جستجوی استاد راهنمای دانشگاهی

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +۳ مورد دیگر

Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.

Oliver Deussenمشاهده پروفایل

استاد

• Visualization
• Computer Graphics
• Digitalization Processes
• +۴ مورد دیگر

Oliver Deussen is a Professor of Visual Computing at the University of Konstanz, recognized by the German Informatics Society (GSI) as a Fellow for his contributions to computer science. His research focuses on visualization, robotics, and environmental modeling, particularly in plant and landscape representation. He has pioneered methods in image manipulation and robotic painting, emphasizing digitalization's societal impacts. His work spans computational biology (e.g., schooling fish behavior) and AI-driven creative technologies. Research Interests: Visualization techniques, swarm behavior analysis, robotic creativity, and interdisciplinary applications of computer science. He explores how computational methods can model natural systems and enhance human-machine interaction. Awards: Fellow of the German Informatics Society (GSI) His research often bridges theory and practice, with contributions to SLAM frameworks, style transfer algorithms, and uncertainty visualization tools. Collaborations in robotics and biology reflect his commitment to applied computational research.

• Computer Vision in Medical Imaging
• Machine Learning
• Medical Imaging Segmentation
• +۳ مورد دیگر

Dr. Antonio Ortiz is a Researcher at the University of Bonn, affiliated with the Life and Medical Sciences Institute (LIMES) and the IRU Mathematics and Life Sciences group. He works under the supervision of Professors Alexander Effland and Jan Hasenauer. His research focuses on Computer Vision in Medical Imaging and Machine Learning applications. He holds a Ph.D. in Electric and Electronics Engineering from Cinvestav, Mexico (2023), a Master's in Computer Science from Cicese, Mexico (2019), and a Bachelor's in Mechatronic Engineering (2017). His research integrates Bayesian methods, deep learning, and optical flow techniques for cardiac MRI segmentation, visual-inertial SLAM systems, and 3D shape measurement. Recent work emphasizes adaptive algorithms for medical imaging and robotics applications. Publications span medical imaging, robotics, and materials science, with a focus on algorithmic innovation and interdisciplinary applications. No scientific awards are explicitly mentioned, but his work demonstrates strong academic contributions. His advising activities and grants are not detailed in the provided text. He collaborates within the Effland Lab and IRT Mathematics and Life Sciences team.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +۳ مورد دیگر

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

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

Tobias Neiß-Theuerkauffمشاهده پروفایل

پژوهشگر

• Optical 3D Metrology
• Augmented Reality in Surgery
• Medical Image Analysis
• +۳ مورد دیگر

Tobias Neiß-Theuerkauff, M.Sc., is a researcher at the Institute for Applied Photogrammetry and Geoinformatics (IAPG) within Jade University of Applied Sciences, Oldenburg. His work centers on optical 3D metrology applications in surgical assistance and maritime systems, currently contributing to the ASKAR3D project (2023-2026) funded by the Federal Ministry of Education and Research for developing augmented reality-guided knee arthroplasty systems. His research integrates computer vision, photogrammetry, and AI to solve practical challenges in medical imaging and maritime engineering. Recent publications demonstrate a focused shift toward surgical applications, developing AI-driven 3D bone reconstruction and semantic segmentation techniques for knee arthroplasty, while maintaining expertise in maritime 3D visualization established through earlier work on vehicle simulation and autonomous systems. Supported by significant grants including ASKAR3D and CoSAIR (Collaborative Spatial Artificial Intelligence in Realtime), his project portfolio reflects strong federal funding recognition. He has co-supervised Master's research in Geodesy and Geoinformatics, specifically guiding thesis work on point cloud triangulation for Unreal Engine visualization in 2019. As a core member of IAPG's Laboratory for optical 3D metrology, Neiß-Theuerkauff collaborates with leading researchers including Prof. Dr. Frank Wallhoff and Prof. Dr. Thomas Luhmann, bridging theoretical computer vision with clinical and maritime implementations through interdisciplinary teamwork.

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

André Borrmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Building Information Modeling & Processing
• Digital Twinning
• +۷ مورد دیگر

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.

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.

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.

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.

• Medical Image Analysis
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

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.