جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Multimedia Systems
• Robotic Interaction
• +۵ مورد دیگر

Wang Yufei is affiliated with Xiamen University, China, holding positions across the School of Informatics, Institute of Artificial Intelligence, Center for Digital Media Computing, and School of Film. The research spans interdisciplinary domains, integrating artificial intelligence, robotics, and multimedia systems for real-world applications. Wang Yufei's research primarily focuses on artificial intelligence , computer vision , robotic interaction , and multimedia systems . Key areas include 3D scene modeling using Gaussian splatting, diffusion models for storytelling and image generation, reinforcement learning for interactive instruction, and AI applications in healthcare, agriculture, and urban planning. The work emphasizes robustness, real-time feedback, and human-centered design. The recent publications demonstrate a strong trend in deep learning applied to vision and multimodal systems , with increasing focus on interactive AI , embodied environments , and domain adaptation . Topics such as differentiable trajectory optimization, backdoor attacks in compression models, and context-aware assistance systems reflect both technical depth and societal relevance. Wang Yufei has not been mentioned in relation to any scientific awards in the provided text. No information is available regarding student advising or research grants. However, the breadth and volume of publications suggest active research leadership and likely involvement in funded projects. Research is conducted in collaboration with teams at Xiamen University, particularly within the Institute of Artificial Intelligence and the Center for Digital Media Computing, focusing on intelligent multimedia and robotic systems. The integration of AI with cultural and educational applications, such as piano instruction and heritage preservation, highlights a unique interdisciplinary lab environment.

• Singularities of Mappings
• Real Deformations of Singularities
• Classification of Singularities
• +۷ مورد دیگر

Joachim H. Rieger is a Professor of Geometry at the Department of Mathematics, Martin Luther University of Halle-Wittenberg in Halle, Germany. His research focuses on mathematical analysis of singularities and their applications across multiple domains. Research Interests: Professor Rieger's work spans: Singularity theory including deformations, classification, and invariants Geometric applications in differential geometry, knot theory, and computational methods Interdisciplinary connections to computer vision and symplectic structures Contact via email at rieger@mathematik.uni-halle.de or phone: +49-345-5524613.

Alexander Hermansمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Robotics
• +۷ مورد دیگر

Dr. Alexander Hermans is a researcher at the Institute for Vision and Graphics, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University. He is actively engaged in cutting-edge research at the intersection of computer vision, deep learning, and robotics, with a focus on 3D perception, segmentation, and anomaly detection. Research Interests: His primary research areas include Computer Vision , Deep Learning , Robotics , 3D Scene Understanding , Semantic and Instance Segmentation , and LiDAR-based Perception . His work often addresses the practical challenges of deploying vision systems in real-world robotic applications. Publication Trends: Dr. Hermans' recent publications demonstrate a strong trend towards leveraging transformer architectures for 3D and video understanding, developing robust methods for anomaly detection, and creating unified frameworks for diverse vision tasks. His research on diffusion models and self-supervised learning also highlights his engagement with the latest advancements in AI. Scientific Awards: Best Vision Paper Award at ICRA 2014 Advising and Grants: While no specific students or grants are listed, his role as a senior author on numerous publications and his leadership in creating benchmarks (like OoDIS) and software indicate a significant mentoring and project leadership role. His work is often supported by large-scale datasets and collaborations, suggesting involvement in substantial research grants and projects (e.g., the STRANDS project). Labs and Teams: He is a core member of the research group led by Prof. Bastian Leibe at RWTH Aachen, a group renowned for its work in computer vision and robotics. His research is closely tied to this lab, which focuses on developing robust, real-world applicable vision systems for autonomous agents.

Marina Kollmitzمشاهده پروفایل

پژوهشگر

• Autonomous Robot Navigation
• Human-Robot Interaction
• Force Compliance
• +۳ مورد دیگر

Marina Kollmitz is a researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg, Germany. She works in the Faculty of Engineering and focuses on robotics research with applications in hospital environments, particularly through the NaRKo project on compliant service robots for logistics in hospitals. Dr. Kollmitz completed her PhD in 2020 at the Autonomous Intelligent Systems group under the supervision of Prof. Dr. Wolfram Burgard at the University of Freiburg. Prior to her doctoral studies, she earned her Dipl.-Ing. in Mechanical Engineering from Leibniz University Hannover between 2007 and 2015, establishing a strong foundation in engineering principles that informs her robotics research. Her research focuses on Autonomous Robot Navigation , Human-Robot Interaction , and Force Compliance in robotic systems. She has pioneered work in detecting people and recognizing their mobility aids (wheelchairs, crutches, walking frames, etc.) using deep learning techniques. Her research enables robots to navigate safely and appropriately in human environments by understanding contextual cues about the people they encounter. She has developed specialized computer vision systems that can operate effectively in complex hospital settings where understanding human mobility needs is critical for safe robot operation. Dr. Kollmitz's publication record demonstrates a clear research trajectory from foundational work on social navigation to sophisticated deep learning approaches for 3D perception. A consistent theme across her work is the practical application of robotics in healthcare settings, with a particular emphasis on developing systems that can safely interact with people who have varying mobility needs. Her MobilityAids project has resulted in a significant contribution to the field with a publicly available dataset containing over 17,000 annotated RGB-D images. During her time at the University of Freiburg, Dr. Kollmitz served as a Teaching Assistant for the Introduction to Mobile Robotics course from 2016 to 2019, contributing to the education of undergraduate and graduate students in robotics fundamentals. She has also contributed to open-source robotics software frameworks, particularly extending Facebook's Detectron system for robot perception tasks. Dr. Kollmitz is actively involved in the Autonomous Intelligent Systems research group, which develops advanced robotic systems with a focus on perception, navigation, and human-robot interaction. Her work bridges theoretical robotics research with practical healthcare applications, developing technologies that could significantly improve how service robots operate in hospitals and other care facilities while maintaining safety and appropriate social interaction.

Sebastian Otteمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Recurrent Neural Networks
• +۱۲ مورد دیگر

Sebastian Otte is a Professor at the Institute for Robotics and Cognitive Systems at the University of Lübeck, where he leads the Adaptive AI research group. Prior to this, he was a postdoctoral researcher and substitute professor at the University of Tübingen, contributing significantly to the Cognitive Modeling and Distributed Intelligence groups. University of Lübeck, Professor (since 2023) University of Tübingen, Postdoc and Substitute Professor (2016–2023) Centrum Wiskunde & Informatica (CWI), Humboldt Fellow (2022–2023) His research focuses on recurrent and spiking neural networks, bio-inspired computing, efficient learning, and adaptive AI systems. He explores how neural models can perform online learning, handle multiple time scales, and solve complex cognitive tasks such as binding, prediction, and motor control. His work bridges machine learning with cognitive science and robotics. The recent publications show a strong trend toward physics-informed neural networks, finite volume methods for PDE modeling, and explainable AI via counterfactual reasoning. His work integrates deep learning with scientific computing, emphasizing robust, interpretable, and efficient models for real-world applications. Scientific awards include: Best Paper Award at ICANN 2019 Humboldt Research Fellowship Editor's Highlight in Water Resources Research He has supervised over 70 bachelor’s and master’s theses and actively mentors students in areas such as spiking neural networks, reservoir computing, and robotics. His teaching includes core computer science and advanced neural network courses. He has been involved in research projects with industry partners like Daimler AG and Mercedes-Benz AG. Otte leads the Adaptive AI research group, which focuses on developing next-generation AI systems that learn efficiently, adapt dynamically, and model complex cognitive and physical processes using biologically inspired architectures.

Norbert Haalaمشاهده پروفایل

استاد مدعو

• Computer Vision
• Geoinformatics
• 3D Data Reconstruction
• +۵ مورد دیگر

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.

• Spectral Imaging
• Mathematical Morphology
• Soft Metrology
• +۵ مورد دیگر

Hilda Deborah is a Senior Researcher at NTNU's Department of Computer Science (IDI Gjøvik), specializing in spectral imaging and mathematical morphology. Affiliated with the Faculty of Information Technology and Electrical Engineering, her work bridges computer science and cultural heritage preservation. She holds dual PhDs in Computer Science (NTNU) and Signal/Image Processing (Université de Poitiers). Her research focuses on texture perception, digital humanities, and innovative imaging dissemination methods. Key awards include the Marie Curie Postdoctoral Fellowship (2018–2020) and NTNU's Outstanding Academic Fellow Programme (2022–2026). She leads projects like the FRIPRO-funded Metrological Texture Analysis and the Lying Pen of Scribes initiative. Recent work includes hyperspectral pigment databases, Dead Sea Scroll fragment analysis, and interactive visualization tools like the Hyperspectral Pigments dataset (2022). Her publications span spectral imaging techniques, artifact analysis, and HCI applications in cultural heritage. She teaches database-related courses (IDATT2002, IDATG2002) and actively collaborates on interdisciplinary projects involving art conservation and digital humanities.

Thomas Tie Luoمشاهده پروفایل

دانشیار

• Trustworthy Artificial Intelligence
• Explainable AI (XAI)
• Adversarial Machine Learning
• +۱۲ مورد دیگر

Thomas Tie Luo is a tenured Associate Professor in the Department of Electrical and Computer Engineering and a courtesy joint appointee in the Department of Computer Science at the University of Kentucky, within the Stanley and Karen Pigman College of Engineering. Previously, he served as an Associate Professor at Missouri University of Science and Technology. He holds a Ph.D. in Electrical and Computer Engineering from the National University of Singapore. Ph.D., Electrical and Computer Engineering, National University of Singapore His research focuses on Trustworthy Artificial Intelligence, particularly in healthcare, medicine, and the Internet of Things (IoT). Key areas include explainable AI (XAI), adversarial and robust machine learning, security and privacy in federated learning, and time series analysis. He develops both theoretical models and real-world systems, such as federated satellite learning frameworks and anomaly detection platforms for edge computing. His work bridges deep learning with practical applications in medical imaging, dementia detection, and secure IoT environments. The recent publications (2023–2025) demonstrate a strong trend in advancing federated learning for satellite and edge networks, enhancing model interpretability in healthcare, and improving adversarial robustness in deep learning. These works appear in top-tier venues like AAAI, PAKDD, IEEE JSAC, and PerCom, often receiving recognition through best paper awards. His research integrates computer vision, signal processing, and secure distributed learning, reflecting a multidisciplinary approach to trustworthy AI. His scientific achievements have been recognized with multiple awards, including: Best Paper Award at PAKDD'25 workshop Best Paper Runner-Up at PAKDD'24 Best Paper Runner-Up at PerCom'24 Best Student Paper Award at AAIM'18 Best Paper Award at ICTC'12 Best Paper Finalist at INFOCOM'15 Dr. Luo advises PhD students in computer science, electrical engineering, and computer engineering. He has successfully mentored several doctoral graduates now in academic and industry roles, including Assistant Professors and Research Scientists at institutions like Washington State University and ByteDance. He has served as a grant panelist for the NSF, U.S. Department of Energy, and Euregio Science Fund, and as an external evaluator for faculty promotion at the University of Washington. His editorial roles include Area Editor for Pervasive and Mobile Computing and Ad Hoc Networks , and Associate Editor for several journals. He leads research efforts involving real testbeds and system implementations, such as federated learning frameworks for LEO satellite networks and lightweight object detection systems like YOGA. His lab emphasizes not only algorithmic innovation but also practical validation through deployed systems in edge computing, IoT, and mobile crowdsensing.

Alexander Asterothمشاهده پروفایل

استاد

• Machine Learning
• Stochastic and Probabilistic Learning Methods
• Surrogate Modelling
• +۵ مورد دیگر

Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.

Kay Pompetzkiمشاهده پروفایل

مدرس

• Robot Learning
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Kay Pompetzki is a Ph.D. student and Lecturer at the Intelligent Autonomous Systems Lab in the Department of Computer Science at Technische Universität Darmstadt . Previously known as Kay Hansel, his research focuses on Robot Learning , Machine Learning , and Human-Robot Interaction . He has contributed to projects involving optimal control , reinforcement learning , and visuo-tactile integration . Bachelor's in Applied Mathematics, RheinMain University of Applied Sciences Master's in Autonomous Systems, TU Darmstadt Visiting Scholar, Intelligent Robotics and Biomechatronics Laboratory (Nagoya University, 2023) Junior Expert Exchange Program delegate (2024) His publications span robot motion planning , goal inference , and sensor integration . Recent work includes tensor-based motion planning, kinematic graph matching, and telerobotics with haptic feedback. He has received recognition for Best Paper Awards in IEEE conferences. Advisor for 20+ theses and projects Reviewer for IEEE IROS, ICRA, CoRL, RSS, and ML workshops

Tim Schneiderمشاهده پروفایل

پژوهشگر

• Robotics
• Reinforcement Learning
• Robotic Manipulation
• +۲ مورد دیگر

Tim Schneider is a Ph.D. student at the Intelligent Autonomous Systems lab of Technische Universität Darmstadt , working in collaboration with École Centrale de Lyon under the Aristotle project. His research focuses on robotics , particularly at the intersection of active perception , tactile sensing , and robotic manipulation . His work investigates how robots can develop environmental understanding through physical interaction , drawing parallels to children's exploratory behavior . Key projects include tactile reinforcement learning , active inference for manipulation , and visuo-tactile sensor fusion in dynamic tasks. Recent publications emphasize foundation models for tactile learning , sensor simulation (TacEx), and multi-modal control systems (AllmAN). He supervises theses on topics ranging from teleoperation feedback to haptic MNIST benchmarks , with a focus on reinforcement learning applications in robotic systems.

Olga Moskatovaمشاهده پروفایل

استاد

• Media Theory
• Visual Culture
• Media Aesthetics
• +۱۲ مورد دیگر

Prof. Dr. Olga Moskatova is Professor of Media Theory at HfG Offenbach (Offenbach University of Art and Design), where she teaches and researches in the Department of Art. She previously held a junior professorship in media studies (visuality and image cultures) at Friedrich-Alexander University Erlangen-Nuremberg from 2018 to 2023. Her academic journey includes research at the Bauhaus University Weimar and a doctorate from the Berlin University of the Arts. PhD in Media Theory, Berlin University of the Arts, 2017 Studies in Social and Business Communication, Berlin University of the Arts & Université Stendhal 3 Grenoble Her research centers on the theory and aesthetics of visual media, with a focus on media materiality, new materialism, networked image cultures, avant-garde and media art, and the emerging concept of protective media and immunization dispositifs. She critically examines how AI technologies shape visual culture, particularly in surveillance capitalism and algorithmic image production. The most recent publications and projects reflect a strong engagement with AI and visual media, exploring how machine learning algorithms classify, generate, and moderate images across platforms. Her editorial work includes significant volumes on video conferencing, televisual seriality, and messy image networks, indicating a sustained interest in digital transformation and media infrastructures. Monograph: Painting on Celluloid: On Relational Materialism in Cameraless Film (2019) Monograph: Human-machine eye: The motif of artificial vision in film (2009) Editor: Video Conferencing: Practices, Politics, Aesthetics (2023) Guest Editor: # messy images , Networked Images in Surveillance Capitalism She founded the international research network 'AI and Visual Media', bringing together scholars from media studies, data studies, and contemporary art to investigate the societal and cultural impacts of visual AI. Her work emphasizes interdisciplinary methodologies, including data set archaeology and media ecology analysis. She has presented lectures at institutions such as Yale University, highlighting the global reach of her research. Olga Moskatova leads research initiatives rather than traditional lab structures, fostering collaborative inquiry into the politics inscribed in AI infrastructures. Her recent workshop 'On Protective Media' explores boundary-forming mechanisms in digital culture, indicating a forward-looking trajectory in theoretical media studies.

Christian Meltebrinkمشاهده پروفایل

استاد

• Intelligent sensor systems
• Physics
• Automated and autonomous agricultural systems
• +۱۲ مورد دیگر

Prof. Dr.-Ing. Christian Meltebrink is a faculty member at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on intelligent sensor systems , autonomous agricultural machinery , and safe environmental perception for outdoor robotics. Key research areas include: Validation methodologies for sensor systems in variable conditions Humanoid test bodies for autonomous vehicle safety Reproducible simulation of agricultural interference factors Embedded systems for field robots Optoelectronic sensor technology Recent publications address sensor validation for autonomous machines, with emphasis on detection reliability and environmental perception . His work integrates CCD/CMOS imaging , spectral analysis , and sensor fusion for agricultural applications. As head of the Laboratory for Intelligent Sensor Systems (located in buildings SO and SP, Sedanstraße 26, Osnabrück), he leads projects like "Amaizeing" field robot and high-speed sensor chip development . Collaborative efforts extend to industry partners (e.g., SICK AG) and institutions like Technische Universität Berlin. Teaching responsibilities include Physics I/II and Sensor Systems for engineering students. His laboratory delivers hands-on research opportunities in imaging technology , autonomous systems , and embedded sensor solutions .

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Dr. Mor Geva is a prominent researcher in natural language processing and machine learning, focusing on transformer-based language models, interpretability, and knowledge representation. Their work addresses fundamental questions about model reasoning, uncertainty expression, and factual recall mechanisms. Key research themes: language model interpretability, multi-hop reasoning limitations, numerical encoding, cross-modal knowledge gaps Recent publications analyze gradient projection, attention head functionality, and fallback behaviors under uncertainty 2023-2025 studies reveal insights into factual association recall, knowledge editing ripple effects, and vision-language model interactions. Collaborations span top institutions in ACL, EMNLP, and ICLR venues. Current research explores parametric knowledge tracing, encoder-decoder dynamics, and attention hijacking phenomena. Tools like LM-Debugger enable interactive model inspection, while theoretical work examines base-10 numerical encoding and task vector creation.

• Computer Science
• Multimodal Learning
• 3D Vision
• +۴ مورد دیگر

Junjie Fei is an active researcher in Computer Science with a focus on Multimodal Learning , Vision-Language Understanding , and Retrieval-Augmented Generation . They have collaborated with prominent researchers like Mohamed Elhoseiny , Sanjoy Chowdhury , and Jun Chen on various projects. Key research contributions include: Part-Aware 3D Vision-Language Understanding (2024) with novel tasks like Part-Aware Point Grounding MAGNET (2025) - a multi-agent framework for audio-visual retrieval WikiAutoGen (2025) - automated Wikipedia-style multimodal article generation V-RAG (2024) - vision-centric retrieval-augmented generation for document haystacks While no explicit academic affiliation or awards are stated in the provided text, their work spans 3D object segmentation , multi-video temporal grounding , multimodal knowledge integration , and large document reasoning , indicating significant contributions to AI research.