جستجوی استاد راهنمای دانشگاهی

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.

• Computational Neuroscience
• Spatial Navigation
• Memory and Learning
• +۵ 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)

• Robotics
• Artificial Intelligence
• Machine Learning
• +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
• SLAM (Simultaneous Localization and Mapping)
• +۳ مورد دیگر

Prof. Andreas Birk is a Professor of Electrical Engineering & Computer Science at Constructor University Bremen gGmbH, leading the Robotics Research Group. His work bridges basic research in artificial intelligence and applied robotics, with a focus on underwater systems, disaster response, and autonomous navigation. He holds a PhD from Universität des Saarlandes and has held visiting professorships at Vrije Universiteit Brussel and other institutions. Education: Dr. rer. nat. (1995) – Universität des Saarlandes, Saarbrücken MSc (Diplom) – Computer Science (1993) BSc (Vordiplom) – Computer Science (1991) Research Interests: Birk’s dual focus spans theoretical intelligence modeling and engineering applications like underwater robotics, SLAM (Simultaneous Localization and Mapping), and human-robot interaction. His projects include underwater cultural heritage mapping, autonomous container unloading, and deep-sea exploration. Key Projects: CADDY: Cognitive Autonomous Diving Buddy (EU-funded HRI research) MORPH: Multimodal Underwater Surveys with UUVs FloodEvac: Robotic assessment of flood risks in infrastructure Awards: Recognized for innovation in robotics competitions (e.g., RoboCup, ICRA), and leadership in IEEE TC Safety, Security, and Rescue Robotics. Received the Ernst-Lange-Prize and Faulhaber Award. Teaching & Outreach: Pioneered online robotics education during the pandemic, emphasizing hands-on labs. Co-founded Constructor’s Robotics program and mentors through Entrepreneurship and Innovation initiatives. Labs & Teams: Leads the Robotics Research Group, collaborating with teams on underwater vision, simulation-in-the-loop validation, and multi-robot systems.

• Robotics
• Artificial Intelligence
• Underwater Robotics
• +۵ 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
• Robotics
• 3D Reconstruction
• +۳ 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.

• Computer Vision
• Control Theory
• Biomedical Image Analysis
• +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.

• Wireless Sensor Networks
• Mobile Computing
• Crowdsourcing Systems
• +۵ 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.

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

Niclas Vödisch is a Ph.D. student and researcher at the Autonomous Intelligent Systems Lab in the Department of Computer Science at the University of Freiburg. Supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada, he is actively contributing to the field of robotics and AI as a member of the ELLIS Society. His research focuses on enhancing machine perception and SLAM systems using deep learning methods, with primary applications in mobile robotics and autonomous driving. Education: Ph.D. Student at University of Freiburg (June 2021 - June 2025, thesis submitted, defense pending) Visiting Ph.D. Student at University of Zurich (June 2024 - December 2024) M.Sc. in Computational Science and Engineering at ETH Zurich (September 2018 - May 2021) Visiting Undergraduate Student at Carnegie Mellon University (August 2016 - May 2017) B.Sc. in Computational Engineering Science at RWTH Aachen University (September 2014 - June 2018) Vödisch's research interests center around Continual Learning for Robotics, Machine Perception, and Simultaneous Localization and Mapping (SLAM). His work addresses the critical challenge of reducing dependency on extensive annotated training data in robotic perception systems. Through innovative approaches leveraging foundation models, he has developed methods that achieve high performance with minimal supervision, making robotic systems more adaptable to real-world environments. His research spans theoretical advances in deep learning and their practical implementation in autonomous systems. An analysis of his publication record reveals a clear progression from foundational SLAM techniques to sophisticated continual learning frameworks and foundation model applications. A unifying theme across his work is data efficiency in robotic perception, with increasing emphasis on collaborative multi-agent systems and cross-modal information integration for robust performance in challenging conditions. His recent publications demonstrate growing influence in the robotics community, evidenced by the IROS 2024 Best Paper Award for his BEVCar work. Scientific Awards: IROS 2024 Best Paper Award on Cognitive Robotics (BEVCar) IROS 2024 Best Student Paper Award Finalist (BEVCar) Dean's List at Carnegie Mellon University (fall 2016) DAAD full scholarship for CMU studies (2016-2017) Niclas has mentored numerous Master's students on projects spanning LiDAR panoptic segmentation, collaborative scene graph generation, and autonomous driving systems. His teaching portfolio includes co-organizing seminars on Robot Learning and Learning with Limited Supervision, as well as leading the FreiCAR practical autonomous driving course across multiple semesters. His research has received substantial funding from the German Research Foundation (DFG) Emmy Noether Program, NVIDIA academic grants, and Qualcomm Technologies Inc., reflecting the significance and potential impact of his work. As a key contributor to the Autonomous Intelligent Systems group at Freiburg, Vödisch collaborates closely with the Robot Learning group and has extended his research network through his visiting position at the University of Zurich's Robotics and Perception Group. His interdisciplinary approach bridges computer vision, robotics, and machine learning, positioning him at the forefront of research in AI-powered autonomous systems with practical real-world applications.

• Continual Learning for Robotics
• Machine Perception
• Simultaneous Localization and Mapping (SLAM)
• +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.

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

Andreas Lars Wachaja is a Doctoral Research Assistant at the Autonomous Intelligent Systems group within the Department of Computer Science at Albert-Ludwigs-Universität Freiburg, Technical Faculty, Germany. He has been actively contributing to research since 2013 under the supervision of Prof. Dr. Wolfram Burgard. His office is located at Georges-Köhler-Allee 080, Room 00-023, Freiburg i. Br. Education: Dipl.-Ing. in Mechanical Engineering, Karlsruhe Institute of Technology (2007–2013) Current Affiliation: Department of Computer Science, Albert-Ludwigs-Universität Freiburg Research Group: Autonomous Intelligent Systems Co-Founder: dotscene GmbH (2016) Andreas Lars Wachaja’s research interests lie at the intersection of robotics and assistive technology. He focuses on developing robotic systems for ambient assisted living , particularly for individuals with visual and mobility impairments. His work includes precise 3D SLAM using LiDAR , machine learning for sensor data analysis , and the design of smart walkers equipped with vibrotactile feedback systems. His research emphasizes real-world applications, usability, and human-centered design in robotic navigation aids. The recent publications highlight a strong trend in assistive robotics , especially in creating navigation solutions for the visually impaired. His work integrates SLAM, haptic feedback, and shared autonomy to develop inclusive technologies. The research spans both academic publications and practical implementations, reflecting a commitment to bridging the gap between robotics and rehabilitation. Andreas has contributed to teaching in areas such as operating systems and robot navigation. While no formal scientific awards are listed, his work has been featured in notable media outlets such as ingenieur.de, Heilbronner Stimme, and idw - Informationsdienst Wissenschaft, indicating public and scientific recognition. He has not supervised any students formally listed, but has collaborated extensively with researchers like Pratik Agarwal, Wolfram Burgard, and Knut Möller. He is involved in significant research projects including ZAFH-AAL (Center for Applied Research on Ambient Assisted Living) and iVIEW (intelligent vibrotactile-induced extended perception), which focus on cognitive and physical assistance through intelligent systems. These projects reflect a multidisciplinary approach combining engineering, computer science, and healthcare applications. Andreas is associated with the Autonomous Intelligent Systems lab at the University of Freiburg, a leading group in robotics and AI research. The lab specializes in mobile robotics, perception, and human-robot interaction, providing a strong foundation for his work in assistive technologies.

• Robotic Technologies for Ambient Assisted Living
• Simultaneous Localization and Mapping (SLAM)
• Machine Learning for Sensor Data Analysis
• +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.

• Robotics
• Artificial Intelligence
• Computational Neuroscience
• +۷ 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.

• Embedded Systems
• Diagnostic Systems
• Neural Networks
• +۶ 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.

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