جست‌وجوی استاد و پژوهشگر

Kevin Verbeekمشاهده پروفایل

استادیار

• Computational Geometry
• Graph Drawing
• Information Visualization
• +۳ مورد دیگر

Kevin Verbeek is an Assistant Professor in the Applied Geometric Algorithms group at Eindhoven University of Technology (TU/e), affiliated with the EAISI Foundational research institute. His research focuses on computational geometry, information visualization, and algorithms for cartographic challenges. He holds a PhD from TU/e (2012) and conducted postdoctoral research at UC Santa Barbara (2012–2014). He coordinates the TU/e Honors Academy's 'Competitive Programming and Problem Solving' track and is a member of the Eindhoven Young Academy of Engineering. Education: PhD in Applied Geometric Algorithms, TU Eindhoven (2012) Postdoctoral Researcher, UC Santa Barbara (2012–2014) Research Interests: Algorithmic stability for dynamic data visualization Geometric algorithms for social networks and cartography Topological data analysis and computational topology Braided river network modeling Key Projects: NWO Veni Award-funded project on stable geometric algorithms (2015–present) GLAMMap: Interactive geo-visualization for cultural heritage metadata Teaching & Outreach: Coordinator of Honors tracks in Competitive Programming Instructor for courses like 'Heuristic Algorithms' and 'Computer Science Research Project' Awards: NWO Veni Award for 'Stable Geometric Algorithms' (2015) Labs/Teams: Active in EAISI Foundational, focusing on algorithmic spatial data analysis and foundational AI research.

Jeannette Bohgمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Jeannette Bohg is a Professor of Robotics and Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Her work focuses on perception, learning, and real-time methods for autonomous robotic manipulation and grasping, emphasizing cross-embodiment learning. She previously led the Autonomous Motion Department at MPI for Intelligent Systems (2012–2017) and completed her PhD at KTH in Stockholm, with earlier studies at Chalmers and TU Dresden. Notable awards include the 2019 IEEE Early Career Award and the 2020 Robotics: Science and Systems Early Career Award. Education: PhD in Robotics, Perception and Learning (RPL), KTH Royal Institute of Technology, Stockholm (2012) Masters in Art and Technology, Chalmers University, Gothenburg Diploma in Computer Science, TU Dresden Research interests span robotic grasping, equivariant policies, and scalable data-driven approaches. Key projects include the DROID dataset for cross-embodiment learning and development of the Tidybot mobile manipulator platform. Her lab explores principles of human sensorimotor coordination to enhance robotic autonomy, integrating machine learning and computer vision. Recent work emphasizes leveraging human demonstrations and vision-language models for generalizable manipulation skills, alongside tactile sensing and sim-to-real transfer techniques.

Ehsan Adeliمشاهده پروفایل

استادیار

• Translational Artificial Intelligence
• Medical Imaging
• Computational Neuroscience
• +۳ مورد دیگر

Ehsan Adeli is an Assistant Professor of Psychiatry & Behavioral Sciences at Stanford University, with courtesy appointments in Computer Science and Biomedical Data Science. He leads the Stanford Translational AI (STAI) Lab, focusing on developing AI algorithms for healthcare and mental health. His research integrates 3D motion analysis from video data with neuroimaging techniques like MRI to study neurodegenerative and neuropsychiatric diseases. He holds academic roles including Director of AI/Innovation in Precision Mental Health and membership in institutes like HAI and Wu Tsai Neurosciences. His work spans digital health, computational neuroscience, and explainable machine learning. Education: Ph.D. in Artificial Intelligence & Computer Vision (Carnegie Mellon University), Postdoc in Biomedical Imaging & Computational Neuroscience (UNC Chapel Hill). Research interests include translational AI for mental health, digital humans (3D motion analysis), and human neuroscience (MRI-based brain function exploration). His lab develops tools like the Short Physical Performance Battery (SPPB) app for objective assessments. Notable honors include the Chairman’s Award for Educational Excellence (2023) and Jaswa Innovator Award (2022-2024). He advises over 20 graduate and postdoctoral students across multiple disciplines. Labs/Teams: STAI Lab, Computational Neuroscience Lab, Stanford Vision and Learning Lab.

Raymond A Yehمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Raymond A. Yeh is an Assistant Professor in the Department of Computer Science at Purdue University since Fall 2022. Previously, he was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC) and completed his PhD in Electrical Engineering at the University of Illinois at Urbana-Champaign (UIUC) in 2021. His research focuses on machine learning and computer vision, particularly in developing algorithms for effective and explainable models across audio, vision, language, and multi-agent systems. Education: Ph.D., Electrical Engineering, UIUC (2021) M.S., Electrical Engineering, UIUC (2016) B.S., Electrical Engineering, UIUC (2014) Research Interests: His work bridges machine learning and computer vision, emphasizing equivariance in neural networks, robustness, and scalable algorithms. Key areas include: Generative models (diffusion models, inpainting) Equivariant deep learning architectures Multi-modal reasoning (vision-language) 3D reconstruction and simulation Recent Contributions: Recent work includes model immunization techniques, scale-equivariant networks, and novel datasets like Tree-D Fusion. His publications span top venues (CVPR, NeurIPS, ECCV) with 4,551 total citations (h-index 18 as of 2025). Awards: Google PhD Fellowship (2018) Best Paper Runner-up at CVPR Workshop (2024) National Science Foundation (NSF) Grant (2024) Purdue Seed for Success Award (2024) Teaching: Courses include Introduction to AI, Computer Vision with Deep Learning, and Foundations of Deep Learning. Student evaluations consistently score above 4.5/5.0. Labs: Leads the Purdue Vision and Learning Lab, focusing on advancing AI through robust, interpretable models with practical real-world applications.

Jacob Feldmanمشاهده پروفایل

استاد

• Cognitive Psychology
• Visual Perception
• Perceptual Organization
• +۴ مورد دیگر

Jacob Feldman is a Professor at Rutgers, The State University of New Jersey , affiliated with the Department of Psychology and the Rutgers Center for Cognitive Science (RuCCS). His research focuses on visual perception , particularly perceptual organization and shape, as well as categorization and concept learning . He employs Bayesian and mathematical models to explore how humans interpret visual data and organize objects into coherent categories. Feldman received his Ph.D. in 1992 from the M.I.T. Department of Brain and Cognitive Sciences and has remained at Rutgers since. His work emphasizes the simplification of complex data through perceptual and cognitive processes, with applications in robotics, virtual reality, and machine learning . Recent studies examine human-robot interaction, dynamic shape cues, and probabilistic foundations of mental representations. His publications span computer vision, cognitive psychology, and AI , reflecting interdisciplinary collaborations. Key themes include structure-from-motion , shape similarity , and cue integration in perception.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Han Gao is an academic affiliated with Technical University of Denmark (DTU), specifically within the Department of Informatics and Mathematical Modelling at DTU Informatics. Their research focuses on interdisciplinary areas spanning artificial intelligence, machine learning, robotics, and control systems. They have also collaborated with institutions like Zhejiang University, Huawei Technologies, and others, indicating a broad network of academic and industrial partnerships. Key research interests include generative AI models, adversarial machine learning, swarm robotics, and energy systems optimization. Their work often bridges theoretical advancements and practical applications, such as improving UAV localization accuracy under GNSS denial conditions and enhancing cybersecurity in industrial IoT. Publications highlight contributions to 3D reconstruction (e.g., DIG3D), robust backdoor attack mitigation, and unified vision models like DINO-X. Collaborations span domains like healthcare (tongue image segmentation) and environmental sensing (remote sensing analysis). While no explicit grants or awards are listed, their prolific publication record (over 150 papers since 2004) underscores active involvement in cutting-edge research. Ongoing projects include risk-aware robotics, edge computing for logistics, and explainable AI frameworks.

• Computer Vision
• Deep Learning
• Robotics
• +۵ مورد دیگر

Hao Yang is a Professor at Southeast University's School of Computer Science and Engineering, specializing in computer vision, deep learning, and robotics. His research spans multiple domains including medical imaging, infrastructure inspection, and multimodal AI systems. His research interests focus on advancing computer vision techniques for practical applications. He develops innovative deep learning architectures for object detection, image segmentation, and quality assessment, with particular emphasis on solving challenges in complex real-world scenarios including rotated object detection, ancient character recognition, and 3D point cloud analysis. His work often integrates novel transformer variants and Mamba architectures to address domain-specific challenges. His publication record shows significant contributions across multiple IEEE and ACM journals, with a strong emphasis on practical applications of AI in engineering contexts. His recent work demonstrates expertise in adapting cutting-edge neural network architectures to solve domain-specific problems in civil engineering, medical imaging, and transportation systems. Hao Yang actively collaborates across disciplines, with publications spanning computer science, electrical engineering, civil engineering, and biomedical applications. His work frequently appears in top-tier conferences including CVPR, AAAI, and ACL, demonstrating his broad impact across multiple AI subfields.

Andreas Birkمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Underwater Robotics
• +۵ مورد دیگر

Prof. Andreas Birk is a Professor of Electrical Engineering & Computer Science at Constructor University Bremen gGmbH, leading the Robotics Research Group. His work bridges basic research in artificial intelligence and applied robotics, with a focus on underwater systems, disaster response, and autonomous navigation. He holds a PhD from Universität des Saarlandes and has held visiting professorships at Vrije Universiteit Brussel and other institutions. Education: Dr. rer. nat. (1995) – Universität des Saarlandes, Saarbrücken MSc (Diplom) – Computer Science (1993) BSc (Vordiplom) – Computer Science (1991) Research Interests: Birk’s dual focus spans theoretical intelligence modeling and engineering applications like underwater robotics, SLAM (Simultaneous Localization and Mapping), and human-robot interaction. His projects include underwater cultural heritage mapping, autonomous container unloading, and deep-sea exploration. Key Projects: CADDY: Cognitive Autonomous Diving Buddy (EU-funded HRI research) MORPH: Multimodal Underwater Surveys with UUVs FloodEvac: Robotic assessment of flood risks in infrastructure Awards: Recognized for innovation in robotics competitions (e.g., RoboCup, ICRA), and leadership in IEEE TC Safety, Security, and Rescue Robotics. Received the Ernst-Lange-Prize and Faulhaber Award. Teaching & Outreach: Pioneered online robotics education during the pandemic, emphasizing hands-on labs. Co-founded Constructor’s Robotics program and mentors through Entrepreneurship and Innovation initiatives. Labs & Teams: Leads the Robotics Research Group, collaborating with teams on underwater vision, simulation-in-the-loop validation, and multi-robot systems.

• Machine Learning
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

DUPUIS Yohan is a Research Director at CESI, leading the Engineering and Numerical Tools research team. His work focuses on perception and mapping for cyber-physical systems, intelligent robotics, and transportation systems. He holds an HDR from the University of Rouen Normandy (2019) and a PhD in Electrical Engineering (2012). He coordinates major national projects including the France 2030 CAIRE project (AI sector), ASTRID Robotics SCOPES, and Battery School initiatives. Education includes an Engineering Diploma from ESIGELEC (2009), MSc in Electrical Engineering from Union Graduate College (USA), and a PhD focused on omnidirectional vision for biometrics. His research spans semantic mapping, autonomous systems, and sensor fusion with applications in smart cities, industrial automation, and maritime networks. Key research interests include: Multi-agent cooperative perception SLAM algorithms for low-texture environments Autonomous vehicle localization Human-robot interaction in industrial contexts LiDAR and radar sensor systems He advises 8 active PhD students working on topics like human-robot affordance modeling, object pose estimation, and mobility prediction in smart cities. Notable contributions include the SynWoodScape autonomous driving dataset and innovative digital twin methodologies for industrial workstations. Led projects include: France 2030 CAIRE project (AI sector coordination) ASTRID Robotics SCOPES (2022-2025) Battery School initiative (2022-2027) ConfluenceS Excellence program (2024-2032) His publications span over 50 peer-reviewed articles, with recent focus on multimodal perception systems and AI-driven urban mobility solutions.

Alexander Schaeferمشاهده پروفایل

پژوهشگر

• Robotics
• Autonomous Systems
• Lidar-based Localization
• +۶ مورد دیگر

Alexander Schaefer is a researcher in the Department of Computer Science at the University of Freiburg, affiliated with the Autonomous Intelligent Systems group under Prof. Dr. Wolfram Burgard. His work focuses on robotics, particularly lidar-based localization, mapping, and sensor modeling for mobile and agricultural robots. His research interests include autonomous navigation, 3D point cloud processing, sensor fusion, and agricultural robotics. He has made significant contributions to lidar-based mapping, change detection in dynamic environments, and the development of compact and differentiable map representations using cosine transforms. The recent publications highlight a strong trend in urban and agricultural vehicle localization, robust sensor modeling, and probabilistic inference for robot navigation. His work frequently appears in top robotics conferences such as ICRA, IROS, and IEEE RA-L, often in collaboration with leading researchers in the field. Alexander Schaefer has supervised several master's theses on topics including autonomous vehicle localization, multispectral soil mapping, and symmetry-based object detection in agricultural fields. He has also been involved in teaching, notably as a supervisor for the Robot Mapping lecture. He has collaborated on research projects such as the Samsung Global Research Outreach Program and the EU project Flourish, focusing on robotic solutions for smart farming using the Bonirob platform. His work bridges academic research with real-world applications in autonomous systems.

Daniel Kuhnerمشاهده پروفایل

پژوهشگر

• Robotics
• Autonomous Intelligent Systems
• Human-Robot Interaction
• +۷ مورد دیگر

Daniel Kuhner is a researcher in the Department of Computer Science at the University of Freiburg, affiliated with the Autonomous Intelligent Systems group under Prof. Dr. Wolfram Burgard. He holds a B.Sc. and M.Sc. in Computer Science from the same institution and has been a PhD student since 2013. Education: Bachelor of Science in Computer Science, University of Freiburg (2007–2010) Master of Science in Computer Science, University of Freiburg (2010–2013) His research centers on robotics and intelligent systems, with key interests in object manipulation, motion planning, human-robot interaction, and brain-computer interfaces. He is involved in the NeuroBots project under the BrainLinks-BrainTools initiative, focusing on assistive technologies for users with communication impairments. His teaching includes organizing proseminars and system design projects on brain signal decoding from 2014 to 2018. Daniel Kuhner's recent publications highlight trends in robot task planning using natural referring expressions, tactile-based object segmentation, and BCI-driven assistive robotics. His work bridges autonomous exploration, real-time interaction, and adaptive systems, emphasizing practical applications in assistive and service robotics. Scientific Awards: No scientific awards mentioned in the provided text. Daniel Kuhner has supervised multiple bachelor's theses, including those of Claas Bollen and Milan Benninger. He has been involved in organizing academic teaching activities and research projects. While no grants are explicitly listed, his participation in BrainLinks-BrainTools indicates involvement in funded interdisciplinary research. He is also associated with the development of autonomous exploration frameworks and assistive robotic systems. Labs and Teams: Autonomous Intelligent Systems Group, University of Freiburg NeuroBots Project, BrainLinks-BrainTools

Josh Tenenbaumمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Cognitive Science
• +۶ مورد دیگر

Josh Tenenbaum is a Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT). He leads the Computational Cognitive Science group, dedicated to understanding how humans learn and reason, particularly focusing on the statistical and computational principles underlying human cognition. His work bridges cognitive science, machine learning, and artificial intelligence, emphasizing Bayesian models and probabilistic reasoning. Tenenbaum's research explores topics like concept learning, causal inference, intuitive physics, and language acquisition, with implications for both human cognitive development and AI systems. His research integrates computational models with behavioral experiments, aiming to explain how people generalize from limited data and form abstract theories of the world. Notable contributions include foundational work on probabilistic models of cognition, the development of the Isomap algorithm for nonlinear dimensionality reduction, and advancements in cognitive science through computational frameworks. Tenenbaum collaborates with students and researchers across disciplines, including neuroscience, computer science, and linguistics. He emphasizes interdisciplinary approaches to address challenges in understanding human intelligence and building more capable AI systems. His group's work is published in top journals like Science and Proceedings of the National Academy of Sciences , and he regularly presents at conferences such as NIPS and Cognitive Science Society meetings. Key themes in his research include the role of prior knowledge in learning, the computational basis of intuitive theories, and the development of algorithms that mimic human-like reasoning. Tenenbaum is also involved in educational initiatives, advising students pursuing PhDs in Brain and Cognitive Sciences or Electrical Engineering and Computer Science at MIT.

Yu Luمشاهده پروفایل

استادیار

• Video Coding
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Yu Lu is an Assistant Professor in the Department of Biological & Ecological Engineering at Oregon State University. Their research focuses on video coding, image processing, and algorithm optimization with applications in multimedia systems and computer vision. Current interests include fast video compression algorithms, salient object detection, and error resilience in 3D video coding. Yu Lu has published extensively on topics such as HEVC/SHVC intra/inter coding optimizations, sparse regularization techniques for image restoration, and Bayesian decision models for scalable video coding. Research highlights include developing multi-stage motion estimation algorithms for versatile video coding (VVC), fast intra-coding methods using online learning, and depth map error concealment strategies for 3D video. Their work bridges algorithmic efficiency and practical multimedia applications, with publications spanning both traditional video coding standards (HEVC) and emerging formats like screen content coding. Publications trends show a strong emphasis on optimizing video encoding processes through hierarchical classification, adaptive switching mechanisms, and edge-assisted mode decisions. Earlier work (2013) explored piezoelectric material analysis, indicating interdisciplinary research interests. No awards or grants are explicitly listed in the provided materials.

• Spacecraft Navigation
• Autonomous Systems
• Computer Vision
• +۵ مورد دیگر

Arunkumar RATHINAM is a Postdoctoral researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the CVI2 department. His research focuses on autonomous systems, spacecraft navigation, and machine learning applications in space exploration. He has contributed to the development of datasets like SPARK and Zero-G Lab, which advance space operations emulation and computer vision techniques. Affiliations: SnT-CVI2, University of Luxembourg Key Research Areas: spacecraft pose estimation, thermal anomaly detection, autonomous navigation, and robotics for space missions His work bridges machine learning with practical space systems, addressing challenges in thermal monitoring (e.g., Li-ion battery cells) and vision-based navigation for spacecraft proximity operations. He has pioneered experimental facilities like the Zero-G Lab to simulate microgravity environments and test space robotics systems. Notable contributions include datasets such as SPARK and AKM, which provide critical data for training models in spacecraft trajectory estimation and textureless target detection. His publications emphasize real-world applications of AI in space systems, including RGB-T pedestrian detection and hybrid attention mechanisms for robust vision systems.

Niclas Vödischمشاهده پروفایل

پژوهشگر

• Continual Learning for Robotics
• Machine Perception
• Simultaneous Localization and Mapping (SLAM)
• +۳ مورد دیگر

Niclas Vödisch is a Ph.D. student and researcher at the Autonomous Intelligent Systems Lab in the Department of Computer Science at the University of Freiburg. Supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada, he is actively contributing to the field of robotics and AI as a member of the ELLIS Society. His research focuses on enhancing machine perception and SLAM systems using deep learning methods, with primary applications in mobile robotics and autonomous driving. Education: Ph.D. Student at University of Freiburg (June 2021 - June 2025, thesis submitted, defense pending) Visiting Ph.D. Student at University of Zurich (June 2024 - December 2024) M.Sc. in Computational Science and Engineering at ETH Zurich (September 2018 - May 2021) Visiting Undergraduate Student at Carnegie Mellon University (August 2016 - May 2017) B.Sc. in Computational Engineering Science at RWTH Aachen University (September 2014 - June 2018) Vödisch's research interests center around Continual Learning for Robotics, Machine Perception, and Simultaneous Localization and Mapping (SLAM). His work addresses the critical challenge of reducing dependency on extensive annotated training data in robotic perception systems. Through innovative approaches leveraging foundation models, he has developed methods that achieve high performance with minimal supervision, making robotic systems more adaptable to real-world environments. His research spans theoretical advances in deep learning and their practical implementation in autonomous systems. An analysis of his publication record reveals a clear progression from foundational SLAM techniques to sophisticated continual learning frameworks and foundation model applications. A unifying theme across his work is data efficiency in robotic perception, with increasing emphasis on collaborative multi-agent systems and cross-modal information integration for robust performance in challenging conditions. His recent publications demonstrate growing influence in the robotics community, evidenced by the IROS 2024 Best Paper Award for his BEVCar work. Scientific Awards: IROS 2024 Best Paper Award on Cognitive Robotics (BEVCar) IROS 2024 Best Student Paper Award Finalist (BEVCar) Dean's List at Carnegie Mellon University (fall 2016) DAAD full scholarship for CMU studies (2016-2017) Niclas has mentored numerous Master's students on projects spanning LiDAR panoptic segmentation, collaborative scene graph generation, and autonomous driving systems. His teaching portfolio includes co-organizing seminars on Robot Learning and Learning with Limited Supervision, as well as leading the FreiCAR practical autonomous driving course across multiple semesters. His research has received substantial funding from the German Research Foundation (DFG) Emmy Noether Program, NVIDIA academic grants, and Qualcomm Technologies Inc., reflecting the significance and potential impact of his work. As a key contributor to the Autonomous Intelligent Systems group at Freiburg, Vödisch collaborates closely with the Robot Learning group and has extended his research network through his visiting position at the University of Zurich's Robotics and Perception Group. His interdisciplinary approach bridges computer vision, robotics, and machine learning, positioning him at the forefront of research in AI-powered autonomous systems with practical real-world applications.