جست‌وجوی استاد و پژوهشگر

• Spatial Cognition
• Computational Neuroscience
• Cognitive Neuroscience
• +۵ مورد دیگر

Professor Hanspeter A. Mallot is a distinguished academic in the Department of Biology within the Faculty of Mathematics and Natural Sciences at Eberhard Karls University Tübingen. Appointed Professor of Cognitive Neuroscience in 2000, he leads research in spatial cognition, computational neuroscience, and vision processing. His work bridges biological and artificial systems, exploring how humans and robots navigate and perceive spatial environments. Dr. Mallot received his PhD from the Faculty of Biology at the University of Mainz, Germany, in 1986. Following his doctoral studies, he held prestigious postdoctoral and research positions at the Massachusetts Institute of Technology, Ruhr-University Bochum, the Max Planck Institute for Biological Cybernetics in Tübingen, and the Institute for Advanced Study in Berlin. Professor Mallot's research primarily focuses on spatial cognition in humans and robots. His laboratory employs behavioral experiments in virtual reality, eye-movement recordings, and simulated agents in both hardware and software environments. His work spans computational neuroscience, cognitive science, and robotics, with particular emphasis on how visual information is processed for navigation and spatial orientation. His research has significant implications for both understanding human cognition and developing more sophisticated artificial navigation systems. His publication record demonstrates consistent contributions to the fields of spatial cognition and computational neuroscience. Over the past decade, his research has increasingly integrated neuroscientific approaches with computational modeling, exploring topics such as path integration, visual homing, spatial memory systems, and the neural basis of navigation. His work often bridges multiple disciplines, combining insights from psychology, neuroscience, computer science, and robotics to develop comprehensive models of spatial cognition. Professor Mallot serves on the editorial board of "Spatial Cognition and Computation" and the "Neuroscientific Society" (NWG). He has previously held leadership positions as president of the European Neural Network Society (ENNS) and the German Society for Cognitive Science (GK), and served on the Neuroscience review panel of the German Research Foundation. He currently leads several major research initiatives including EU Strep CURVACE, the DFG Research Training Group Bioethics, the Center for Integrative Neuroscience (CIN), and the Bernstein Center for Computational Neuroscience Tübingen (BCCN). These projects reflect his interdisciplinary approach, combining neuroscience, cognitive science, and computational modeling to address fundamental questions about spatial cognition. Professor Mallot has established several notable research laboratories and teams focused on spatial cognition and computational neuroscience. His work has been supported by prestigious funding bodies including the European Union and the German Research Foundation (DFG). His research group collaborates extensively with other institutions across Europe, particularly on projects related to robot navigation and spatial cognition in virtual environments.

• Automatic Speech Recognition
• Conversational User Interfaces
• Human-Computer Interaction
• +۶ مورد دیگر

Prof. Hans-Peter Hutter is a Professor of Computer Science at the ZHAW School of Engineering, specializing in Deep Learning-based Automatic Speech Recognition, Conversational User Interfaces, and Human-Centered Computing. He leads the Human-Centered Computing research group at InIT/ZHAW and has held this position since 2005. His work focuses on accessibility technologies, mobile usability, and inclusive design for visually impaired users. Education: Dr. sc. techn. ETH in Computer Engineering (ETH Zurich, 1996) Dipl. El.-Ing. ETH in Electrical Engineering (ETH Zurich, 1986) Research Interests: Advancing accessibility in digital systems (e.g., accessible PDFs, navigation aids for visually impaired users) Speech recognition and dialogue systems Mobile application design principles Service engineering and platform development His recent work emphasizes multimodal interaction, accessible document remediation, and SLAM systems for navigation assistance. Projects: Leading the InCrowd-VI dataset project for indoor navigation Developing MathNet for mathematical expression recognition Creating accessible tourism services in Lake Constance region Grants & Labs: Active in EU-funded and industry collaborations, leading the InIT Institute founded in 2002. Collaborates with organizations like SwissICT and ACM.

• Computer Vision
• Deep Learning
• Medical Imaging
• +۳ مورد دیگر

Dr. Mecocci Alessandro is a Full Professor at the Department of Information Engineering and Mathematical Sciences of the University of Siena . He specializes in computer vision, deep learning, and medical imaging, with a focus on applications in healthcare, robotics, and IoT systems. His research addresses challenges in automated diagnostics, real-time surveillance, and environmental monitoring. Teaching Roles : He leads courses such as Advanced Digital Image Processing (Master’s in Electronics & Communications Engineering) and Image and Multimedia Laboratory (Bachelor’s in Computer & Information Engineering). His academic activities emphasize practical training in computer vision and signal processing. Research Interests : His work spans deep learning , medical image analysis , UAV navigation , and IoT infrastructure . Notable projects include real-time ambulance detection systems, automated urine culture analysis, and smart waste management frameworks. Publications : Over 50 peer-reviewed articles highlight his contributions to fields like retinal imaging , glomeruli segmentation , and coastal management systems . Recent trends focus on integrating clinical data with AI for medical diagnostics and optimizing IoT sensor networks for environmental applications. Advising & Grants : While no specific grants are listed, his research collaborations span academic and industrial partners. He mentors students in advanced imaging techniques and IoT applications. Labs & Teams : His work is conducted within the University of Siena’s engineering departments, with contributions to cross-disciplinary projects in healthcare technology and robotics.

• Computer Vision
• Deep Learning
• Biomedical Imaging
• +۴ مورد دیگر

Meng Xu is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Her research focuses on computer vision, deep learning, and biomedical imaging applications within robotics and healthcare domains. She contributes to advancing localization systems, image processing techniques, and neural network architectures for real-world challenges. Her work bridges theoretical advancements with practical implementations, such as end-to-end visual localization networks (e.g., Bev-locator) and unsupervised methods for biomedical image enhancement. She also explores multi-modal fusion strategies (e.g., MCAPR) and optimization techniques for efficient deep learning models. Key research directions include improving camera pose estimation accuracy, developing robust feature matching algorithms, and applying deep learning to body shape classification and human pose estimation. Her publications reflect a strong emphasis on solving technical challenges in both robotics and medical imaging through innovative computational approaches.

Kailai Liمشاهده پروفایل

استادیار

• Robotic Perception
• Sensor Fusion
• State Estimation
• +۲ مورد دیگر

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

Sajad Saeedi Gharahbolaghمشاهده پروفایل

استادیار

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Focal-plane Sensor-processor Arrays (FPSP)
• +۶ مورد دیگر

Sajad Saeedi Gharahbolagh is an Assistant Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University and an Honorary Research Fellow at Imperial College London's Department of Computing. His research spans robotics, SLAM, focal-plane sensor-processor arrays (FPSP), and deep learning for autonomous systems. Education : PhD in Electrical and Computer Engineering (2014) from the University of New Brunswick. Prior Roles : Dyson Research Fellow (2018-2019) at Imperial College London; Postdoctoral Fellow at University of New Brunswick (2014); R&D Engineer at 2G Robotics (2015). His research focuses on Simultaneous Localization and Mapping (SLAM) for single/multi-robot systems Focal-plane Sensor-Processor Arrays (FPSP) for high-speed, low-power vision processing Autonomous aerial/underwater robotics Deep learning integration with traditional robotics algorithms Control systems for heterogeneous robotic platforms His work addresses challenges in computational efficiency, robustness in GPS-denied environments, and real-time multi-sensor data fusion. Recent publications highlight advancements in Distributed NeRF for collaborative mapping MR.CAP multi-robot control/planning BIT-VIO visual-inertial odometry WiFi-based geometric mapping FPSP-optimized CNNs PathBench benchmarking framework Scientific Awards : Dyson Research Fellowship (2018-2019) Best Robotics Paper (CRV 2021) Best Student Presentation (IROS 2023) Research Team : PhD Students: Christopher Kolios, Navid Zarrabi, Messiah Esfahani, Ishaan Mehta, Mahboubeh Asadi, Jack Saunders MASc Students: Georgia Jovanovic, Hussein Ali Jaafar, Austin Vuong, Roni Sherman, Matthew Lisondra, Glenn Shimoda, Ali Babaei, Robel Efrem, Messiah Ataey, Christopher Kolios, Nikolas Kourtzanidis Laboratory Facilities : Robotics and Computer Vision Lab (RCVL) with Vicon motion capture system 14 TurtleBot 3 platforms (Waffle Pi/Burger variants) Germicidal UVC-equipped G-Robots Jetbots with onboard GPU processing OpenMANIPULATOR robotic arms

KHOO Boo Cheongمشاهده پروفایل

استاد

• Fluid Mechanics
• Computational Fluid Dynamics
• Numerical Methods
• +۷ مورد دیگر

Professor KHOO Boo Cheong is a faculty member in the Department of Mechanical Engineering at the National University of Singapore (NUS), affiliated with the College of Design and Engineering. He holds a BA (Hons.) from the University of Cambridge (1980), an MEng from NUS (1984), and a PhD from MIT (1989). His research focuses on Computational Fluid Dynamics (CFD) , Numerical Methods , and Fluid Mechanics , with applications to aerodynamics, turbulence, and fluid-structure interaction. Recent work emphasizes machine learning integration in CFD, bio-inspired UAV design, and icing mechanics for aerospace systems. Key research trends include advancing high-fidelity simulations for complex flows (e.g., turbulence modeling, cavitation suppression), optimizing fluid-structure systems (e.g., lightweight shells, train aerodynamics), and developing novel experimental techniques (e.g., light field PIV, wind spectra analysis). His contributions bridge theoretical CFD innovations with practical engineering challenges in transportation, renewable energy, and robotics. Professor Khoo collaborates on projects involving pressure gain combustion systems , ventilated supercavitating models , and quantum algorithms for fluid dynamics . His lab’s work often combines advanced numerical modeling with experimental validation, addressing real-world issues like wind effects on trains, ice accretion on airfoils, and autonomous navigation systems.

• Robotic Vision
• SLAM Systems
• 3D Reconstruction
• +۱ مورد دیگر

Viorela Ila is Senior Lecturer at University of Sydney's School of Aerospace, Mechanical and Mechatronic Engineering. Her research develops perception algorithms for robotics, specializing in visual SLAM and 3D reconstruction. She contributes to the Centre for Robotics and Intelligent Systems. Research advances robust state estimation in dynamic environments. Recent work provides convergence guarantees for visual-inertial SLAM systems. Awarded Best Paper honors and MICINN/FULBRIGHT fellowship for contributions to robotic vision.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jia Liu is a Professor at Tsinghua University's School of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning multiple disciplines in computer science and engineering. Her extensive publication record demonstrates leadership in artificial intelligence, machine learning, and their applications across diverse domains. Dr. Liu's research interests encompass the development and application of advanced computational techniques to solve complex real-world problems. She has pioneered innovative approaches in neural network architectures, medical imaging analysis, remote sensing applications, and multi-agent systems. Her work on Asym-UNet and ThreeF-Net has significantly advanced breast lesion segmentation techniques, while her contributions to digital twin technology have enhanced nuclear reactor operations and Martian exploration systems. Analysis of Dr. Liu's recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications in healthcare, environmental monitoring, defense technology, and education. Her work demonstrates consistent innovation in algorithm development, particularly in adapting machine learning techniques to domain-specific challenges. Dr. Liu maintains an active research program with numerous collaborations across academia and industry. Her work on federated learning for dual carbon initiatives reflects her commitment to addressing global sustainability challenges through technological innovation. As an educator, Dr. Liu has developed innovative teaching methodologies that integrate virtual reality and game-based learning, particularly in language education. Her research on interactive e-learning environments demonstrates her dedication to improving educational outcomes through technology.

• Computer Vision
• Robotics
• 3D Reconstruction
• +۳ مورد دیگر

Luis Puig is an academic researcher specializing in computer vision and robotics, focusing on omnidirectional imaging systems, 3D reconstruction, and sensor calibration. His work bridges theoretical advancements with practical applications in navigation assistance for visually impaired individuals, intelligent tutoring systems, and multi-camera system integration. Key contributions include: Pioneering research in omnidirectional vision systems, including calibration techniques and feature extraction. Development of algorithms for 3D tracking, deformable surface modeling, and RGB-D sensor fusion. Advances in visual SLAM (Simultaneous Localization and Mapping) and stereo visual odometry. His interdisciplinary approach also extends to educational technology, designing adaptive learning systems for mathematical problem-solving. Over 23 publications in top venues like IEEE Trans. PAMI, CVIU, and ICRA reflect his expertise in both technical and applied domains.

Andry Maykol Pintoمشاهده پروفایل

استادیار

• Robotics
• Autonomous Vehicles
• Machine Learning
• +۵ مورد دیگر

Andry Maykol Pinto is a Senior Researcher at the Center for Robotics and Autonomous Systems at INESC TEC and an Assistant Professor at the Faculty of Engineering of the University of Porto. He earned a Ph.D. (2014) and M.Sc. (2010) in Electrical and Computer Engineering from the University of Porto, focusing on Robotics. Research Interests: His work spans Robotics, Autonomous Vehicles, Machine Learning, Underwater Imaging, 3D Reconstruction, and Marine Robotics. He develops advanced perception systems for UAVs and AUVs, focusing on multimodal data fusion, visual transformers, and reinforcement learning to address navigation and reconstruction challenges in offshore and underwater environments. Supervised Theses (2023): Eduardo Jorge Pereira Gonçalves: Deep learning for visual odometry in ASVs Daniel Filipe Barros Campos: Multi-domain awareness for offshore wind farm inspection João Afonso Borges Carvalho: AI for marine growth classification Francisco Soares Pinto da Silva Neves: RL-based UAV inspection of windmills João Basto do Rosário: Ego motion analysis from video data Gabriel da Silva Martins Loureiro: Edge intelligence for seafloor perception João Francisco Sengo Guimarães: Machine vision for deep-space images Maria Inês Rodrigues Pereira: Multi-agent RL for maritime perception João Rodrigues Mina: Quality assessment for underwater imaging Rafael Marques Claro: Robust UAV landing perception Contact: andry.m.pinto@inesctec.pt | +351228340554

Raimund Edlingerمشاهده پروفایل

پژوهشگر

• Robotics
• Autonomous Systems
• Rescue Robotics
• +۲ مورد دیگر

Raimund Edlinger is a research-focused academic at the University of Applied Sciences Upper Austria, Wels campus, affiliated with the Research Center Wels. He leads the Center of Excellence for Automotive/Mobility and Smart Production, with specialized focus areas in Digital Transformation, Rail Automation, Telematics, and Smart Automation and Robotics. His interdisciplinary work bridges engineering and practical robotics applications across diverse environments. Edlinger's research centers on advancing robotics for real-world challenges, particularly in GNSS-denied scenarios and hazardous environments. His expertise spans autonomous navigation, sensor fusion (LiDAR, thermal imaging, tactile systems), and human-robot interaction, with significant contributions to rescue robotics, underground mapping, and space analog missions. Recent work emphasizes digital twin applications for Mars simulations and tactile terrain assessment to enhance robot mobility in complex terrains. Analysis of his publication trends reveals a strategic shift toward multi-sensor integration for reliability in adverse conditions (e.g., smoke, narrow tunnels, Mars analogs). His 2022-2025 output shows concentrated effort in practical deployment challenges: casualty evacuation systems, nuclear disaster response, and cost-effective sensing solutions, reflecting strong industry and emergency response collaboration. His competitive robotics achievements include: 1st place EnRicH 2021 (Manipulation category) Multiple RoboCup podium finishes (2015-2022) in Rescue League and Best-in-Class Manipulation German Open championship recognitions across five consecutive years Edlinger directs significant research initiatives including the active MOBES project (Mobile Erdstall Scanner, 2025-2026), MARK Racing Team, and Autility platform development, funded by Austria's Climate and Energy Fund and ICT programs. He supervises student research while contributing to national robotics strategy through activities like ÖBFV Führungsseminar workshops on digitalization. His work is operationalized through the RRT-Roboto-Racing Team and specialized labs within Research Center Wels, featuring underground structure simulators, Mars analog testbeds, and sensor calibration facilities for extreme environments. Current projects integrate 5G communication and AI-driven manipulation for next-generation rescue systems.

• Robotics
• Autonomous Systems
• Control Systems
• +۵ مورد دیگر

Jonay Tomás Toledo Carrillo is a Full Professor at the University of La Laguna, affiliated with the Department of Computer Science and Systems Engineering within the School of Computer Science and Systems Engineering. He leads the GRULL (Robotics Group of the University of La Laguna), focusing on robotics, control systems, and autonomous systems. He earned his PhD in 2008 with a thesis on nonlinear control strategies for quadrotor helicopters, advised by Dr. Leopoldo Acosta Sánchez. His research interests span robotics, autonomous navigation, sensor fusion, and assistive technologies. Key projects include developing mobility aids for visually impaired individuals and enhancing localization systems through adaptive algorithms. Recent work emphasizes real-time sensor processing (e.g., LSTM networks for odometry), magnetic field control for accessibility, and Kalman filter variants for multi-sensor integration. Publications highlight contributions to robotics applications, including autonomous vehicles, wheelchair navigation, and BCI systems. His work bridges theoretical control methods with practical implementations in unstructured environments. Collaborations include interdisciplinary efforts in computer vision, ontology-based navigation, and educational simulators like MNEME for memory hierarchy teaching. Dr. Toledo Carrillo’s research aligns with the university’s engineering and automation priorities, with a focus on real-world impact through low-cost, high-precision solutions. His team’s innovations address challenges in both robotics engineering and accessibility technologies.

• Neuromorphic Computing
• Neural Dynamics
• Robotics
• +۴ مورد دیگر

Dr. Yulia Sandamirskaya is the Head of the Research Center 'Cognitive Computing in Life Sciences' at Zurich University of Applied Sciences (ZHAW), where she leads a research group focused on neuromorphic cognitive architectures for embedded AI systems. Her work integrates neural dynamics with robotic control , spanning real-time tasks such as sensing, planning, decision-making, learning, and motor control for assistive robots. She collaborates extensively with institutions like ETH Zurich and KTH Royal Institute of Technology, utilizing neuromorphic hardware such as dynamic vision sensors (DVS) and robotic platforms. Her research encompasses neuromorphic architectures for obstacle avoidance, path integration, and sequence learning, as well as theoretical frameworks like Dynamic Neural Fields (DNFs) for cognitive modeling. She explores autonomous learning mechanisms, including unsupervised and reinforcement learning, and applies these to robotics in domains such as spatial language grounding and haptic exploration . Her group has published extensively on topics like event-based vision , resonator networks , and hardware implementations of neural algorithms. Dr. Sandamirskaya has supervised multiple MSc theses on neuromorphic systems and robotic applications, with advisees working on projects like UAV obstacle avoidance, tactile object recognition, and neural-dynamic sequence generation. She actively contributes to workshops and conferences, including Science Robotics , CVPR , and IEEE conferences , and her work addresses challenges in low-power, high-speed robotic agents operating in real-world environments.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.