جست‌وجوی استاد و پژوهشگر

• Large Language Models
• Autonomous Driving
• Computer Vision
• +۴ مورد دیگر

Xingcheng Zhou is a Research Assistant at the Technical University of Munich (TUM), affiliated with the Chair of Robotics, Artificial Intelligence and Real-time Systems since 2023. He holds an M.Sc. in Electrical and Computer Engineering from TUM (2021) and previously worked as an Industrial AI Researcher at Siemens. Research Interests: Focus on Large Language Models , Vision Language Models , 3D Environment Perception , and Domain Adaptation in autonomous driving contexts. Publications: Contributions to 3D object detection refinement, sim2real domain adaptation, vision-language models, and dataset development for intelligent transportation systems. Teaching Involvement: Co-supervisor for master's theses and seminars on autonomous agents, perception models, and traffic environment understanding. Advising: Mentoring students on projects including LiDAR-guided monocular detection, world models, and multimodal benchmarks for transportation scenes. Trends in Research: Zhou's work bridges low-light image enhancement with spatial-frequency features, surface-aware frameworks for 3D detection, and weakly-supervised domain adaptation. He contributes to benchmarking spatio-temporal video understanding and evaluating autonomous driving datasets. Supervision and Collaboration: Co-authored key surveys and frameworks with Prof. Alois C. Knoll and peers, focusing on real-time roadside LiDARs, graph-based object relationships, and vision-language integration for traffic analysis.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۴ مورد دیگر

Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.

Mohammadhossein Malmirمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Robotic Manipulation
• Sim2Real Transfer
• +۳ مورد دیگر

Mohammadhossein Malmir is a Researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems at Technische Universität München (TUM). He joined the chair in November 2019 and currently contributes to the A-IQ Ready project (successor of AI4DI ), focusing on learning algorithms for industrial manipulators and mobile robots in manufacturing and intralogistics tasks. Education B.Sc. in Electrical Engineering (Control Systems) from Amirkabir University of Technology (Tehran Polytechnic) (2014) M.Sc. in Automation and Control Engineering from Politecnico di Milano (2018) His research addresses Sim2Real Transfer , Reinforcement Learning , Robust Control , and Model Predictive Control for autonomous robotic systems. He has published extensively on sim2real policy transfer, domain randomization, and control algorithms for manipulation and navigation tasks. Recent publications highlight his work in dexterous grasping , continual domain adaptation , and deep reinforcement learning for trajectory optimization . His advising roles include supervising 13 master's students and interdisciplinary projects, with co-advisorship on topics like vision-based robotic grasping and socially compliant path planning . He teaches courses on Cognitive Systems and Cloud-Based/Simulation-Based Machine Learning in Robotics , emphasizing reinforcement learning and sim2real methodologies.

• Computer Vision
• 3D Graphics
• Robotics
• +۴ مورد دیگر

Li Yi is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology, where they lead cutting-edge research at the intersection of computer vision, 3D graphics, and robotics. Their work focuses on advancing neural rendering, point cloud processing, and embodied AI with applications in human-object interaction and robotic manipulation. Research interests span Computer Vision , 3D Graphics , Robotics , Point Cloud Processing , Neural Rendering , and Human-Object Interaction . Recent work explores language-grounded spatial reasoning, dexterous manipulation, and 4D dynamic content generation, with publications appearing in top venues like CVPR, ICCV, and NeurIPS. Their research bridges theoretical advances with practical applications in embodied AI systems. The publication trends reveal a strong focus on neural rendering techniques (particularly NeRF variants), embodied AI for robotic manipulation , and multimodal understanding integrating vision, language, and action. Recent work increasingly incorporates large language models and focuses on generalizable approaches that transfer from simulation to real-world settings. As an advisor, Professor Li has mentored numerous students including Yunze Liu, Xueyi Liu, Zekun Qi, and Runpei Dong, who frequently appear as first authors on collaborative publications. Their research has been supported by significant grants enabling work on human-robot interaction, 3D scene understanding, and embodied AI systems. Professor Li leads a research group focused on developing comprehensive frameworks for spatial reasoning, object manipulation, and dynamic scene understanding. The team works on creating benchmarks like TACO for tool-action-object understanding and developing systems like MobileH2R for human-robot handover tasks. Current work emphasizes real-world applicability with a focus on generalizable solutions that work across diverse settings.

Stephan Pareigisمشاهده پروفایل

استاد

• Autonomous Systems
• Robotics
• Reinforcement Learning
• +۳ مورد دیگر

Prof. Dr. Stephan Pareigis is the Head of the Department of Computer Science at Hamburg University of Applied Sciences. He holds a professorship in Applied Mathematics and Technical Informatics. His career includes leadership roles such as Chair of the Examination Board for Computer Science (2005–2018) and founding director of the autosys research lab . His expertise spans autonomous systems, reinforcement learning, robotics, and embedded machine learning. He has extensive industry experience, including roles at Heidelberger Druckmaschinen AG and Siemens AG before joining academia in 2004. His research focuses on bridging simulation and real-world applications in autonomous systems, particularly in miniature platforms for reliable AI development. Education and Academic Journey: 1992: Diplom Mathematiker (LMU Munich, minor in Computer Science) 1998: Dr.rer.nat in Numerical Methods for Reinforcement Learning (CAU Kiel) Research Interests: Autonomous and Intelligent Systems Reinforcement Learning for robotics Embedded Machine Learning Miniature autonomy testing frameworks His work emphasizes real-world applicability through projects like Miniature Autonomy for scalable AI validation. Labs and Teams: Leads the autosys research lab , focusing on autonomous systems research and industry collaboration through the Forschungs- und Transferzentrum Smart Systems .

Daniel Büscherمشاهده پروفایل

پژوهشگر

• Autonomous Robot Navigation
• Deep Learning
• Computer Vision
• +۱ مورد دیگر

Daniel Büscher is a post-doctoral researcher at the Robot Learning Lab of the University of Freiburg. His work focuses on autonomous robot navigation , deep learning , and probabilistic state estimation . He has contributed to robotics research through projects involving mobile manipulation and audio-visual navigation . Research Interests : Autonomous navigation, deep learning, computer vision, probabilistic estimation Teaching : Lecturer and tutor for Introduction to Mobile Robotics and Robot Mapping (2017–2023) Publication Trends : Recent work emphasizes robot design optimization , language-grounded scene graphs , and uncertainty-aware perception . His research connects reinforcement learning with mobile manipulation and cross-domain navigation challenges.

• Medical Image Analysis
• Deep Learning in Healthcare
• Vascular Segmentation
• +۳ مورد دیگر

Baochang Zhang is an Assistant Professor in the Department of Informatics at the Technical University of Munich's School of Computation, Information and Technology. He is affiliated with the Chair of Computer Applications in Medicine (Prof. Navab) at the Garching campus, specializing in medical image analysis and AI-driven healthcare solutions. His research focuses on Medical Image Analysis with emphasis on vascular structures, including: Deep learning for low-dose CT denoising and X-ray angiography processing Multi-modal fusion techniques for cognitive impairment prediction Real-time surgical guidance systems for endovascular procedures Self-supervised learning frameworks for vessel segmentation Analysis of his 15 most recent publications (2019-2025) reveals a strong trajectory in solving clinical imaging challenges through innovative AI methods. His work consistently bridges computer vision and clinical applications , with increasing focus on zero-shot learning, domain adaptation, and surgical robotics integration since 2022. Key trends include replacing traditional segmentation with diffusion models and addressing missing data in multi-modal clinical datasets. No scientific awards were documented in the provided materials. While no formal advisees are listed in the scraped data, his lab appears to focus on translational medical AI projects with strong industry and clinical partnerships. The publications suggest active grant funding in EU medical technology initiatives, particularly for intraoperative imaging systems and neurodegenerative disease prediction tools. Zhang leads research within TUM's medical imaging group under Prof. Navab, likely contributing to the CAMP (Computer Aided Medical Procedures) Lab ecosystem. His team develops clinical decision support systems with emphasis on real-time vascular analysis during interventions.

• Autonomous Driving
• Multi-Object Tracking
• Sensor Calibration
• +۳ مورد دیگر

Leah Strand is a Ph.D. candidate and research assistant at the Technical University of Munich (TUM) in the Department of Robotics, Artificial Intelligence and Real-time Systems under Prof. Knoll. She joined TUM in March 2020 and contributes to projects like Providentia++ , which focuses on creating real-time digital twins of traffic on the Autobahn A9. Education: M.Sc. in Mechatronics and Information Technology, B.Sc. in Engineering Science (both from TUM) Research interests include: Autonomous Driving Multi-Object Tracking Sensor Calibration and Fusion Her recent publications emphasize image enhancement , multi-camera calibration , and traffic surveillance datasets . She actively participates in teaching and project advising, with expertise in real-time systems and infrastructure-based perception for autonomous vehicles. Contact: leah.strand@tum.de

• Remote Sensing
• Computer Vision
• Machine Learning
• +۶ مورد دیگر

Qian Cheng is a Professor at the National University of Defense Technology, specifically affiliated with the College of Electronic Science in Changsha, China. With an extensive publication record spanning from 2013 to 2025, Dr. Cheng has established a significant research presence across multiple interdisciplinary domains. Their work bridges theoretical computer science with practical applications in agriculture, medical imaging, and remote sensing technologies. Dr. Cheng's research primarily focuses on the intersection of machine learning, computer vision, and domain-specific applications. Their work demonstrates particular expertise in applying advanced computational techniques to solve real-world problems in precision agriculture, where they've developed innovative approaches for crop monitoring using UAV technology and spectral analysis. In medical imaging, Dr. Cheng has contributed to photoacoustic imaging techniques for bone characterization and ophthalmic disease diagnosis. The research portfolio also includes significant contributions to autonomous systems, including UAV path planning and autonomous driving technologies. Analysis of Dr. Cheng's recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of artificial intelligence, with particular emphasis on transfer learning approaches that can adapt models across different domains and conditions. The research consistently demonstrates practical implementation value, with numerous applications in agricultural monitoring, medical diagnostics, and autonomous vehicle technologies. This work often involves collaboration with large research teams across multiple institutions, reflecting the complex, multidisciplinary nature of the problems being addressed. Dr. Cheng actively mentors numerous researchers, with several students appearing as co-authors on publications. Their research group appears to focus on developing practical AI solutions that can be deployed in real-world settings, particularly in agricultural and medical contexts where precise monitoring and analysis are critical.

Erdal Kayacanمشاهده پروفایل

استاد

• Control Systems
• Robotics
• Computer Vision
• +۳ مورد دیگر

Prof. Dr. Erdal Kayacan serves as a Professor within the Department of Electrical Engineering and Information Technology at Paderborn University's Faculty of Computer Science, Electrical Engineering and Mathematics. His academic work centers on advancing control systems and robotics, particularly through nonlinear control theory and computer vision applications for autonomous systems such as underwater vehicles and wind turbine inspection robots. Research Interests: Kayacan's primary research areas include nonlinear control, robotics, and computer vision. He specializes in developing robust control algorithms for autonomous systems, with a focus on visual SLAM and sim2real transfer. His projects address challenges in underwater navigation, wind turbine inspection, and the creation of realistic simulation frameworks for robotics. Publication Trends: Analysis of his recent publications (2023-2024) reveals a strong integration of deep learning with traditional control methods. Key contributions include advancements in trajectory-aware object masking for SLAM, nonlinear model predictive control for visual tracking, and the generation of synthetic datasets for underwater robotics. These works consistently target real-world autonomous system applications. Teaching, Mentorship, and Projects: Kayacan teaches courses including System Theory and Nonlinear Control of Autonomous Systems. He supervises project seminars in robotic control and navigation. His current projects include the development of AI-based tools for sim2real transfer in autonomous robotics, indicating active grant-funded research. Research Group: As a member of the Automatic Control (RAT) group, Kayacan contributes to Paderborn University's research in control engineering. Current projects focus on AI-based sim2real transfer for autonomous robots.

Ostap Okhrinمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Transportation Systems
• +۴ مورد دیگر

Ostap Okhrin serves as a Professor of Econometrics and Statistics at Dresden University of Technology, holding the Chair of Econometrics and Statistics with a special emphasis on Transportation Systems. His academic career is marked by a strong focus on methodological advancements in econometrics and statistics, applied to complex real-world problems in transportation and finance. Professor Okhrin's research interests span econometrics, statistical theory, copula modeling, time series analysis, and financial risk management. He has significantly expanded into machine learning and reinforcement learning applications for autonomous systems, with deep expertise in traffic flow modeling, autonomous driving, maritime navigation, and financial volatility estimation. His work bridges theoretical statistics with practical engineering challenges, particularly in transportation systems and risk forecasting, addressing high-dimensional data and dynamic environments through innovative methodological frameworks. Analysis of Okhrin's recent publications (2024-2025) reveals a pronounced interdisciplinary trajectory integrating reinforcement learning with transportation engineering. Key themes include drone-based trajectory data collection for traffic monitoring, algorithms for autonomous ships on inland waterways, and Sim2Real transfer frameworks for autonomous driving. Concurrently, he advances financial econometrics through high-frequency risk forecasting models incorporating realized moments. This dual focus demonstrates his ability to transfer statistical innovations across domains while maintaining rigorous theoretical foundations in copula theory and time series analysis.