Prof. Nils Pohl is a Professor for High Frequency Integrated Circuits at Ruhr-Universität Bochum and Deputy Head of the Integrated Circuits and Sensor Systems Department at Fraunhofer Institute FHR. His research focuses on microwave and terahertz integrated circuits, radar systems, and high-frequency sensor technologies. He holds a Dr.-Ing. in electrical engineering from Ruhr-Universität Bochum (2010) and has led multiple collaborative projects in radar technology and sensor development. Prof. Pohl is actively involved in IEEE technical committees and serves as a reviewer for top conferences like IEEE MTT-S and ISSCC. His awards include the IEEE MTT-S Outstanding Young Engineer Award (2018) and the IHP Fellowship (2017). Education : PhD in Electrical Engineering (2010), Ruhr-Universität Bochum Doctoral thesis on 80 GHz radar system design Research Interests : High-frequency integrated circuits for radar (FMCW, MIMO), terahertz sensors, antenna design, and material characterization Applications in automotive radar, industrial sensing, and non-destructive testing Grants & Projects : Leadership in Fraunhofer-Government collaborations for radar sensor development EU-funded projects on terahertz imaging and 6G communication systems Labs/Teams : Integrated Systems Team at Ruhr-Universität Bochum Fraunhofer FHR’s Chip Design Team
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نمایش ۱۶-۳۰ از ۱۱۱ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
- Computer Vision
- Machine Learning
- Video Analysis
- +۵ مورد دیگر
Prof. Laura Leal-Taixé is an Associate Professor at the Technical University of Munich (TUM) leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Chair, promoted from a 2017 Tenure Track Assistant Professorship. Her work focuses on advancing computer vision and machine learning, particularly in video analysis, multi-object tracking, and autonomous systems. She received a Sofja Kovalevskaja Award (2017) for her project socialMaps, which integrates dynamic social data into traffic modeling. Education: B.Sc./M.Sc. in Telecommunications Engineering, Technical University of Catalonia (UPC), Barcelona Ph.D. in Information Processing, Leibniz University Hannover (2014) Postdoc at ETH Zurich (2014–2016), and Senior Researcher at TUM’s Computer Vision Group (2016–2019) Research Interests: Multi-object tracking and segmentation in videos Motion analysis and semantic segmentation for autonomous driving Deep learning for video understanding Social dynamics modeling in urban environments Awards & Grants: €1.65M Sofja Kovalevskaja Award (Humboldt Foundation, 2017) DAAD Australia-German Joint Research Scheme (2017) Multiple travel grants from CVPR and Women in Computer Vision Labs & Collaborations: Dynamic Vision and Learning Group at TUM Collaborations with ETH Zurich, Northeastern University, and NVIDIA
Prof. Dr.-Ing. André Borrmann is an academic leader at the Technical University of Munich (TUM) , where he has headed the Chair of Computing in Civil and Building Engineering since 2011 (formerly Computational Modeling and Simulation). He serves as Director of the TUM Georg Nemetschek Institute - AI for the Built World since 2025 and Spokesperson for the Leonhard Obermeyer Center since 2013. Research Interests Artificial Intelligence in Civil Engineering Digital Twinning Building Information Modeling (BIM) Pedestrian Dynamics Knowledge Representation Construction Simulation His work focuses on AI application across the built environment lifecycle - from generative design to maintenance prediction - with significant contributions to BIM standardization and buildingSMART International IFC extensions. He co-authored the German Ministry of Transport BIM Roadmap and led the BIM4INFRA2020 project. Awards include the 2024 Konrad Zuse Medal and multiple best paper awards at international conferences.
Ganlin Zhangمشاهده پروفایل
پژوهشگر
- 3D Vision
- Visual SLAM
- Structure from Motion
- +۲ مورد دیگر
Ganlin Zhang is a PhD researcher at the Technical University of Munich (TUM) within the Computer Vision Group (Informatics 9). His work focuses on 3D Vision , Visual SLAM , and 3D Reconstruction using deep learning and geometric processing techniques. Key research areas: 3D Vision, Visual SLAM, Structure from Motion, 3D Reconstruction, Deep Learning Recent publications highlight advancements in RGB-only SLAM systems with implicit encodings, dynamic scene bundle adjustment, and robust rotation averaging methods. His work bridges classical geometry processing with modern AI approaches for spatial AI applications. He collaborates with leading researchers in the field, including Luc Van Gool and Michael R. Oswald , and contributes to cutting-edge developments in computer vision through active participation in conferences like CVPR and ICCV . The Computer Vision Group at TUM provides a vibrant research environment for his work, with access to state-of-the-art facilities at Boltzmannstrasse 3, Garching, Germany.
Dana Serditovaمشاهده پروفایل
پژوهشگر
- sociolinguistics
- dialectology
- sociophonetics
- +۷ مورد دیگر
Dana Serditova is a Postdoctoral Researcher in the English Department at the University of Freiburg under the Chair of Prof. Dr. Dr. h.c. Bernd Kortmann, with additional affiliation as a Guest Researcher at the Department of English and American Studies at Heinrich Heine University Düsseldorf (HHU). Her doctoral research on "Salience in New Orleans English" established expertise in regional dialect features including PIN~PEN merger, /l/-vocalization, and /aɪ/-monophthongization. Current work bridges sociolinguistics and speech technology through investigations of user speech adaptation when interacting with dialect-sensitive versus neutral automatic speech recognition (ASR) systems. Research spans dialectology across North America, England, and Australia, with critical examinations of bias in speech recognition algorithms and human-AI interaction dynamics. Her HHU-based SLAM LAB collaborations with Prof. Dr. Kevin Tang and international partners (Hochschule Düsseldorf, Newcastle University) explore AI-induced language change through experimental human-machine interaction studies. Active in interdisciplinary research at the linguistics-technology interface, she solicits collaborations in sociophonetics, ASR system design, and computational sociolinguistics.
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.
Maximilian Jun Zhang is a PhD student and Researcher at the Institute of Media Studies, Eberhard Karls University of Tübingen, working on the interdisciplinary project "The Answering Machine" , funded by the Volkswagen Foundation. His research explores voice-based human-AI interaction in psychotherapy, AI ethics, and immersive media. He holds an M.A. in Film Studies from Freie Universität Berlin and a B.A. in Journalism and Communication Studies. Education: Bachelor’s in Media Studies & German (Universität Tübingen) Bachelor’s in Journalism & Film Studies (Freie Universität Berlin) Master’s in Film Studies (Freie Universität Berlin) Research Interests: Focuses on AI ethics, artificial voices in media, VR, neurofilmology, and gender studies. His work combines empirical research with artistic collaboration, such as improvisational theater performances with AI chatbots to study human-machine relationships. Key Contributions: Awarded the 2023 AI in Film prize; presented at media conferences (e.g., GfM Jahrestagung 2023); active in public outreach via podcasts and science slams. Leads workshops on AI creativity and media analysis.
Pan Pan is a Professor in the Department of Biomedical Engineering at Huazhong University of Science and Technology, with extensive research contributions spanning medical image analysis, computer vision, and underwater wireless communications. Their work demonstrates strong interdisciplinary collaboration between biomedical engineering and computer science, with significant industry partnerships including Alibaba. Research interests focus on medical image analysis (particularly automatic breast ultrasound systems), deep learning applications in healthcare diagnostics, and secure underwater communications . Their work bridges theoretical advances with practical clinical applications, developing innovative segmentation algorithms, tumor detection systems, and secure communication protocols for specialized environments. Analysis of recent publications reveals a strong trend toward integrating multi-modal data fusion techniques with uncertainty-aware deep learning models for medical diagnostics. The research spans both fundamental algorithm development (novel segmentation networks, feature matching optimization) and domain-specific applications (ABUS tumor detection, ICU mortality prediction, underwater sensor networks). Pan Pan maintains active collaborations with major Chinese technology companies and academic institutions, evidenced by the consistent publication record in top-tier conferences including CVPR, ICCV, and NeurIPS. While specific awards aren't documented in the provided materials, the research impact is demonstrated through numerous high-impact publications across computer vision and biomedical engineering venues. The research program shows particular strength in translating computer vision techniques to medical applications, with significant contributions to semi-supervised learning approaches for medical image segmentation where labeled data is scarce. Recent work also demonstrates growing interest in secure communications for specialized environments like underwater sensor networks.
Benjamin Sugerمشاهده پروفایل
پژوهشگر
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.
Marcel Voßhansمشاهده پروفایل
پژوهشگر
Marcel Voßhans serves as a Researcher and Technical Project Leader for the AMEISE Team (Project RAFT) at Esslingen University of Applied Sciences' School of Computer Science and Engineering. He teaches specialized courses including Perceptional Algorithms, ROS, Sensory Systems (LiDAR/Camera), Autonomous Driving & Simulation, and Automotive Functional Safety. Education: 2015-2018: B.Eng. in Electrical Engineering and Information Technology, Hochschule Hannover 2018-2020: M.Sc. in Applied Computer Science: Autonomous Systems, Hochschule Esslingen Research Focus: His work centers on Autonomous Driving and AI-driven perception systems , with critical contributions to infrastructure reliability and sensor fusion for automated vehicles. He investigates how conventional road infrastructure interacts with autonomous systems through SLAM technology and deep learning-based environment detection. Publication Trends: His recent works demonstrate a cohesive research trajectory addressing real-world autonomous driving challenges, particularly in vehicle classification using stereo vision (2020), infrastructure-vehicle interaction requirements (2020), and conventional infrastructure reliability validation (2021). These studies emphasize practical implementation of AI in automotive contexts with strong safety considerations. Professional Background: Combines industry experience from Daimler AG (Steer-by-Wire development) and Continental Teves AG (testing rig automation) with academic leadership in the AMEISE research team. His international experience includes academic work in Sweden and China.
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.
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.
Maolin Gaoمشاهده پروفایل
پژوهشگر
Maolin Gao is a Researcher at the Computer Vision Group , TUM School of Computation, Information and Technology , and a PhD candidate at the Department of Informatics 9 , Technical University of Munich (TUM). He is supervised by Prof. Daniel Cremers and was a visiting scholar at Stanford University's Geometric Computing Lab under Prof. Leonidas J. Guibas in Summer 2024. Education : Bachelor in Physics from Tongji University (Shanghai), with exchange experience at TUM and RWTH Aachen Master's in Electrical Engineering and Information Technology from TUM, thesis on Blind Deconvolution supervised by Dr. Michael Hirsch, Prof. Philipp Hennig, and Prof. Bernhard Schölkopf at Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen Research Focus : Maolin's work bridges geometry and learning to enhance human and machine understanding of 2D/3D data. His research spans 3D shape matching , non-rigid correspondence , geometric consistency , and deep learning applications in computer vision and graphics. He also explores photometric stereo , bundle adjustment , and scale-invariant matching methods . Article Trends : His publications emphasize 3D shape analysis , including partial-to-partial matching , non-rigid deformations , and geometric consistency algorithms . Recent works like EchoMatch (CVPR 2025) and CoE (3DV 2025) highlight advancements in point cloud correspondences and cycle-consistency . Earlier papers address distributed optimization (3DV 2020) and isometric multi-shape matching (CVPR 2021). Scientific Awards : Oral Presentation Award at 3DV 2020 for Distributed Photometric Bundle Adjustment Labs & Collaborations : Affiliated with TUM CVAI Lab and Stanford Geometric Computing Lab . Collaborates with researchers like Prof. Florian Bernard and Dr. Michael Hirsch.
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.
- Robotics
- Computer Vision
- Sensor Fusion
- +۵ مورد دیگر
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.