Find Academic Supervisors

Stefan LeuteneggerView profile

Assistant Professor

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +7 more

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

• Robotics
• Photogrammetry
• Simultaneous Localization and Mapping (SLAM)
• +12 more

Cyrill Stachniss is a Full Professor at the University of Bonn , where he heads the Lab for Photogrammetry and Robotics and is affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence . He was previously a Visiting Professor in Engineering at the University of Oxford until 2025. His academic journey includes positions at the University of Freiburg, University of Zaragoza, and the Swiss Federal Institute of Technology. University: University of Bonn School: Faculty of Engineering Department: Department of Photogrammetry Academic Rank: Professor His research spans robotics, photogrammetry, SLAM, autonomous navigation, perception systems, agricultural robotics, and unmanned aerial vehicles . He emphasizes probabilistic techniques for mobile robots and has made significant contributions to visual and LiDAR-based localization, scene understanding, and 3D reconstruction. The recent publications highlight a strong trend toward neural implicit representations, agricultural phenotyping, radar-based perception, and active learning . His team develops robust systems for real-world deployment in dynamic and unstructured environments, particularly in precision farming and autonomous vehicles. Scientific Awards: IEEE RAS Early Career Award (2013) Microsoft Research Faculty Fellow (2010) 7th EURON Georges Giralt Award (2008) Multiple Best Paper Awards at ICRA, IROS, RSS, and RAL Faculty Teaching Award, University of Freiburg He has advised numerous students and leads the DFG Cluster of Excellence PhenoRob and Research Unit FOR 1505 Mapping on Demand . His lab has co-founded three startups, reflecting strong industry and societal impact. He also runs the educational video series 5 Minutes with Cyrill , explaining key robotics concepts.

Nikolas BraschView profile

Researcher

• 3D Computer Vision
• SLAM
• 3D Scene Understanding
• +1 more

Nikolas Brasch is a PhD candidate and researcher at the Chair of Computer Aided Medical Procedures (CAMP) within the Department of Informatics at the Technical University of Munich. He is affiliated with the research group led by Prof. Nassir Navab and maintains his office at Boltzmannstr. 3, 85748 Garching b. München, Room 5613.03.039. Brasch's research centers on 3D computer vision with emphasis on SLAM (Simultaneous Localization and Mapping) and 3D scene understanding. His work bridges computer vision with medical applications, particularly in medical augmented reality and surgical robotics. He has developed innovative approaches for improving depth estimation in challenging environments and creating robust SLAM systems that maintain accuracy in dynamic settings. His publication record shows a consistent trajectory in advancing 3D vision techniques with practical applications in medical contexts. His research demonstrates particular strength in sensor fusion techniques and semantic understanding of 3D environments. Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments (2021) Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments (2020) Semantic Monocular SLAM for Highly Dynamic Environments (2018) As an educator, Brasch serves as a teaching assistant for the Praktikum on 3D Computer Vision course at TUM. He mentors students on various thesis projects related to 3D vision, human pose estimation, and semantic reconstructions, typically collaborating with Prof. Federico Tombari. His teaching activities reflect his research expertise and commitment to training the next generation of computer vision researchers.

Guillermo GallegoView profile

Professor

• Robotics
• Computer Vision
• Event-based Vision
• +5 more

Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).

Ayush TewariView profile

Assistant Professor

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +3 more

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.

Benjamin SugerView profile

Researcher

• Robotics
• Autonomous Systems
• Traversability Analysis
• +6 more

Benjamin Suger is a researcher in the Department of Computer Science at the University of Freiburg, affiliated with the Autonomous Intelligent Systems group within the Faculty of Engineering. His work centers on robotics and intelligent systems, particularly in autonomous navigation and environment modeling. Diploma in Mathematics, University of Freiburg (2003–2011) PhD Student, Autonomous Intelligent Systems Group, University of Freiburg (2011–2017) His research interests include traversability analysis, SLAM, 3D modeling, and mobile robotics. He applies machine learning and computer vision techniques to enable robust robot navigation in complex and dynamic environments. His work often integrates sensor data from 3D lidar and visual systems to improve perception and localization. The recent publications highlight a strong focus on long-term autonomy, outdoor navigation, and memory-efficient SLAM algorithms. Key themes include handling perceptual changes, terrain adaptability, and integration of open geospatial data like OpenStreetMap. His work bridges theoretical algorithm development with real-world robotic applications. Scientific recognition includes being a finalist for the Best Conference Paper Award. This reflects the impact and quality of his contributions to the robotics community. Best Conference Paper Award Finalist Benjamin Suger has contributed significantly to research projects such as LifeNav and has served as a teaching assistant for the Introduction to Mobile Robotics course. While no direct advising of students is listed, his collaborative work with prominent researchers like Wolfram Burgard indicates active participation in a larger academic and research team. He has not received external grant mentions in the provided text. He is part of the Autonomous Intelligent Systems lab at the University of Freiburg, a leading group in robotics research, where he contributes to advancing the state of the art in autonomous navigation and environmental understanding for mobile robots.

• Computer Vision
• Robotics
• 3D Reconstruction
• +5 more

Thomas Läbe is a Researcher in the Department of Photogrammetry at the University of Bonn's Institute of Geodesy and Geoinformation. His roles include technical associate and software developer, with a focus on advancing robotics, computer vision, and agricultural automation. He holds a diploma in computer science (Dipl.-Inform. (FH)) from Cologne University of Applied Sciences (1994). His research spans image processing, bundle adjustment, visual odometry, and camera calibration, with notable contributions to SLAM (Simultaneous Localization and Mapping) for autonomous systems. Education: 1994: Dipl.-Inform. (FH) in Computer Science, Cologne University of Applied Sciences Research Interests: Focuses on integrating vision-based technologies into robotics for precision agriculture, 3D reconstruction, and robust localization in dynamic environments. His work addresses challenges in sensor calibration, panoptic segmentation, and semantic mapping for autonomous systems operating in agricultural and urban settings. Publications Trends: Recent work emphasizes agricultural robotics, including fruit completion, plant phenotyping, and robust localization under changing conditions. Key themes include LiDAR-based localization, semantic occupancy prediction, and end-to-end learning for 3D representations. Awards: 2001 Hansa Luftbild Price (Best PFG paper award, shared) Technical Contributions: Developed software frameworks for bundle adjustment, multi-camera systems, and obstacle detection. Collaborated on projects like the Phenoliner field phenotyping platform and UAV-based environmental monitoring systems. Labs/Teams: Active in the Photogrammetry professorship group, contributing to the PRBonn (Perception, Robotics & Bonn) robotics initiatives, with open-source tools like OverlapNet and PAg-NeRF for 3D perception.

Johannes SchneiderView profile

Researcher

• Bundle Adjustment
• Visual Odometry
• Multi-camera Systems
• +7 more

Johannes Schneider is a researcher at the University of Bonn, affiliated with the Institute of Geodesy and Geoinformation's Department of Photogrammetry. His role includes scientific research and academic contributions in the field of computer vision and robotics. He holds a Master's degree in Geodesy and Geoinformation from the University of Bonn (2011) and has been a PhD student since 2012, supervised by Wolfgang Förstner, focusing on visual SLAM and multi-camera systems. His research interests span bundle adjustment, visual odometry, multi-camera systems, and unmanned aerial vehicle (UAV) applications. He actively contributes to the 'Mapping on Demand' project (funded by DFG) and has developed software tools like BACS (Bundle Adjustment for Camera Systems). Teaching responsibilities include lectures on '3D Coordinate Systems' and project supervision for master students. Notable achievements include the Karl Kraus Young Scientist Award (2013). His work emphasizes real-time navigation, obstacle detection, and precise 3D reconstruction using UAVs, integrating sensors like RTK-GPS, IMUs, and fisheye cameras. Recent publications highlight advancements in dense stereo matching, SLAM algorithms, and system calibration.

Daniele CattaneoView profile

Researcher

• Autonomous Robots
• Deep Learning for Localization
• Sensor Fusion
• +3 more

Dr. Daniele Cattaneo is a Junior Research Group Leader at the Robot Learning Lab (University of Freiburg, Germany). He specializes in autonomous robotics, deep learning for perception and localization, and sensor fusion. His research focuses on embodiment-agnostic and environment-agnostic systems for robots, with applications in autonomous driving and healthcare robotics. Education: Ph.D. in Computer Science, Università degli Studi di Milano-Bicocca (2016–2020) M.Sc. and B.Sc. in Computer Science, Università degli Studi di Milano-Bicocca (2013–2016, 2010–2013) Research Interests: His work addresses challenges in LiDAR-camera calibration, SLAM (Simultaneous Localization and Mapping), unsupervised domain adaptation, and multimodal fusion for robust perception. Key projects include CMRNext (LiDAR-camera matching), Syn-Mediverse (healthcare scene understanding), and Continual SLAM (long-term autonomy). Awards & Grants: He leads funded projects like AI-Drive (next-gen autonomous driving algorithms) and iSUOR (operating room video analysis). Collaborations include work with the AIS Group and Robotic Learning Lab . Students & Labs: Supervises 12+ students in topics like LiDAR localization, HD maps, and radar-based navigation. Active in the Robot Learning Lab at Freiburg, contributing to open-source datasets and tools for robotics research.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

Professor Andreas Geiger leads the Autonomous Vision Group (AVG) at the University of Tübingen , heading the Department of Computer Science and serving as core faculty at the Tübingen AI Center . He is Principal Investigator in the ML in Science cluster of excellence and CRC Robust Vision , while coordinating the ELLIS PhD program . Develops machine learning models for computer vision, NLP, and robotics Focus on 2D/3D representations, geometry/material reconstruction, and robust AI Applications in autonomous vehicles, VR/AR, and document analysis His research has produced hundreds of publications with significant impact, including multiple best paper awards at top venues. The Scholar Inbox platform he co-created revolutionizes academic paper discovery, winning business model awards at Tübingen AI Center spinoff events. Key research areas include: Neural rendering and 3D scene understanding Self-driving perception and planning systems Simulation frameworks for autonomous validation Efficient reinforcement learning architectures Recent awards include: CVPR 2024 Best Paper Sage 10-Year Impact Award 2024 IEEE PAMI Young Researcher Award 2018 Active in CyberValley and ELLIS Institute Tübingen , he maintains strong industry collaborations through initiatives like the ML ⇌ Science Colaboratory . His group's work appears in journals like TPAMI and conferences including SIGGRAPH 2025.

• 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.

• Neuromorphic Computing
• Embodied Artificial Intelligence
• Robotics
• +6 more

Dr. Yulia Sandamirskaya is the Head of Research Center "Cognitive Computing in Life Sciences" at Zurich University of Applied Sciences (ZHAW), focusing on neuromorphic computing applications for embodied artificial intelligence. Her work bridges computational neuroscience and robotics, emphasizing neural-dynamic architectures for real-time decision-making, learning, and sensorimotor integration in autonomous agents. Key Research Areas: Neuromorphic hardware, dynamic neural fields, spiking neural networks, spatial language modeling, and autonomous sequence generation. Projects: Developed controllers for UAVs and robotic arms using event-based vision sensors, explored on-chip unsupervised learning, and designed models for spatial language interpretation in robots. Scientific Contributions: Her publications span robotics conferences and journals like Science Robotics and Frontiers in Neurorobotics , addressing topics such as path integration, obstacle avoidance, and cognitive architectures. Recent work (2024) includes visual odometry with resonator networks and hyperdimensional scene factorization on neuromorphic chips. Advising: Supervised multiple MSc theses at ETH Zurich and NSC/INI programs, mentoring students on neuromorphic navigation, spiking networks, and tactile learning. Collaborated with institutions like ETH Zurich, University of Queensland, and INI Bochum. Labs & Collaborations: Leads the "Neuromorphic Computing Applications: Embodied AI" group at ZHAW, partnering with INIvation (Zurich) and Jörg Conradt (KTH) on neuromorphic hardware implementations. Projects integrate cognitive models with robotic platforms, emphasizing energy efficiency and low-latency interaction.

Norbert HaalaView profile

Adjunct Professor

• Computer Vision
• Geoinformatics
• 3D Data Reconstruction
• +5 more

Norbert Haala is an Adjunct Professor and Deputy Director at the University of Stuttgart's Department of Photogrammetric Computer Vision. He leads the Research Group in Photogrammetric Computer Vision and is involved in ISPRS Working Group II/2 (Point Cloud Generation) and EuroSDR Commission 2 (Modelling and Processing). His work focuses on 3D data collection, SLAM algorithms, LiDAR integration, and UAV-based geospatial technologies. Research interests include image-based data collection, photogrammetric computer vision, and semantic segmentation of 3D point clouds. He has contributed to benchmarks like Hessigheim 3D and TIME, and developed tools like SURE for dense image matching. Teaching includes courses on digital image processing, terrain modeling, and computer vision. His publications emphasize real-time mapping, hybrid georeferencing, and neural surface reconstruction from airborne imagery. Projects involve UAV monitoring, indoor SLAM for robotics, and multi-modal data fusion for urban modeling. He collaborates on EuroSDR initiatives and publishes regularly in remote sensing and robotics domains.

• Robotics
• Artificial Intelligence
• Computer Vision
• +5 more

Jong-Hwan Kim is a Professor at KAIST's College of Engineering, specialized in robotics and artificial intelligence. His research focuses on neural networks, autonomous systems, computer vision, and human-robot interaction. He has published extensively in top-tier conferences like CVPR, ICRA, and IEEE Transactions. Key contributions include work on developmental learning networks, episodic memory models, and AI applications in robotics and healthcare. He co-chaired the RiTA conference series and has collaborated with institutions globally. His research bridges theoretical advancements with practical applications in robotics, medical diagnostics, and multimodal AI. Affiliations: KAIST (main), Seoul National University (PhD 1987) Research Highlights: Visual odometry, gesture recognition, emotion modeling, and robotics task intelligence Recent projects include text recognition via finger movement, Alzheimer's disease classification using EEG-FNIRS fusion, and multimodal emotion recognition systems. His work emphasizes interdisciplinary approaches, integrating robotics, computer vision, and cognitive science.

Tobias SeidlView profile

Professor

• Bionics
• Biomechanics
• Neuroethology
• +3 more

Prof. Tobias Seidl serves as Vice Dean at the Westphalian Institute for Bionics within the Department of Mechanical Engineering at the Westphalian University of Applied Sciences in Bocholt, Germany. He has been with the university since January 2011, teaching bionics and sensor technology while leading research in biomimetic applications for robotics and engineering. Education: Bionics studies at Saarbrücken PhD on desert ant navigation systems under Rüdiger Wehner at the University of Zurich with fieldwork in Tunisia Professional experience at the European Space Agency (ESA) in Noordwijk, Netherlands Research Focus: Prof. Seidl's work centers on bionics , translating biological principles into engineering solutions. His expertise spans neuroethology (neural basis of natural behavior), functional morphology (structure-function relationships), and biomechanics . Key application areas include biomimetic robotics inspired by ants and spiders, sensor development, and bio-inspired materials. His research consistently bridges entomology with robotics, aerospace, and materials science to solve complex engineering challenges. Publication Trends: Recent publications (2025-2016) reveal a strong focus on biomimetic applications in robotics, 3D printing, and aerospace. Dominant themes include adaptive biomimetic valves for automotive cooling, force-based path integration in walking robots, hydrophobic surface replication, and satellite deployable structures inspired by insect wings. His work consistently leverages biological observations—particularly from desert ants and spiders—to advance robotic locomotion, adhesion mechanisms, and space technology. Research Infrastructure: As head of the Westphalian Institute for Bionics, Prof. Seidl leads R&D projects such as developing force sensors in ant legs for robotic applications. His institute serves as a hub for interdisciplinary collaboration between biologists and engineers, focusing on translating biological insights into technical innovations for automotive, aerospace, and medical applications.