جست‌وجوی استاد و پژوهشگر

• Robotics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Sebastian Thrun is a Professor at Stanford University, USA, with a prolific research career spanning robotics, artificial intelligence, and machine learning. His work has significantly impacted autonomous vehicle technology, computer vision, and medical AI applications. Thrun's research interests focus on robotics, particularly simultaneous localization and mapping (SLAM), autonomous driving systems, and probabilistic state estimation techniques. His work extends to deep learning applications in medical imaging, notably achieving dermatologist-level skin cancer classification. He has pioneered approaches in meta-learning, vector quantization, and efficient clustering algorithms using multi-armed bandits. His recent publications demonstrate a strong trend toward improving efficiency in machine learning algorithms, particularly in areas like decision trees, vector quantization, and nearest neighbor search. Thrun has also made significant contributions to medical AI, applying deep learning to skin cancer detection and molecular property prediction. Max Planck Research Award (2011) Thrun has advised numerous PhD students who have become prominent researchers in robotics and AI, including David Stavens, Anna Petrovskaya, and Jesse Levinson. His research has been supported by significant grants, particularly for autonomous vehicle development, including Stanford's entry in the DARPA Urban Challenge (Junior). His work bridges theoretical advances with practical applications across multiple domains. Thrun has led research teams focused on autonomous driving systems, 3D reconstruction, and medical AI applications. His work on the DARPA Urban Challenge demonstrated advanced capabilities in urban autonomous navigation, while his more recent research explores the intersection of deep learning and efficiency optimization in machine learning algorithms.

Erdal Kayacanمشاهده پروفایل

استاد

• Control Systems
• Robotics
• Computer Vision
• +۳ مورد دیگر

Prof. Dr. Erdal Kayacan serves as a Professor within the Department of Electrical Engineering and Information Technology at Paderborn University's Faculty of Computer Science, Electrical Engineering and Mathematics. His academic work centers on advancing control systems and robotics, particularly through nonlinear control theory and computer vision applications for autonomous systems such as underwater vehicles and wind turbine inspection robots. Research Interests: Kayacan's primary research areas include nonlinear control, robotics, and computer vision. He specializes in developing robust control algorithms for autonomous systems, with a focus on visual SLAM and sim2real transfer. His projects address challenges in underwater navigation, wind turbine inspection, and the creation of realistic simulation frameworks for robotics. Publication Trends: Analysis of his recent publications (2023-2024) reveals a strong integration of deep learning with traditional control methods. Key contributions include advancements in trajectory-aware object masking for SLAM, nonlinear model predictive control for visual tracking, and the generation of synthetic datasets for underwater robotics. These works consistently target real-world autonomous system applications. Teaching, Mentorship, and Projects: Kayacan teaches courses including System Theory and Nonlinear Control of Autonomous Systems. He supervises project seminars in robotic control and navigation. His current projects include the development of AI-based tools for sim2real transfer in autonomous robotics, indicating active grant-funded research. Research Group: As a member of the Automatic Control (RAT) group, Kayacan contributes to Paderborn University's research in control engineering. Current projects focus on AI-based sim2real transfer for autonomous robots.

Thorben Schoepeمشاهده پروفایل

پژوهشگر

• AI-driven robotics
• sensing
• event-cameras
• +۳ مورد دیگر

Thorben Schoepe is a Postdoctoral Researcher at the Peter Grünberg Institut (PGI) , specifically within the Neuromorphic Software Ecosystems (PGI-15) department at Forschungszentrum Jülich GmbH. His research focuses on developing robust, low-power, and low-latency event-driven robotic systems for applications in SLAM (Simultaneous Localization and Mapping), object detection, and navigation. Education: B.Sc. in Biomimetics, Westphalian University M.Sc. in Biomechatronics, Bielefeld University PhD in Neuromorphic Sensorimotor Systems, University of Groningen Research Interests: AI-driven robotics Sensing technologies Event-based cameras Hardware-software co-design Neuromorphic computing Low-latency systems

Thomas Waltherمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Spatial Navigation
• Memory and Learning
• +۵ مورد دیگر

Dr. Thomas Walther is a postdoctoral researcher at the Institute of Neuroinformatics (INI), Faculty of Computer Science, Ruhr University Bochum. His work centers on computational neuroscience with a focus on modeling cognitive processes in mammals, particularly spatial navigation, learning, and memory extinction through computational approaches. His educational background includes: Diploma in Electrical Engineering and Information Technology from the University of Dortmund, with thesis research on medical image interpretation, tumor tracking, and robot-assisted radiotherapy Ph.D. in Electrical Engineering and Information Technology from Ruhr University Bochum, dissertation on autonomous visual model learning based on Organic Computing principles Dr. Walther's research spans computational neuroscience, virtual reality systems, robotics, computer vision, computational auditory scene analysis, and Organic Computing. He investigates hippocampal circuit mechanisms underlying spatial cognition and memory processes, employing deep reinforcement learning and computational modeling to bridge neural mechanisms with behavioral outcomes. His work integrates biological plausibility with artificial intelligence techniques to unravel complex cognitive functions. His publication record shows consistent focus on applying reinforcement learning to neuroscience questions, particularly spatial navigation and extinction learning phenomena, while maintaining strong contributions to computer vision (human body modeling) and robotics (RatSLAM optimization). This interdisciplinary trajectory demonstrates evolving integration between machine learning methodologies and neurobiological inquiry. Scientific awards: None mentioned in available information. Dr. Walther has supervised multiple Master's theses including research on deep reinforcement learning algorithms across environments, virtual reality applications, context representation in learning systems, and transfer of learned associations. His advising emphasizes computational approaches to cognitive questions within the INI's collaborative framework. As a core member of the Computational Neuroscience group at INI, he contributes to the institute's mission of understanding natural cognitive systems through experimental psychology, neurophysiology, and AI to develop novel solutions for artificial cognitive systems within interdisciplinary collaborations.

Joerg Roewekaemperمشاهده پروفایل

پژوهشگر

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Joerg Roewekaemper is a researcher in the Autonomous Intelligent Systems group at Albert-Ludwigs-Universität Freiburg's Faculty of Engineering. His work focuses on robotics, localization, and sensor calibration. Education: Dipl.-Ing. (FH) in Mechatronics Engineering (Bochum University of Applied Sciences, 2009), Master o.Sc. in Systems and Control (Coventry University, 2009) Research interests include path planning for mobile manipulators, localization of mobile robots, and sensor calibration. His publications cover topics like multiple-body registration, RRT planning in high-dimensional spaces, and scan matching techniques for SLAM. Recent work analyzes trends in improving robot localization accuracy through probabilistic methods and sensor fusion, particularly for autonomous navigation systems operating in dynamic environments. Teaching: Teaching Assistant for "Einführung in die Informatik" (Introduction to Computer Science) at University of Freiburg (WS2011/12, SS2012, WS2012/13)

Luciano Spinelloمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Robotics
• SLAM (Simultaneous Localization and Mapping)
• +۳ مورد دیگر

Luciano Spinello is a Research Fellow affiliated with the University of Freiburg's Department of Computer Science, working within the AIS Lab led by Prof. W. Burgard. Previously, he held roles at Amazon Research (Seattle), ETH Zurich (PhD under Prof. Roland Siegwart), and EPFL Lausanne as a research assistant. His research focuses on the intersection of computer vision and robotics, specializing in robot perception, SLAM, and autonomous systems. He has contributed to projects involving RGB-D data processing, terrain classification, and socially-aware navigation algorithms. Education: PhD in Computer Science from ETH Zurich (2009), Electrical Engineering degree from Rome, Italy. Academic activities include organizing workshops (RSS 2014, IROS 2012), serving on program committees for robotics conferences, and editorial roles (IROS associate editor). His work emphasizes multimodal sensing, object detection in 3D environments, and robust localization across dynamic conditions. Key technical contributions include methods for RGB-D fusion, adaptive domain adaptation, and large-scale place recognition. His research bridges theoretical advancements with practical applications in autonomous robotics, including navigation systems and human-robot interaction protocols.

• 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.

• Computer Vision
• Control Theory
• Biomedical Image Analysis
• +۵ مورد دیگر

Zhen Tang is an active researcher with a prolific publication record spanning from 2007 to 2025, primarily in computer science and engineering disciplines. Their work appears consistently in high-impact venues including IEEE Access, IEEE Transactions, and major conferences in computer vision and systems engineering. Research interests span computer vision, control theory, biomedical image analysis, machine learning, and multi-agent systems. Tang's work demonstrates a strong interdisciplinary approach, connecting theoretical control systems with practical applications in medical imaging, distributed computing, and emerging technologies like DNA computing. Recent publications show a growing interest in large language models, blockchain applications, and ethical considerations in technology design. The publication trends reveal an evolution from foundational work in image processing and pattern recognition (2010-2015) to more complex systems involving multi-agent control and deep learning (2016-2020), and most recently expanding into large language models, blockchain healthcare applications, and technology ethics. The research shows strong connections between theoretical control systems and practical applications across multiple domains. Zhen Tang has established long-term collaborations with researchers including Yanli Wan, Zhenjiang Miao, Wei Wang, and others, suggesting stable research group affiliations. The consistent publication output across 18 years indicates an established academic career with significant contributions to multiple subfields within computer science and engineering.

• Wireless Sensor Networks
• Mobile Computing
• Crowdsourcing Systems
• +۵ مورد دیگر

Qi Han is a Professor in the Department of Computer Science at Colorado School of Mines, with a distinguished research career spanning over two decades in wireless sensor networks, mobile computing, and distributed systems. Their work bridges theoretical foundations with practical applications in augmented reality, robotics, and smart city infrastructure. Research interests focus on three interconnected domains: 1) Networked robotic systems including UAV-UGV collaboration and swarm intelligence, 2) Mobile augmented reality with emphasis on real-time performance and spatial accuracy, and 3) Crowdsourced data systems for smart city applications. Recent work demonstrates sophisticated integration of these areas, particularly in energy-aware path planning, communication-resilient robot teams, and quality-aware AR systems. Analysis of recent publications (2022-2025) reveals a strategic shift toward integrating large language models with physical systems, developing more robust communication frameworks for drone networks, and creating comprehensive evaluation metrics for AR applications. The research trajectory shows increasingly complex system integration, moving from single-device solutions to coordinated multi-agent systems with sophisticated networking requirements. Principal Investigator for multiple NSF-funded projects on networked robotic systems Recipient of best paper awards at MobiQuitous and DCOSS conferences Senior member of IEEE Computer Society Organizing committee member for ACM MobiSys and IEEE PerCom conferences As a research advisor, Qi Han has mentored numerous graduate students who have gone on to publish in top-tier venues and secure positions in both academia and industry. Their lab maintains strong industry partnerships with companies developing AR/VR technologies and drone systems. Current research directions include energy-aware coordination of heterogeneous robot teams, privacy-preserving crowdsensing frameworks, and adaptive AR systems for industrial applications.

Georg von Wichertمشاهده پروفایل

پژوهشگر

• Cognitive Systems
• Computer Vision
• Robotics
• +۳ مورد دیگر

Georg von Wichert is a Rudolf Diesel Industry Fellow at the Technical University of Munich (TUM) since 2009, affiliated with Siemens Corporate Technology. He holds a Diploma (M.Sc.) in electrical and control engineering from Darmstadt University of Technology (1992) and a Ph.D. in electrical engineering (1998). His research focuses on cognitive systems, including computer vision, robotics, and sensor fusion, with applications in autonomous systems and human-robot interaction. Affiliations: Siemens AG (Corporate Technology) & TUM Institute for Advanced Study (Focus Group: Cognitive Technology) Education: Darmstadt University of Technology (Diploma 1992, Ph.D. 1998) His work spans probabilistic modeling for multi-agent systems, semantic scene understanding, and robotic navigation. Notable contributions include table-top scene analysis, semantic indoor mapping, and robotic systems for everyday environments. He has led national and European research projects and contributed to initiatives like the Robotic Bar demonstration of human-robot collaboration. Scientific Awards: Rudolf Diesel Industry Fellowship (2009). Research Highlights: Developed algorithms for 6-DOF pose estimation, robotic exploration in dynamic environments, and context-aware robotic assistants. His publications address challenges in simultaneous segmentation, object recognition, and 3D localization using stereo vision.

Lukas Luftمشاهده پروفایل

پژوهشگر

• Robot Localization and Mapping
• Causality and Foundations of Quantum Physics
• Probabilistic Methods
• +۳ مورد دیگر

Lukas Luft is a postdoctoral researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. His work spans robotics and quantum physics, focusing on probabilistic methods for robot localization, multi-robot systems, and causal inference. Post Doc (2020–present) PhD in Computer Science, University of Freiburg (2020) Master and Bachelor in Physics, RWTH Aachen and University of Freiburg His research in Robot Localization and Mapping includes advanced probabilistic techniques like Bayes filters, decentralized algorithms for multi-robot systems, and change detection in environments using full posterior distributions. He also explores Causality and Foundations of Quantum Physics , applying entropic inequalities and information theory to causal discovery and non-locality. The articles highlight his contributions to robotics, particularly in sensor modeling for Lidar, simultaneous localization and mapping (SLAM), and efficient probabilistic methods. In quantum physics, his work addresses causal structures and entropic information, bridging AI with foundational physics. Luft has collaborated with leading researchers, including Prof. Wolfram Burgard and Bernhard Schölkopf, and contributed to key conferences like Robotics: Science and Systems (RSS) and IEEE IROS.

• Robotics
• Artificial Intelligence
• Computational Neuroscience
• +۷ مورد دیگر

Robin Dietrich is a Researcher at the Department of Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems at the Technical University of Munich . His work bridges computational neuroscience and robotics, focusing on translating neural mechanisms from mammalian brains into algorithms for mobile robot navigation. B.Sc. and M.Sc. in Computer Science Research on hippocampal temporal dynamics for neuromorphic SLAM Specializes in spiking neural networks for navigation and radar processing Research Interests : Robin's research explores the intersection of robotics, artificial intelligence, and computational neuroscience . His work specifically investigates spiking neural networks, FMCW radar data processing, and neuromorphic algorithms for autonomous systems. Recent projects focus on uncertainty quantification, evolutionary optimization, and multi-robot exploration using biologically inspired models. Publication Trends : Robin's publications (2019-2025) demonstrate a consistent focus on neuromorphic computing for robotic perception , with increasing specialization in spiking neural networks for radar processing and biologically inspired navigation algorithms . Key collaborations include contributions to multi-robot exploration metrics and hardware acceleration frameworks. Teaching Contributions : Robin has taught Digital Signal Processing and Real-Time Systems lectures since 2019, co-led seminars on Bio-inspired Data Processing , and supervised practical courses on Intelligent Mobile Robots using ROS.

Andreas Wenzelمشاهده پروفایل

استاد

• Embedded Systems
• Diagnostic Systems
• Neural Networks
• +۶ مورد دیگر

Prof. Dr.-Ing. Andreas Wenzel is a Professor at Schmalkalden University of Applied Sciences in the Faculty of Electrical Engineering, specializing in Embedded Systems. His office is located in Building M, Room 0401, and he can be reached at +49 3683 688 5113. With a distinguished research career spanning over two decades, Prof. Wenzel has established himself as a leading expert in embedded diagnostic systems with applications across multiple domains including biomedical engineering, industrial automation, and assistive technologies. Prof. Wenzel's primary research interests focus on the development and application of embedded diagnostic systems, with particular emphasis on neural networks for pattern recognition, fuzzy logic systems for classification problems, and real-time monitoring solutions. His work bridges theoretical machine learning approaches with practical engineering applications, resulting in innovative solutions for quality control in manufacturing processes, medical diagnostics, and mobility assistance technologies. He has made significant contributions to EEG data analysis for sleep stage and anesthesia depth monitoring, as well as developing smart systems for injection molding quality assessment and advanced mobility solutions for elderly individuals. Analysis of Prof. Wenzel's publication record reveals a consistent research trajectory focused on applying computational intelligence to solve practical engineering problems. His work demonstrates exceptional versatility across domains while maintaining a cohesive research theme centered on embedded diagnostic systems. From 2012-2016, his research output shows increasing focus on real-time embedded solutions with industrial applications, particularly in manufacturing quality control and assistive technologies, while continuing his foundational work in biomedical signal processing. The interdisciplinary nature of his research is evident in collaborations with medical professionals, industrial partners, and robotics specialists. Prof. Wenzel leads the Embedded Diagnostic Systems Research Group at Schmalkalden University of Applied Sciences, which maintains strong industry partnerships, particularly in plastics manufacturing and medical technology sectors. His team develops practical embedded solutions that address real-world challenges in production quality monitoring, medical diagnostics, and mobility assistance. The research group's work is characterized by its practical orientation, with many projects resulting in deployable systems rather than purely theoretical contributions. Prof. Wenzel's strategic focus on applied research ensures that his work has direct industrial relevance and practical impact.

Reiner Marchthalerمشاهده پروفایل

استاد

• Embedded Systems
• Sensor Data Fusion
• Kalman Filters
• +۲ مورد دیگر

Prof. Reiner Marchthaler is a Professor at Esslingen University of Applied Sciences within the Faculty of Computer Science and Information Technology. He serves as Deputy Director of the Institute for Intelligent Systems (IIS), Scientific Director of the Green IT 2026 Conference, and Liaison Lecturer for the Friedrich Ebert Foundation. His academic leadership spans autonomous systems research and educational initiatives in embedded technologies. His research centers on Embedded Systems and Sensor Data Fusion, with pioneering work on Kalman filters for autonomous systems. He maintains the authoritative resource kalman-filter.de and has developed real-time capable SLAM algorithms, camera-based reference systems, and parking space detection frameworks. His expertise extends to entropy-based safety evaluation in autonomous driving and semantic segmentation using mixed real/synthetic data. Analysis of his 2020-2025 publications reveals dominant trends in autonomous driving systems, emphasizing real-time sensor fusion, deep learning for perception, and safety validation. Key subfields include adaptive Kalman filtering (ROSE-Filter), landmark-based navigation, neural network training with synthetic data, and maximum entropy safety frameworks. His work bridges theoretical innovation with automotive applications, particularly in model vehicle testing environments. Prof. Marchthaler leads research at the Institute for Intelligent Systems, directing the Green IT 2026 initiative and advising the Friedrich Ebert Foundation. His team develops ROS-based validation environments for autonomous algorithms and maintains the Kalman filter knowledge portal. Current projects focus on connected traffic systems using conventional infrastructure landmarks and entropy-optimized safety protocols for production vehicles.