جستجوی استاد راهنمای دانشگاهی

Davide Tateoمشاهده پروفایل

استاد مهمان

• Reinforcement Learning
• Robotics
• Safe and Reliable Robot Learning
• +۳ مورد دیگر

Davide Tateo is a postdoctoral researcher and visiting professor at TU Darmstadt, leading the Safe and Reliable Robot Learning Research Group within the Intelligent Autonomous Systems group of the Computer Science Department. His research focuses on developing safe and efficient reinforcement learning algorithms for real-world robotics applications. His work spans Reinforcement Learning (Safe RL, Deep RL) and Robotics (fast motion planning, locomotion). He is involved in multiple funded projects including KIARA (advanced manipulation in risky scenarios), DeepWalking (human gait learning), and INTENTION (active perception for legged robots). Recent publications highlight his expertise in Safe RL (inductive biases, collision probability fields), Locomotion (multi-embodiment, morphology-aware policies), and Optimization (contact planning, trajectory distillation). He collaborates with the PEARL lab at TU Darmstadt and has contributed to key workshops like CoRL 2024 and RSS 2024. Contact details: Email: davide.tateo@tu-darmstadt.de Room E303, Building S2|02, Hochschulstr. 10, Darmstadt Phone: +49-6151-16-20811

Ashwin Ramمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Wearable Computing
• Augmented Reality
• +۴ مورد دیگر

Ashwin Ram is a postdoctoral researcher at Saarland University's Human-Computer Interaction & Interactive Technologies Lab, under Prof. Jürgen Steimle. He holds a PhD in Computer Science from the National University of Singapore (NUS), advised by Prof. Shengdong Zhao, and a Bachelor's in Electronics Engineering from NIT Trichy. His research focuses on wearable augmented reality, smart glasses, and accessibility, leveraging cognitive and behavioral theories to design intelligent interfaces. Notable contributions include Mindful Moments (DIS '23, Honorable Mention), a mindfulness tool for smart glasses, and a quadruped robot guidance system for visually impaired individuals (CHI '24, Honorable Mention). He has served as an Associate Chair (AC) for UIST 2025 and CHI 2025. His work bridges HCI with wearable computing, exploring topics like video learning optimization (LSVP, IMWUT '21), sound source localization via neural networks (NCC '18), and accelerating Hawkes processes for event modeling (ICML '17 workshop). He collaborates internationally, including a research visit at UCL's Multi-Sensory Devices Group. Key achievements include 17 peer-reviewed publications (Google Scholar, ORCID: 0000-0003-1430-8770) and interdisciplinary projects like semantic floor map-based robot navigation. Beyond academia, he practices Carnatic music, plays guitar, and is fluent in Malayalam, Tamil, and English, with proficiency in French, German, and Hindi.

Ayush Tewariمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۳ مورد دیگر

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.

Christof Röhrigمشاهده پروفایل

استاد

• Robotics
• Mobile Robotics
• Wireless Sensor Networks
• +۳ مورد دیگر

Christof Röhrig is a Professor at the FH Dortmund - University of Applied Sciences and Arts , conducting research at the Institute for the Digitalization of Work and Living Environments (IDiAL). He leads the Intelligent Mobile Systems Lab , focusing on robotics, wireless sensor networks, and kinematic modeling. His research spans mobile robotics , indoor localization , and industrial automation , with a strong emphasis on IoT , 5G networks , and rescue robotics . Key trends in his recent work include LoRaWAN localization , modular robot design , and digital twin architecture . He collaborates extensively on motion control , sensor fusion , and energy-efficient systems , though no specific students, awards, or grants are listed in the provided data. The lab at IDiAL explores robotics in emergency response and industrial IoT applications , with a focus on precision localization and collaborative automation .

• Computer Vision
• Robotics
• 3D Reconstruction
• +۵ مورد دیگر

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.

• Integrated Circuits
• High-Frequency Electronics
• Radar Technology
• +۳ مورد دیگر

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

• 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

Yan Xiaمشاهده پروفایل

پژوهشگر

• 3D Vision
• Localization and Navigation
• Robotics
• +۲ مورد دیگر

Yan Xia is a Senior Researcher at the Computer Vision Group of the Technical University of Munich (TUM) and a Research Scientist at the Munich Center for Machine Learning. She collaborates with Prof. Daniel Cremers and previously completed her PhD at TUM under supervision of Prof. Uwe Stilla and Prof. Daniel Cremers, with a visiting period at the Visual Geometry Group of the University of Oxford under Dr. João Henriques.

• Control Engineering
• Automotive Engineering
• Model Predictive Control
• +۵ مورد دیگر

Dr.-Ing. Frank-Josef Heßeler is a Senior Research Engineer (Geschäftsführender Oberingenieur) and Deputy Institute Director at the Institute of Control Engineering (IRT), RWTH Aachen University . His work focuses on control systems for automotive and urban mobility applications, including model predictive control, vehicle localization, and intelligent infrastructure. He actively contributes to research in autonomous driving, hybrid drivetrains, and thermal systems optimization. Control Engineering Automotive Systems Model Predictive Control Thermal Diagnostics Urban Traffic Simulation His publications highlight advancements in connected vehicle localization, scenario-specific motion modeling, and hybrid drivetrain control. He collaborates extensively with Dirk Abel and other researchers. No scientific awards or student advisement details are explicitly mentioned in the provided texts. He is affiliated with the Institute of Control Engineering, engaging in projects like CERMcity (autonomous urban driving testbed) and Galileo-based navigation systems. His work integrates simulation platforms, Neuro-Fuzzy models, and hardware-in-the-loop testing for automotive control solutions.

Torsten Sattlerمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image-based Localization
• 3D Reconstruction
• +۳ مورد دیگر

Dr. Torsten Sattler is a computer vision researcher at RWTH Aachen University, Germany, specializing in image-based localization and 3D scene reconstruction. His work focuses on developing efficient algorithms for camera pose estimation relative to large 3D models, with significant contributions to mobile localization systems and scalable reconstruction techniques. His primary research interests include: Image-based localization and pose estimation Large-scale 3D scene reconstruction Structure-from-Motion techniques Efficient correspondence search algorithms Mobile vision applications Point cloud processing and rendering Dr. Sattler's publication record shows a clear progression from fundamental algorithm improvements to practical systems for real-world applications. His research demonstrates particular expertise in optimizing RANSAC implementations, developing direct 2D-to-3D matching techniques, and creating memory-efficient solutions for mobile devices. The trend in his work moves toward increasingly complex systems that address practical challenges in urban-scale localization and reconstruction. Award: Best Paper Award at the ICCV Workshop on Big Data in 3D Computer Vision (2013) Dr. Sattler has maintained strong collaborations with researchers including Bastian Leibe and Leif Kobbelt. His work bridges theoretical computer vision with practical applications in augmented reality, robotics, and mobile navigation systems, often providing publicly available source code and project pages to support reproducibility and further research.

• Geoinformatics
• Cartography
• Geodetic Science
• +۵ مورد دیگر

Prof. Monika Sester is a distinguished Professor and Executive Director of the Institute of Cartography and Geoinformatics at Leibniz University Hannover, within the Faculty of Civil Engineering and Geodetic Science. She also serves as Spokesperson for the Leibniz Research Center FZ:GEO and holds multiple leadership roles including Faculty Information Officer (FIO) for the Faculty of Civil Engineering and Geodetic Science, Ombudsman for Good Scientific Practice, and Exchange Coordinator for Geodetic Science and Geoinformatics. Her research focuses on the intersection of geospatial information science, cartography, and urban mobility. Prof. Sester's work spans several key areas: Geospatial data processing and analysis Cartographic representation and visualization Urban mobility and transportation systems Spatial data uncertainty and quality Digital mapping technologies and applications Historical map analysis and interpretation Prof. Sester's recent publications demonstrate a strong focus on applying advanced computational techniques to geospatial problems. Her work shows increasing emphasis on machine learning applications for map analysis, urban mobility optimization, and 3D spatial modeling. She has been particularly active in researching applications of deep learning for historical map interpretation, urban mobility patterns, and spatial uncertainty visualization. Her contributions to the field have been recognized through leadership positions in major research initiatives: Executive Director, Institute of Cartography and Geoinformatics Spokesperson, Leibniz Research Center FZ:GEO Faculty Information Officer, Faculty of Civil Engineering and Geodetic Science Ombudsman for Good Scientific Practice Member of multiple academic committees including the Admissions and Examination Board Prof. Sester actively collaborates with students and researchers across multiple projects focused on geospatial information systems, urban mobility, and cartographic visualization. Her leadership extends to guiding research directions within the Leibniz Research Center FZ:GEO, which brings together interdisciplinary expertise to address complex spatial challenges.

Johannes Schneiderمشاهده پروفایل

پژوهشگر

• Bundle Adjustment
• Visual Odometry
• Multi-camera Systems
• +۷ مورد دیگر

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.

Simon Bultmannمشاهده پروفایل

پژوهشگر

• Collaborative Perception
• Sensor Fusion
• Robot Perception
• +۴ مورد دیگر

Dr. Simon Bultmann is currently a Postdoctoral Researcher at the Robot Learning Lab, Albert-Ludwigs-Universität Freiburg. Previously, he completed his Ph.D. in the Autonomous Intelligent Systems Group at the University of Bonn (2019–2024) and earned his M.Sc. and B.Sc. in Electrical Engineering and Information Technology from Karlsruhe Institute of Technology (KIT, 2012–2018). His research focuses on collaborative perception, sensor fusion, and machine learning for embedded systems ("Smart Edge Sensors"). Key applications include real-time multi-modal semantic fusion, 3D scene perception, and autonomous UAVs. Awards: Best Paper Award at IEEE SSRR 2020 Finalist for Best Paper Award at RoboCup 2021 His work spans robotics, computer vision, and distributed systems, with contributions to international conferences like ICRA, RSS, and IAS. He has also been involved in competitions such as MBZIRC, demonstrating autonomous UAV capabilities in disaster response scenarios.

Benjamin Sugerمشاهده پروفایل

پژوهشگر

• Robotics
• Autonomous Systems
• Traversability Analysis
• +۶ مورد دیگر

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.

Federico Boniardiمشاهده پروفایل

پژوهشگر

• Robotics
• Mobile Robot Navigation
• Localization and Mapping
• +۶ مورد دیگر

Federico Boniardi is a researcher at the Autonomous Intelligent Systems group in the Department of Computer Science at Albert-Ludwigs-Universität Freiburg, Faculty of Engineering. He holds advanced degrees in Mathematics and Artificial Intelligence and has been actively contributing to robotics research since 2014. Bachelor’s in Mathematics, University of Milano (2006–2010) Master’s in Mathematics, University of Milano (2010–2012) MSc in Artificial Intelligence, University of Edinburgh (2013–2014) PhD Research Assistant, University of Freiburg (2014–2019) His research centers on mobile robot navigation, focusing on robust localization and mapping in indoor environments using LiDAR and architectural or hand-drawn floor plans. He explores long-term autonomy and human-robot interfaces, particularly in sketch-based map interpretation and shared control systems. His work integrates sensor calibration, topometric mapping, and deep learning for room layout extraction. The most recent publications reflect a strong trend in leveraging CAD and sketched maps for robot localization, combining probabilistic filtering, graph-based optimization, and semantic understanding. His contributions span key robotics venues such as IEEE/RSJ IROS, IEEE ICRA, and Robotics and Autonomous Systems. Scientific Contributions: Developed pose graph-based localization for long-term navigation Advanced LiDAR-based localization in architectural plans Explored Bayes filters for shared autonomy Enabled robot navigation using hand-drawn sketch interfaces Federico has served as a Teaching Assistant for Theoretical Computer Science and supervised student seminars on robot navigation. He has contributed to EU-funded research projects including RobDREAM and SQUIRREL, focusing on robot performance optimization and clutter management. He is based in Freiburg, Germany, and continues active research in autonomous intelligent systems.