-based fracture assembly), BrepGen (B-rep CAD generation), and HouseDiffusion (floorplan synthesis). He explores applications in autonomous systems, AR/VR, and robotics. Key Projects: Generative Adversarial Networks for architectural design (House-GAN series) Neural networks for inertial navigation (RoNIN) Diffusion models for spatial puzzles and CAD modeling Awards: Recipient of the PAMI Longuet-Higgins Prize (2020), NSF CAREER Award (2015), and multiple Google Faculty Research Awards. Teaching: Courses include CMPT412 Computer Vision and CMPT762 Advanced Computer Vision at SFU. Active in supervising over 30 graduate students and postdocs.

• Computer Science
• Engineering
• Pose Estimation
• +5 more

Sari Peltonen is a University Lecturer in Computing Sciences. She holds a Doctor of Science (Technology) in Information Technology (awarded 2000) and a Master of Science in Mathematics from the University of Tampere (awarded 1996). Her research focuses on computer vision, robotics, and engineering applications, with specialized interests in pose estimation, photogrammetry, retroreflective marker systems, and safety-critical environments. Recent work explores applications in nuclear fusion (ITER) and forest navigation systems. Peltonen's publication trends show strong focus on computer vision solutions for industrial applications (75%), robotics navigation (15%), and interdisciplinary behavioral science (10%). Recent work demonstrates increasing emphasis on safety-critical systems and environmental adaptability. She maintains active peer-review contributions for journals including Scientific Reports and Journal of Electronic Imaging , and conferences such as IEEE ISCAS and EMBEC/NBC.

Kirill KrinkinView profile

Adjunct Professor

• Co-evolutionary Hybrid Intelligence
• Mobile Robotics
• Simultaneous Localization and Mapping (SLAM)
• +4 more

Dr. Kirill Krinkin is an Adjunct Professor at Constructor University and Visiting Professor at Neapolis University , with a PhD in Software Engineering (2004). He serves as Head of Research Lab at JetBrains , focusing on the intersection of AI, robotics, and education technology. Research Focus : Co-evolutionary Hybrid Intelligence : Developing human-AI collaboration frameworks Mobile Robotics : Lightweight perception algorithms (VinySLAM, Multi-agent SLAM) Flipped Learning : LLM-powered lifelong education systems Next-Gen OS : Agent-based scheduling and real-time feedback models Scientific Contributions : Co-authored 6 publications (2017-2023) in hybrid intelligence, robotics, and education 2 granted patents (2022) in flexible manufacturing and autonomous vehicle design

Andreas KunzView profile

Lecturer

• Virtual Reality
• Mixed Reality
• Human-Computer Interaction
• +6 more

Andreas Kunz is a Lecturer at the Department of Mechanical and Process Engineering, ETH Zurich, where he leads the Innovation Center for Virtual Reality (ICVR). His research focuses on developing user-oriented virtual and mixed reality systems tailored for industrial applications across product development processes and digital factories. Current Affiliation: ETH Zurich, Institute for Machine Tools Research Themes: Virtual Reality, Mixed Reality, Human-Computer Interaction, Digital Twin Systems Since joining ETH Zurich in 1994 and completing his habilitation in 2004 on "Interaction with the digital product model in virtual space," Kunz has pioneered VR systems for visualization, collaboration, and haptic interfaces in industrial contexts. His work combines academic research with practical implementation through industry collaborations. Recent research trends show increasing focus on real-world VR applications including: Multiuser redirected walking algorithms Attention guidance systems Industrial MTM motion transcription Smartphone-MR device integration Eye tracking for cognitive analysis Haptic interfaces for control panels The ICVR group under Kunz's leadership maintains strong industry partnerships while producing significant publications in IEEE VR, ISMAR, and ACM VRST conferences. Their work bridges theoretical VR research with practical implementations in manufacturing, assembly, and safety-critical environments.

• High-Performance Integrated Circuit Design
• Artificial Intelligence Accelerators
• Digital Signal Processing Chips
• +3 more

Dr. Fengwei An is an Associate Professor at the Shenzhen-Hong Kong Institute of Microelectronics , Southern University of Science and Technology (SUSTech). He earned his Ph.D. in Engineering from Hiroshima University (2013) , following a Master's (2010) and Bachelor's (2006) from Qingdao University of Science and Technology. Current Role : 2025-Present - Associate Dean and Associate Professor, SUSTech Shenzhen-Hong Kong Institute of Microelectronics Prior Academic Roles : 2017-2018 - Associate Professor, Hiroshima University; 2013-2017 - Assistant Professor, Hiroshima University Industry Experience : 2018-2019 - Chief Engineer, Panasonic Semiconductor Co., Ltd., Japan Research Interests focus on low-power edge artificial intelligence chip design for computer vision, including: Ultra-large-scale digital integrated circuit design System-on-Chip (SoC) integration Image processing and recognition Machine learning hardware Autonomous driving applications High-speed motion tracking systems Publications include over 80 top-tier journal/conference papers (e.g., TCAS-I/II, ESSCIRC, APCCAS) on AI accelerators, stereo vision processors, and sensor interfaces, with 15 recent articles highlighting advancements in stereo matching, video denoising, and reconfigurable coprocessors. Scientific Awards : 2023 - SUSTech Outstanding Teaching Award 2022 - APCCAS Best Paper Nomination 2022 - PrimeAsia Bronze Leaf Award 2020 - Wu Wenjun Artificial Intelligence Science and Technology Award (Second Prize) Patents : 9 Chinese and 3 Japanese inventions in AI chip design, stereo matching systems, and sensor interfaces.

Maani GhaffariView profile

Assistant Professor

• Robotics
• Autonomous Systems
• Applied Mathematics
• +7 more

Maani Ghaffari is an Assistant Professor in the Department of Naval Architecture and Marine Engineering and Robotics at the University of Michigan. His work focuses on robotics, autonomous systems, and the integration of applied mathematics with machine learning. He leads the Computational Autonomy and Robotics Laboratory, advancing research in sensor fusion, state estimation, and geometric control. Ghaffari teaches courses such as NA/EECS 568: Mobile Robotics and ROB 101: Computational Linear Algebra . His research interests include robotic perception, planning under uncertainty, and invariant filtering techniques for navigation systems. He has developed frameworks like GPS-DRIFT for marine robotics and contributed to SLAM algorithms for surgical and underwater applications. Ghaffari's work emphasizes robustness in dynamic environments, leveraging Lie algebraic methods and equivariant neural networks. Notable contributions include correspondence-free point cloud registration, Bayesian semantic mapping, and energy-based terrain modeling for legged robots. His research bridges theory and practice, with applications in surgery navigation, autonomous vehicles, and industrial anomaly detection.

• GNSS (Global Navigation Satellite Systems)
• Indoor Positioning Systems
• Pseudolite Technology
• +7 more

Baoguo Yu is a Professor at the Department of Navigation Science and Technology within the School of Geodesy and Geomatics at Wuhan University. He has established himself as a leading researcher in the field of navigation and positioning systems, with over 70 publications spanning from 2016 to 2025 across prestigious journals including IEEE Transactions on Instrumentation and Measurement, Sensors, and IEEE Access. His research interests primarily focus on GNSS (Global Navigation Satellite Systems), indoor positioning technologies, pseudolite systems, and signal processing for navigation applications. Professor Yu's work demonstrates a strong emphasis on practical implementations, particularly in challenging environments where traditional GNSS signals are degraded or unavailable. His recent publications reveal a growing integration of machine learning techniques with traditional navigation approaches, reflecting the evolving nature of the field. Analysis of his 15 most recent publications shows a clear research trajectory toward high-precision positioning solutions, with particular attention to indoor navigation challenges, LEO satellite navigation augmentation, and robust positioning under interference conditions. His work spans theoretical algorithm development to practical implementations for smartphones, unmanned vehicles, and specialized navigation equipment. Professor Yu maintains extensive collaborations with researchers across China, particularly with scholars from Wuhan University and other prominent Chinese institutions. His frequent co-authors include Lu Huang, Xingli Gan, Heng Zhang, and Chuanzhen Sheng, suggesting well-established research teams working on complementary aspects of navigation technology. His research has significant applications in autonomous vehicles, pedestrian navigation, emergency response systems, and any domain requiring precise positioning where GPS signals are unreliable. The consistent publication output in high-impact journals demonstrates his active research program and continued contributions to advancing navigation science.

Naser El-SheimyView profile

Professor

• Satellite Technologies
• Multi-sensor Systems
• Navigation and Positioning
• +7 more

Dr. Naser El-Sheimy is a Professor at the University of Calgary's Schulich School of Engineering, Department of Geomatics Engineering, and holds the Tier I Canada Research Chair in Geomatics Multi-Sensor Systems. He is a Fellow of the Canadian Academy of Engineering (CAE) and the US Institute of Navigation (ION). His research focuses on multi-sensor systems, GNSS/INS integration, mobile mapping, and autonomous vehicle navigation. He has published over 500 papers and supervised 100+ students. Education: B.S. (Ain Shams University, 1984), M.S. (Ain Shams University, 1990), Ph.D. (University of Calgary, 1996). Entrepreneurial ventures: Founded Micro Engineering Group and Profound Positioning Inc., instrumental in TECTERRA's establishment. Research expertise includes inertial navigation algorithms, MEMS sensors, and applications in transportation and GIS. Awards include the ASTech Leadership Award and Killam Professorship. His work has advanced navigation technologies for self-driving cars and unmanned systems. Editorial roles: Editor-in-chief of Geomatics Journal, associate editor of ION Navigation Journal, among others. Leadership: Former ISPRS Commission I President (2008-2012).

• Robotics
• Resilient Robotic Autonomy
• Robot Control
• +5 more

Prof. Konstantinos Alexis is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on resilient robotic autonomy, emphasizing robot control, path planning, and Simultaneous Localization and Mapping (SLAM). He leads major international grants from agencies like DARPA, NSF, and the Norwegian Research Council, advancing autonomous systems for extreme environments such as subterranean exploration and search-and-rescue operations. His work integrates advanced control strategies with field robotics, demonstrated through projects like the DARPA Subterranean Challenge. Key contributions include robust model predictive control for UAVs, contact-based inspection planning, and graph-based exploration path planning. Alexis has organized workshops on aerial robotics at top conferences like ICRA and ICUAS, and his team develops open-source tools for robotics autonomy. Award-winning publications include the IET 2014 Premium Award and multiple best-paper recognitions. His research extends to solar-powered UAVs, thermal-inertial localization, and multi-modal sensor fusion for degraded environments. The Autonomous Robots Lab at NTNU is central to his research activities.

• Intelligent Sensor Systems
• Autonomous Systems
• Cyber-Physical Systems
• +19 more

Niki Trigoni is a Professor of Computing Science at the University of Oxford, Department of Computer Science, and a Governing Body Fellow at Kellogg College. She leads the Cyber Physical Systems Group and is Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS), a major interdisciplinary program combining robotics, machine learning, sensor systems, and verification/control. Her research focuses on intelligent and autonomous sensor systems, with applications in indoor positioning, healthcare monitoring, environmental sensing, and smart cities. Key interests include data fusion, inference and learning from sensor data, human-robot interaction, communication protocols for sensor networks, and innovative mobile sensing architectures such as participatory and social sensing. Her recent publications (2020–2023) highlight a strong trend in deep learning-based sensor fusion, particularly using mmWave radar, LiDAR, inertial, and visual data. Work spans human sensing (e.g., mmPoint), localization (e.g., PointLoc, RadarLoc), SLAM (e.g., iMag+), and domain adaptation (e.g., illumination-aware thermal detection). The research demonstrates a consistent focus on robustness in GPS-denied environments and real-world deployment. She has secured external funding, including a 3-year NIST grant for indoor positioning systems for emergency responders. She actively supervises a large cohort of PhD students and research associates, many of whom have gone on to notable careers. Her leadership roles in major conferences such as IPSN and Sensys further underscore her impact in the field. Labs and Teams: Cyber Physical Systems Group EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS)

• Accessibility Technology
• Assistive Technologies
• Artificial Intelligence
• +6 more

Prof. Alireza Darvishy is a Professor at the ZHAW School of Engineering, leading research in accessibility technologies and assistive systems. He specializes in enhancing accessibility for people with disabilities through AI, SLAM systems, and digital tools. His work focuses on improving access to educational materials, navigation aids for visually impaired individuals, and inclusive design principles across digital platforms. He has pioneered projects like the 'Sight Guide' wearable navigation system for the Cybathlon and developed the PAVE tool for PDF accessibility. His research also addresses challenges in higher education accessibility, generative AI ethics, and SLAM applications. Key research areas include chart accessibility, PDF remediation, and assistive technologies for mobility impairments. He has collaborated on over 30 projects, including 'Accessible Scientific PDFs for All' and 'Age-Appropriate Digital Channels'. His work emphasizes real-world implementation through datasets like InCrowd-VI and user-centric design approaches. Notable contributions include advancing SLAM systems for indoor navigation, guidelines for accessible teaching at universities, and studies on AI's impact on students with disabilities. He advocates for inclusive technology policies and accessible digital infrastructure in education and tourism.

• Robotics
• Navigation Systems
• Sensor Fusion
• +5 more

Chan Gook Park is a prominent researcher in robotics, navigation systems, and sensor fusion with an extensive publication record spanning over two decades. His work primarily focuses on visual-inertial odometry, LiDAR-based navigation, indoor positioning systems, and applications in both terrestrial and space environments. Dr. Park's research interests encompass robotics, sensor fusion, computer vision, and navigation systems. His work demonstrates a consistent focus on developing robust algorithms for visual-inertial odometry under challenging conditions, including low-light environments and indoor settings. He has made significant contributions to pedestrian navigation using wearable devices, lunar landing navigation systems, and infrared target detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward deep learning integration in traditional navigation systems, with increasing focus on event-based vision sensors, multi-sensor fusion techniques, and applications in wearable technology. His work spans both theoretical algorithm development and practical implementations for real-world navigation challenges. Dr. Park has mentored numerous researchers who frequently appear as first authors on his publications, including Jae Hong Lee, Hanyeol Lee, and In Ho Lee. His collaborative network extends across various Korean institutions, with frequent co-authorship patterns suggesting a well-established research laboratory focused on navigation and sensor systems. His research group appears to maintain strong connections with the aerospace and robotics communities, regularly publishing in top-tier IEEE conferences and journals including IEEE Transactions on Robotics, IEEE Access, and proceedings from ICRA and PLANS conferences. The laboratory seems particularly focused on practical implementations of navigation systems for both terrestrial and space applications.

• Pervasive Computing
• Sensor Systems
• Machine Learning
• +7 more

Berend Jan van der Zwaag is an Assistant Professor at the University of Twente, affiliated with the Digital Society Institute and the Pervasive Systems group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His interdisciplinary work bridges sensor technology, machine learning, and animal biomechanics. His research interests focus on pervasive computing, sensor systems, and machine learning applications, particularly in equine health. He investigates how inertial measurement units (IMUs) and AI models can be used for lameness detection, gait analysis, terrain classification, and kinetic parameter prediction in horses. His work emphasizes real-world deployment of smart, data-driven systems in veterinary and agricultural contexts. The recent publications reveal a strong trend in applying deep learning and recurrent neural networks to time-series sensor data from horses. His research integrates wearable technology with AI to enable precision monitoring of animal locomotion, contributing to both veterinary science and pervasive computing. The interdisciplinary nature of his work spans engineering, computer science, and life sciences. Berend Jan van der Zwaag actively contributes to scientific discourse through expert media commentary, such as on the role of sensors in data-driven maintenance. His research has garnered citations and downloads, indicating growing academic and practical relevance. He supervises students, including J. I. M. Parmentier, whose work on equine IMU data forms a significant part of recent publications. While no formal grants are listed, his output suggests involvement in collaborative, applied research projects. His work is supported by open-access dissemination and digital research metrics platforms like Pure and PlumX. Berend Jan van der Zwaag is part of a dynamic research network at the University of Twente, contributing to the Pervasive Systems lab, which focuses on intelligent, context-aware sensor systems for societal challenges. His work exemplifies the convergence of digital technology with real-world applications in health and sustainability.