Find Academic Supervisors

• Artificial Intelligence
• Data Science
• Computer Vision
• +6 more

Niklas Beuter is a Professor of Artificial Intelligence and Data Science at TH Lübeck, Germany, within the Department of Electrical Engineering and Computer Science. He has been in this position since 2023, contributing to both research and education in AI and computer vision. His educational background includes a Diplom in Computer Science with a focus on robotics from Universität Bielefeld, completed with distinction, followed by a Ph.D. from the same institution in 2011. His doctoral research centered on 3D human detection and tracking for mobile robotic platforms, supporting situation awareness in dynamic environments. Beuter's research is deeply rooted in artificial intelligence, computer vision, and robotics , with a focus on dynamic 3D scene analysis, human-robot interaction, and autonomous systems . His work bridges theoretical models with real-world applications, particularly in autonomous driving and intelligent robotic perception. He has led research teams in industry, demonstrating strong leadership in applied AI. The trend in his publications reflects a consistent focus on perception systems for robots and vehicles , evolving from foundational work in 3D reconstruction and gesture-based interaction to advanced topics like pedestrian intent forecasting using deep learning. His contributions span top conferences such as IEEE ICRA, CVPR, and Intelligent Vehicles, indicating sustained engagement with the core AI and robotics communities. While no formal scientific awards are listed in the provided text, his leadership roles and publication record reflect significant professional recognition. He actively supervises student theses and invites students to register for thesis topics via email, indicating an ongoing commitment to mentoring. Although no specific grants are mentioned, his industrial and academic research leadership suggests involvement in funded projects. He is a member of research groups including CoSA and serves as Deputy Head of ISy, showing active participation in institutional research organization. His research has been conducted in collaboration with teams at Universität Bielefeld, Daimler Research Center, and Robert Bosch GmbH, reflecting a strong interdisciplinary and industry-academia network. His work continues to influence both academic research and industrial applications in AI and autonomous systems.

Aljaž BožičView profile

Researcher

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +7 more

Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.

Andreas NüchterView profile

Professor

• Robotics
• Computer Vision
• Sensor Fusion
• +5 more

Andreas Nüchter is a Professor and Chair of Robotics at Julius-Maximilians-University Würzburg (Germany), leading the Chair of Computer Science XVII. He holds additional roles including Visiting Chair at ENSTA - Institut Polytechnique de Paris, Dean of Studies at the Institute of Computer Science, and Vice President of Zentrum für Telematik e.V. His research focuses on robotics, 3D reconstruction, SLAM, and sensor systems for autonomous systems. He has authored influential papers in IEEE Transactions on Robotics, Acta Astronautica, and robotics conferences like ICRA and IROS. Education: Not explicitly detailed in text, but inferred through academic roles. Research Interests: Robotics, computer vision, sensor fusion, autonomous systems, 3D reconstruction, and space robotics applications. His work emphasizes real-time systems, planetary exploration, and open-source software frameworks like SceneFactory and libBICOS. Recent projects include CNN-based spacecraft pose estimation, spherical mobile mapping systems, and radar SLAM (RIV-SLAM). Advising & Grants: Mentors a team including MSc/BSc students (e.g., Luca Anteunis, Fabian Arzberger). Involved in grants related to robotics, mining automation (AMADEE-24), and space exploration. Collaborates with institutions like NASA (CADRE lunar rovers) and Fraunhofer IPM. Labs/Teams: Leads the Robotics Lab at JMU, develops spherical robots for planetary missions, and maintains open-source libraries (libBICOS, SceneFactory). Active in projects like AMADEE-24 Mars simulation for human-robot interaction.

Claus BrennerView profile

Adjunct Professor

• LiDAR Mapping
• Point Cloud Processing
• Robust Estimation
• +3 more

apl. Prof. Dr.-Ing. Claus Brenner is an Adjunct Professor at the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on LiDAR mapping, point cloud processing, and robust estimation, with applications in autonomous systems, urban mapping, and disaster risk assessment. He leads the Graduiertenkolleg 2159 research group on integrity and collaboration in dynamic sensor networks. Key research areas include 3D reconstruction, SLAM (Simultaneous Localization and Mapping), semantic segmentation of mobile mapping data, and cooperative perception systems. His work integrates advanced machine learning techniques with geospatial data analysis, addressing challenges in sensor fusion, uncertainty modeling, and real-time localization. Recent publications span topics like voxel-based point cloud localization for smart spaces, flood risk mapping using LiDAR, and adversarial shape completion. Brenner has contributed to benchmark datasets such as LuCoop and LUMPI, advancing research in cooperative perception and urban navigation. His methods emphasize robustness and scalability, often leveraging generative models and statistical frameworks for urban environment analysis. Notable projects include the development of high-definition mapping using LiDAR, trajectory-based road network reconstruction, and semantic annotation from user trajectories. His work bridges theoretical advancements in computer vision with practical applications in autonomous systems and smart infrastructure.

Tristan ThielmannView profile

Professor

• Media Studies
• Science and Technology Studies (STS)
• Media Geographies
• +13 more

Tristan Thielmann is Professor of Science, Technology and Media Studies at the University of Siegen, Faculty I. He serves as Deputy Spokesperson of the DFG Collaborative Research Centre 1187 ‘Media of Cooperation’ and leads subprojects A03 and P04 on navigation in online/offline spaces and scientific media practices. He is based in Siegen, Germany, and is actively engaged in research, teaching, and academic leadership. PhD in Communication Studies Studies in Media Studies, European Media and Cultural Studies, Experimental Media Design Tristan Thielmann’s research centers on media geographies, navigation studies, cultural cartographies, and the historical and praxeological analysis of media technologies. His work integrates science and technology studies (STS), ethnomethodology, and cultural theory to examine how media shape cooperation, perception, and spatial understanding. He investigates topics such as geomedia, sensor media, digital displays, and mobile practices, often through historical case studies involving radar, early computing, and transportation media like the bicycle. His recent publications reveal a strong focus on the material and procedural dimensions of media, emphasizing practices over representations. He analyzes how digital technologies like SLAM, ENIAC displays, and sensor networks reconfigure human-environment relations and cooperative knowledge production. His work bridges media theory, history, and geography, contributing to a deeper understanding of digital society’s infrastructures. Associate Fellow of the Royal Institute of Navigation (2018) Visiting Fellow, Software Studies Initiative, UC San Diego (2008) Visiting Fellow, Comparative Media Studies, MIT (2011/12) Visiting Fellow, Digital Ethnography Research Centre, RMIT (2015) Thielmann has supervised and co-directed the DFG Research Training Group ‘Locating Media’ since 2016. He has received funding from the German Research Foundation (DFG) for multiple projects, including long-term research on cooperative media practices. His editorial work includes co-editing volumes on collaboration, display theory, and actor-media theory, supporting scholarly discourse in media studies. He has not publicly listed any advisees, but his leadership roles indicate mentorship and academic guidance. He is a key figure in the ‘Media of Cooperation’ research environment, contributing to interdisciplinary collaboration across media studies, geography, and science and technology studies. His work with the SFB 1187 fosters innovative research on the material conditions of cooperation in digital and historical contexts.

Sven LangeView profile

Researcher

• Sensor Fusion
• Simultaneous Localization and Mapping (SLAM)
• Mobile Robotics
• +5 more

Dr.-Ing. Sven Lange is a post-doctoral researcher at Technische Universität Chemnitz (TU Chemnitz), affiliated with the Chair of Automation Technology and the Chair of Neurorobotics. His work spans research, education, and project management in robotics. Research interests include: Factor-graph optimization for SLAM and sensor fusion Nonlinear least squares methods in robotics Robust navigation for autonomous systems GNSS signal processing in urban environments Educational robot design for undergraduate courses Integration of multi-modal error models in state estimation Key publication trends focus on factor graphs, outlier mitigation, and sensor fusion techniques for UAVs, GNSS, and indoor navigation. His work emphasizes parameter-free algorithms and open-source implementations. Projects include DLR SpaceBot Cup robotics, smartLoc GNSS navigation, and TUC-Bot educational platforms. He has contributed to sensor fusion frameworks for Ceres and GTSAM solvers.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Dr. Chenyang Zhao is an Assistant Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. His research spans multiple domains at the intersection of computer vision, machine learning, and engineering applications. Dr. Zhao's primary research interests include: Computer Vision and Deep Learning Robotics and Autonomous Systems Medical Imaging and Healthcare Applications Hardware Acceleration for AI Precision Measurement and Instrumentation His recent work demonstrates a strong focus on practical applications of AI across diverse domains, from medical imaging to infrastructure inspection. Dr. Zhao has developed innovative approaches in areas such as sewer inspection systems using evidential deep learning, RGB-D semantic SLAM for robotics, and lightweight computing-in-memory architectures for edge AI applications. His research shows a consistent pattern of addressing real-world engineering challenges with cutting-edge machine learning techniques, particularly emphasizing trustworthiness, uncertainty quantification, and practical implementation constraints. Dr. Zhao's laboratory focuses on developing trustworthy AI systems with applications in: Infrastructure monitoring and maintenance Medical diagnostics and imaging Autonomous robotic systems Energy-efficient AI hardware

Wolfram BurgardView profile

Professor

• Artificial Intelligence
• Robotics
• Mobile Robotics
• +3 more

Prof. Wolfram Burgard is a Professor for Robotics and Artificial Intelligence and Founding Chair of the Engineering Department at the University of Technology Nuremberg. Previously, he was a professor of computer science at the University of Freiburg (1999–2021), where he established the renowned Autonomous Intelligent Systems research lab. His research focuses on probabilistic techniques for robot navigation, state estimation, and control, with notable contributions to SLAM, path-planning, and mobile robot applications such as museum tour-guides (Rhino/Minerva) and autonomous vehicles. His research interests span artificial intelligence, mobile robotics, and autonomous systems. Key projects include deploying interactive museum robots, developing autonomous parking systems (2008), and the pedestrian-like Obelix robot (2012). He has authored over 350 papers, co-authored seminal books like Probabilistic Robotics , and holds the prestigious Gottfried Wilhelm Leibniz Prize (2009) and ERC Advanced Grant (2010). He is an ECCAI and AAAI Fellow, and member of the German Academy of Sciences Leopoldina and Heidelberg Academy of Sciences. Prof. Burgard coordinated the BrainLinks-BrainTools Cluster of Excellence (2012–2019), funded by the German Research Foundation. His work emphasizes real-world applications, including industrial plant supervision and autonomous driving systems. His Erdős number is ≤4, and his publications are indexed on Google Scholar.

Peer NeubertView profile

Professor

• Robotics
• Computer Vision
• Artificial Intelligence
• +3 more

Prof. Dr. Peer Neubert is a Professor and Head of the Intelligent Autonomous Systems (INTAS) Working Group at the University of Koblenz. He is affiliated with the Department of Computer Visualistics (Department 4) and serves as Head of the Institute of Computational Visualistics. His academic roles include membership in the Department Council and Library Committee of FB4. His research focuses on robotics, computer vision, and autonomous systems, with particular emphasis on visual place recognition, SLAM, hyperdimensional computing, and adaptive navigation in dynamic environments. Neubert's work spans theoretical advancements and practical implementations, such as benchmarking systems (e.g., HS3-Bench), efficient algorithms (FETCH, LocalSPED), and sensor-based solutions (HealthWalk). His team addresses challenges in long-term navigation, environmental adaptation, and data-efficient learning. Collaborations include external PhD students like Kenny Schlegel and projects like MineSweeper, demonstrating interdisciplinary impact. His contributions are reflected in over 50 publications since 2007, covering topics from hyperspectral segmentation to biomedical applications. While no formal educational details are provided, his extensive research output and leadership roles underscore his expertise in computational visualistics and autonomous systems.

• Artificial Intelligence
• 3D Scene Reconstruction
• Simultaneous Localization and Mapping (SLAM)
• +3 more

Yiming Zhou is a researcher at Saarland University of Applied Sciences (htw saar) in Saarbrücken, Germany. Their work spans multiple engineering domains with emphasis on artificial intelligence integration, 3D scene reconstruction, and advanced imaging techniques. Contact: yiming.zhou@htwsaar.de Location: Goebenstraße 40, 66117 Saarbrücken Research Focus Zhou's research explores cutting-edge technologies in: AI applications for non-destructive evaluation Dynamic SLAM systems for robotics Next-generation 3D reconstruction methods Multimodal deception detection frameworks Novel Gaussian splatting techniques Semantic encoding for spatial data Recent Publications Trends Their scholarly output reveals a trajectory toward Real-time spatial mapping solutions Hybrid neural-implicit representations CAD-integrated building documentation AI-enhanced image translation pipelines Signal processing innovations Cross-modal data fusion Laboratory Affiliation Zhou contributes to the faculty laboratories at htw saar, focusing on engineering research through practical implementations and algorithm development.

• Control Systems
• Robotics
• Unmanned Aerial Vehicles
• +3 more

Yuan Yuxia is a Researcher at the Chair of Autonomous Aerial Systems (TULRAAS) within the Technical University of Munich (TUM), actively contributing to both research and teaching missions at the Ottobrunn campus (Lise-Meitner-Str. 9). Her institutional affiliation places her within a leading European research group focused on advanced aerial robotics and control systems under Prof. Ryll's leadership. Her research spans Control Systems , Robotics , Unmanned Aerial Vehicles , Human-Robot Interaction , Brain-Computer Interfaces , and Rehabilitation Robotics , with dual specializations in UAV payload control and wearable assistive devices. She has pioneered dual quaternion methodologies for UAVs with cable-suspended loads to address dynamic instability, while simultaneously developing brain/muscle signal-driven exoskeletons that restore stair-climbing capability for mobility-impaired users. Her technical approach integrates nonlinear control theory, fuzzy neural networks, and reinforcement learning to solve real-world robotics challenges. Analysis of her 15 most recent publications (2018-2025) reveals two dominant research trajectories: (1) Advanced UAV control systems focusing on cable-suspended load manipulation through geometric approaches like dual quaternions, and (2) Rehabilitation robotics emphasizing human-in-the-loop control via physiological signals. These streams converge in her work on brain-controlled mobile robots and adaptive exoskeletons, demonstrating exceptional interdisciplinary synthesis across aerospace engineering, biomedical applications, and machine learning. Her publications consistently target high-impact control challenges with immediate practical relevance. Scientific Awards: No major scientific awards are documented in the provided sources. Advising and Grants: While specific student names and grant details aren't disclosed, her extensive publication record across robotics and rehabilitation domains indicates active supervision of graduate researchers and successful acquisition of competitive research funding. Her collaborations with medical and engineering teams suggest participation in multi-institutional projects addressing both industrial UAV applications and healthcare robotics. Laboratory and Team Affiliations: Yuan operates within TULRAAS's state-of-the-art Ottobrunn facilities specializing in UAV testing and simulation. The group maintains strong industry partnerships for technology transfer, particularly in aerial manipulation and medical robotics. Her position within this internationally recognized chair enables cutting-edge research on autonomous systems with direct pathways to real-world deployment in logistics, search-and-rescue, and clinical rehabilitation settings.

Tayyab NaseerView profile

Researcher

• Robust Visual Perception
• Outdoor Robotics
• Deep Neural Networks
• +3 more

Tayyab Naseer is affiliated with the Albert-Ludwigs-Universität Freiburg, specifically within the Technische Fakultät (Faculty of Engineering) and the Autonomous Intelligent Systems Department. His research focuses on robust visual perception for outdoor robots, deep neural networks, and machine learning. He holds a BSc in Electrical Engineering from the University of Engineering and Technology Lahore (2005–2009) and an MSc in Communication Systems Engineering from the Technical University of Munich (2010–2012). He completed his PhD at the Autonomous Intelligent Systems group under Prof. Dr. Wolfram Burgard at Freiburg. His work emphasizes long-term autonomy in robotics, addressing challenges like seasonal changes in visual localization. Notable achievements include the Best PhD-Student Award at ICVSS 2016 . He has contributed to projects like LifeNav and published extensively in robotics conferences (e.g., ICRA, IROS) and journals like IEEE Transactions on Robotics. His research often intersects with computer vision, SLAM, and deep learning for robotic systems. Teaching roles include lab instructor for Image and Video Compression at TU Munich and teaching assistant for Introduction to Mobile Robotics at Freiburg. He has also delivered invited talks at workshops organized by LUMS and TU Kaiserslautern, focusing on field and assistive robotics.

• Mobile Robotics
• Autonomous Systems
• Simultaneous Localization and Mapping (SLAM)
• +3 more

Giorgio Grisetti is a full professor at Sapienza University of Rome, affiliated with the Department of Systems and Computer Science. He is a member of the RoCoCo lab at Sapienza since 2010 and the Autonomous Intelligent Systems Lab at Freiburg University (headed by Wolfram Burgard). His research focuses on mobile robotics, including SLAM, localization, and path planning. He holds a PhD in Computer Engineering from Sapienza (2006), advised by Daniele Nardi, and a M.Sc. in Computer Engineering from the same institution (2001). Education: PhD in Computer Engineering, Sapienza University of Rome (2006) M.Sc. in Computer Engineering, Sapienza University of Rome (2001) Research interests emphasize probabilistic methods for robotics, with a focus on SLAM using Rao-Blackwellized particle filters. His work spans theoretical and applied robotics, contributing to navigation systems for autonomous robots. Collaborations include Freiburg University’s lab, where he conducted postdoctoral research since 2006.

Darius BurschkaView profile

Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +3 more

Prof. Dr.-Ing. Darius Burschka is a Professor of Robotics, Artificial Intelligence, and Embedded Systems at Technische Universität München (TUM), specifically within the TUM School of Computation, Information and Technology. He leads the Professorship of Robotics, Artificial Intelligence and Embedded Systems and collaborates closely with the German Aerospace Center (DLR). His academic career includes postdoctoral research at Yale University (1998), associate research scientist and assistant research professor roles at Johns Hopkins University (1999-2005), and has been at TUM since 2005. His research focuses on sensor systems in robotics, human-machine interfaces, video-based navigation, and 3D reconstruction. Notable contributions include advancements in laser-based map generation, monocular navigation algorithms, and endoscopic image registration for medical applications. Prof. Burschka has received awards such as the Airtec 2010 Silver Award and best paper awards from prominent conferences like IROS and MICCAI. His work spans robotics, autonomous systems, computer vision, and medical imaging, with a strong emphasis on real-world applications in automotive, healthcare, and industrial automation. Education: Bachelor/Master in Electrical Engineering at TUM Doctorate (Dr.-Ing.) in Electrical Engineering, TUM (1998) His research interests include: Autonomous navigation and control Multi-sensor fusion and 3D reconstruction Human-robot interaction and haptic systems Robotic perception in dynamic environments Recent articles highlight advancements in graph neural networks for action segmentation, hybrid tracking systems, and latency modeling in industrial robotics, reflecting his ongoing contributions to cutting-edge robotics and AI.

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

Charles E. Thorpe is a Professor at Carnegie Mellon University, affiliated with the Robotics Department within the College of Engineering. His work focuses on robotics, autonomous systems, computer vision, and sensor-based navigation. He has contributed significantly to projects like the NavLab, an early autonomous vehicle initiative, and has pioneered research in simultaneous localization and mapping (SLAM), obstacle detection, and machine learning applications in robotics. His collaborations span across academia and industry, with notable partnerships in autonomous vehicle development and robotics education. Key Research Areas: Autonomous Vehicles, SLAM, Computer Vision, Robotics Algorithms, Human-Robot Interaction. Major Projects: NavLab, Urban Navigation Systems, Laser-Based Perception. Thorpe's publications emphasize practical robotics solutions, from theoretical algorithms to real-world implementations in urban environments. His work bridges robotics theory with applications in healthcare, transportation, and education.