جست‌وجوی استاد و پژوهشگر

Andreas Langeمشاهده پروفایل

استاد

• Public Economics
• Environmental Economics
• Behavioral Economics
• +۴ مورد دیگر

Prof. Dr. Andreas Lange is a Professor of Economics at the University of Hamburg's Faculty of Business, Economics and Social Sciences, leading the Chair of Public Economics. He joined in 2010 and held prior positions at the University of Maryland and the University of Gothenburg. His research focuses on public economics, environmental policy, and behavioral economics, with emphasis on climate policy, emissions trading, and policy under risk/uncertainty. He earned his PhD from the University of Heidelberg and holds adjunct roles at the University of Maryland and ZEW Mannheim. Education: PhD (Dr.rer.pol.) in Economics (2000, Heidelberg), Diplom in Mathematical Economics (1997, Bielefeld). Research highlights include theoretical/experimental studies on international environmental agreements and voluntary public goods provision. He serves as Co-Editor-in-Chief of the Journal of Environmental Economics and Management and leads projects like CLiCCS (Cluster of Excellence on Climate Change). Recent articles explore ambiguity attitudes, climate adaptation, and cross-cultural cooperation, reflecting his interdisciplinary approach. His work bridges laboratory experiments and real-world policy, addressing global challenges like climate change through economic frameworks. Editorial Roles: Co-Editor-in-Chief, Journal of Environmental Economics and Management Key Projects: CLiCCS (2019–present), NOSTACLIMATE (2018–2022) Affiliations: AREC, University of Maryland; ZEW Mannheim

Luciano Spinelloمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Robotics
• SLAM (Simultaneous Localization and Mapping)
• +۳ مورد دیگر

Luciano Spinello is a Research Fellow affiliated with the University of Freiburg's Department of Computer Science, working within the AIS Lab led by Prof. W. Burgard. Previously, he held roles at Amazon Research (Seattle), ETH Zurich (PhD under Prof. Roland Siegwart), and EPFL Lausanne as a research assistant. His research focuses on the intersection of computer vision and robotics, specializing in robot perception, SLAM, and autonomous systems. He has contributed to projects involving RGB-D data processing, terrain classification, and socially-aware navigation algorithms. Education: PhD in Computer Science from ETH Zurich (2009), Electrical Engineering degree from Rome, Italy. Academic activities include organizing workshops (RSS 2014, IROS 2012), serving on program committees for robotics conferences, and editorial roles (IROS associate editor). His work emphasizes multimodal sensing, object detection in 3D environments, and robust localization across dynamic conditions. Key technical contributions include methods for RGB-D fusion, adaptive domain adaptation, and large-scale place recognition. His research bridges theoretical advancements with practical applications in autonomous robotics, including navigation systems and human-robot interaction protocols.

• Computer Science
• Electrical Engineering
• Machine Learning
• +۳ مورد دیگر

Xue Jiang is a faculty member at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering, China. Their research focuses on interdisciplinary areas including machine learning, signal processing, biomedical engineering, and cybersecurity. With a prolific publication record, they specialize in neural networks, fault diagnosis systems, federated learning, and brain-computer interfaces. Key research interests include developing intelligent algorithms for fault detection in mechanical systems, advancing medical imaging techniques through deep learning, and enhancing privacy in distributed data frameworks. Their work spans applications in robotics, cybersecurity, and bioinformatics. Notable contributions include adversarial robustness studies for EEG-based systems and PML-based computational methods for wave propagation analysis. Publications highlight innovations across engineering and computer science, with a focus on practical implementations in real-world systems. Collaborations with institutions like the Hong Kong University of Science and Technology and Harbin Institute of Technology reflect their extensive academic network.

Torsten Mützeمشاهده پروفایل

استاد

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +۵ مورد دیگر

Torsten Mütze is a Professor at the Institute of Mathematics at the University of Kassel. Previously, he held positions as an Assistant Professor at the University of Warwick (2019–2024) and was affiliated with Charles University Prague's Department of Theoretical Computer Science and Mathematical Logic. His academic journey includes a postdoc under Martin Skutella at TU Berlin, research stays at Georgia Tech and ETH Zurich, and a software engineering role at Supercomputing Systems Zurich. Education: PhD in 2011 from ETH Zurich under Angelika Steger's supervision Master's and Bachelor's degrees in related fields Research Interests: Focuses on discrete mathematics and theoretical computer science, including combinatorial algorithms, graph theory, computational geometry, order theory, Ramsey theory, and combinatorial games. His work bridges foundational theory and real-world applications, particularly in algorithm design and combinatorial generation. Advising & Grants: Supervised students: Francesco Verciani, Nastaran Behrooznia, Namrata, Arturo Merino, Frieder Smolny, Karl Däubel, Jerri Nummenpalo, and Ondřej Mička Funded projects: DFG Heisenberg grant 522790373, Chancellor's International Scholarships, and EU funding Labs & Collaborations: Organizes workshops like 'Combinatorics, Algorithms and Geometry' and contributes to research networks such as the 'Combinatorial Optimization and Graph Algorithms' group. His work involves collaborations with institutions globally, including ETH Zurich, Georgia Tech, and TU Berlin.

Edzard Höfigمشاهده پروفایل

استاد

• Internet of Things (IoT)
• Sensor Networks
• Sensor Fusion
• +۷ مورد دیگر

Prof. Edzard Höfig is a Professor in the Department of Computer Science and Media at Berlin School of Economics and Management. He holds a Dr.-Ing. (PhD) from TU Berlin (2011) and a diploma in computer science from TU Berlin (2003). His research focuses on IoT, sensor networks, data lineage, and trustworthiness of open data. He has organized and spoken at international conferences such as the International Open Data Dialog (2012) and presented at venues like Universitat Pompeu Fabra (2013). His work spans autonomic systems, network management, and metadata quality assessment. Höfig advises students on topics including generative design and sensor fusion. He is also involved in curriculum development, including courses like 'Seminar on OO-Design & UML.' Education: TU Berlin (PhD 2011, Diploma 2003) Key Research Areas: IoT, Open Data Trustworthiness, Autonomic Systems Key Contributions: Metadata Quality Frameworks, Network Behavior Modeling His recent publications address challenges in data visualization, network traffic analysis, and open government data quality. He actively participates in interdisciplinary initiatives like Berlin's Open Data Strategy, exploring legal and technical aspects of open data implementation.

Jörn Heesمشاهده پروفایل

استاد

• Data Science
• Deep Learning
• Anomaly Detection
• +۳ مورد دیگر

Prof. Jörn Hees is a Professor for Data Science at the Department of Computer Science, Hochschule Bonn-Rhein-Sieg (H-BRS), and serves as Chairman of the Computer Science Faculty Council. He is affiliated with the Institute for Artificial Intelligence and Autonomous Systems (A2S) and the Institute of Technology, Resource and Energy-efficient Engineering (TREE). His research focuses on Data & Graph Mining, Deep Learning, Anomaly Detection, Explainable AI, and AI-assisted education (e.g., automated grading systems). He teaches courses such as Stochastics, Data Analysis and Visualization, Artificial Intelligence (Bachelor level), and Deep Learning Foundations, Natural Language Processing (Master level). His work bridges theoretical advancements with practical applications in finance, remote sensing, and education. Notable projects include federated outlier detection systems (Fin-Fed-OD), the TreeSatAI benchmark dataset for tree species classification, and AI-driven grading solutions using LLMs. His publications emphasize anomaly detection techniques, multimodal fusion, and interpretable machine learning. Despite no explicit awards listed, his contributions to AI education and computational methods are evident through his extensive publication record. He coordinates research teams and advises on infrastructure for AI education and infrastructure monitoring via Earth observation data.

• Computer Vision
• Robotics
• 3D Reconstruction
• +۳ مورد دیگر

Luis Puig is an academic researcher specializing in computer vision and robotics, focusing on omnidirectional imaging systems, 3D reconstruction, and sensor calibration. His work bridges theoretical advancements with practical applications in navigation assistance for visually impaired individuals, intelligent tutoring systems, and multi-camera system integration. Key contributions include: Pioneering research in omnidirectional vision systems, including calibration techniques and feature extraction. Development of algorithms for 3D tracking, deformable surface modeling, and RGB-D sensor fusion. Advances in visual SLAM (Simultaneous Localization and Mapping) and stereo visual odometry. His interdisciplinary approach also extends to educational technology, designing adaptive learning systems for mathematical problem-solving. Over 23 publications in top venues like IEEE Trans. PAMI, CVIU, and ICRA reflect his expertise in both technical and applied domains.

Lukas Kiesslingمشاهده پروفایل

پژوهشگر ارشد

• Applied Microeconomics
• Behavioral Economics
• Peer Effects
• +۳ مورد دیگر

Lukas Kiessling holds the rank of Senior Research Fellow (Post-Doc) at the Max Planck Institute for Research on Collective Goods, affiliated with the Center for Social and Economic Behavior (C-SEB) at the University of Cologne. He is currently a Senior Data Science Consultant at Comma Soft AG since 2021. He earned his Ph.D. from the University of Bonn's Bonn Graduate School of Economics in 2020. His research focuses on applied microeconomics and behavioral economics, with emphasis on topics such as peer effects, mental health outcomes, parenting styles, and gender wage gaps. Key areas include the long-term impacts of peer environments on mental health and socioeconomic trajectories, the role of self-selected peers in performance, and parental decision-making processes. Notable work includes studies on how peer dynamics influence student mental health over decades, the economic consequences of self-selected peer groups in schools, and gender disparities in wage expectations among students. His empirical methods include field experiments, large-scale surveys, and administrative data analysis. Lukas has published in journals such as The Economic Journal , Management Science , and European Economic Review . His research addresses policy-relevant questions regarding education systems, labor markets, and family economics.

• Computer Vision
• Control Theory
• Biomedical Image Analysis
• +۵ مورد دیگر

Zhen Tang is an active researcher with a prolific publication record spanning from 2007 to 2025, primarily in computer science and engineering disciplines. Their work appears consistently in high-impact venues including IEEE Access, IEEE Transactions, and major conferences in computer vision and systems engineering. Research interests span computer vision, control theory, biomedical image analysis, machine learning, and multi-agent systems. Tang's work demonstrates a strong interdisciplinary approach, connecting theoretical control systems with practical applications in medical imaging, distributed computing, and emerging technologies like DNA computing. Recent publications show a growing interest in large language models, blockchain applications, and ethical considerations in technology design. The publication trends reveal an evolution from foundational work in image processing and pattern recognition (2010-2015) to more complex systems involving multi-agent control and deep learning (2016-2020), and most recently expanding into large language models, blockchain healthcare applications, and technology ethics. The research shows strong connections between theoretical control systems and practical applications across multiple domains. Zhen Tang has established long-term collaborations with researchers including Yanli Wan, Zhenjiang Miao, Wei Wang, and others, suggesting stable research group affiliations. The consistent publication output across 18 years indicates an established academic career with significant contributions to multiple subfields within computer science and engineering.

• Recommendation Systems
• Machine Learning
• Medical Informatics
• +۴ مورد دیگر

Zhaopeng Qiu is an active researcher in computer science with a strong publication record spanning from 2012 to 2025. His work primarily focuses on recommendation systems, machine learning, and their applications in healthcare and online services. He frequently collaborates with researchers including Xian Wu, Zhi Zheng, Hengshu Zhu, and Hui Xiong on projects related to large language models, medical informatics, and job recommendation systems. Dr. Qiu's research interests include Recommendation Systems, Machine Learning, Medical Informatics, Natural Language Processing, Artificial Intelligence, Data Mining, and Healthcare AI. His work demonstrates a clear evolution from earlier research in mobile robotics (2012-2015) to current cutting-edge work applying large language models to recommendation problems across various domains. Analysis of his recent publications shows a strong trend toward leveraging large language models for recommendation tasks, with significant contributions in medication recommendation, job matching, and fairness-aware systems. His work bridges theoretical AI advancements with practical applications, particularly in healthcare contexts where AI can have significant real-world impact. While specific scientific awards aren't documented in the available publications, his work appears in top-tier venues including WWW, AAAI, KDD, and IEEE/ACM transactions journals, indicating recognition within the research community. Dr. Qiu's research has practical implications for online platforms, healthcare systems, and labor market technologies. His recent focus on large language models for recommendation suggests he's at the forefront of integrating emerging AI capabilities with traditional recommendation paradigms.

• Computer Vision
• Machine Learning
• Pattern Recognition
• +۳ مورد دیگر

Larry Davis is a Professor affiliated with the University of Maryland, College Park. His research focuses on advanced topics in Computer Vision, Machine Learning, and Pattern Recognition, with a particular emphasis on video analysis, deep learning architectures, and applications in surveillance systems. His work spans multiple domains including adversarial machine learning, efficient video recognition frameworks, and object detection techniques. Key research areas include: Development of scalable video analysis models Advances in deep learning for computer vision tasks Optimization of neural network architectures Multi-camera person re-identification systems His publications reflect contributions to both foundational methods (e.g., truncated Cauchy matrix factorization) and applied systems (e.g., adversarial attack mitigation on video transformers). Collaborations with researchers like Zuxuan Wu and Ramani Duraiswami highlight his interdisciplinary approach to solving complex visual recognition challenges.

• Machine Learning
• Signal Processing
• Computer Vision
• +۴ مورد دیگر

Yuan Liao is an active researcher with a focus on interdisciplinary areas spanning computer science, electrical engineering, and applied mathematics. His work emphasizes innovative solutions in signal processing, machine learning, robotics, and human mobility studies. Notably, he has contributed to advancements in wearable biomedical devices, remote sensing image analysis, and next-generation wireless communication systems like 6G. Collaborations with institutions like Academia Sinica and co-authors such as Vasilis Friderikos highlight his global academic network. Liao's research often bridges theoretical frameworks with practical applications, addressing challenges in healthcare, transportation, and urban planning. Key contributions include developing novel neural network architectures for tasks like image inpainting and emotion recognition from EEG signals. His work on robotic aerial base stations for mmWave backhauling demonstrates expertise in cutting-edge wireless systems. While specific institutional affiliations are not explicitly stated in the provided data, his prolific publication record across top-tier journals and conferences indicates an established academic role, likely as a researcher or faculty member in a technical discipline.

• Computer Vision
• Machine Learning
• 3D Object Recognition
• +۴ مورد دیگر

Riccardo Spezialetti is a researcher at the University of Bologna's Department of Computer Science and Engineering, specializing in advanced 3D vision and deep learning. He earned his PhD in 2020 with a thesis titled 'Learning to understand the world in 3D,' focusing on geometric deep learning and 3D object representation. His work bridges computer vision, machine learning, and neural representations, with notable contributions to unsupervised domain adaptation, LiDAR processing, and neural field-based 3D reconstruction. Key research interests include equivariant descriptors, implicit neural representations, and self-supervised learning for 3D data. He collaborates frequently with Samuele Salti and Luigi Di Stefano, co-authoring over 25 publications in top venues like CVPR, ICCV, and IEEE PAMI. His research has practical applications in autonomous systems, robotics, and photorealistic 3D reconstruction.

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

Wei Jia is a Professor at the School of Computer and Information, Hefei University of Technology, China. Their research focuses on artificial intelligence, machine learning, computer vision, and robotics, with contributions to knowledge graphs, biometric systems, and autonomous systems. They have co-authored over 130+ publications in top-tier journals and conferences, including venues like IEEE Transactions, CVPR, and AAAI. Research interests span deep learning techniques, graph neural networks, and optimization for large-scale systems. Notable work includes entity extraction frameworks, safety analysis in engineering systems, and swarm control algorithms for unmanned vehicles. Contributions also extend to data management systems, such as the TierBase key-value store and the OVERLORD data loader for foundation models. Publications highlight interdisciplinary applications in cybersecurity, robotics, and biomedical imaging. Their work often bridges theoretical advancements with practical implementations, addressing challenges in both software and hardware systems. No specific awards or grants are listed in the provided text.

• Cross-Modal Retrieval
• Image-Text Matching
• Agricultural Robotics
• +۴ مورد دیگر

Mingyong Li is a Professor in the Department of Agricultural Engineering at Huazhong Agricultural University's College of Engineering, specializing in agricultural robotics, computer vision, and cross-modal retrieval systems. His research bridges agricultural engineering with advanced machine learning techniques, focusing particularly on rice transplanting automation and precision agriculture systems. His primary research interests include Cross-Modal Retrieval, Image-Text Matching, Agricultural Robotics, Computer Vision, Machine Learning, Semantic Analysis, and Emotion Recognition. Li's work demonstrates strong integration between theoretical machine learning advancements and practical agricultural applications, with particular emphasis on developing vision-based systems for crop monitoring and robotic manipulation in farming environments. Analysis of his recent publications (2023-2025) reveals a strong trend toward multi-modal learning systems that integrate visual perception with textual understanding, particularly applied to agricultural robotics. His work shows increasing sophistication in handling ambiguity in image-text matching while maintaining practical applications in agricultural machinery automation. The research spans both theoretical machine learning advancements and their concrete implementations in agricultural equipment. Professor Li has mentored several researchers who have become frequent collaborators, including Junyu Chen, Yewen Li, and Mingyuan Ge. His work demonstrates consistent funding support through numerous collaborative projects focused on agricultural automation and intelligent systems. His laboratory appears to focus on agricultural robotics systems, with particular emphasis on seedling transplantation machinery, rice farming automation, and sensor-based monitoring systems for crop quality assessment. The research integrates mechanical engineering, computer vision, and machine learning to create practical agricultural solutions.