جست‌وجوی استاد و پژوهشگر

Stefan Leuteneggerمشاهده پروفایل

استادیار

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +۷ مورد دیگر

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

Andreas Bullingمشاهده پروفایل

استاد

• Human-Computer Interaction
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Andreas Bulling is a Professor at the Institute for Visualisation and Interactive Systems , University of Stuttgart, Germany. His research focuses on Human-Computer Interaction , Eye Tracking , and Computer Vision , with applications in Machine Learning , Virtual Reality , and Information Visualization . 2025 Publications: HOIGaze (Extended Reality), ChartQC (Data Visualization), HAIFAI (Human-AI Interaction), SummAct (Behavioral Summarization), Chartist (Chart Reading). 2024 Contributions: HumanEYEze (Multimodal AI), HOIMotion (3D Object Detection), MultiMediate'24 (Engagement Estimation), Unified Model of Saliency (Scanpath Prediction). His recent work explores gaze estimation , interactive behavior modeling , and privacy-preserving eye-tracking systems . Key subfields include Extended Reality , Neural Networks , and Behavioral Biometrics . While no explicit scientific awards are mentioned, his research has been widely cited (8,003 total citations) and downloaded (132,740 times). Andreas leads projects in Interactive Systems and collaborates with institutions such as Aalto University , KU Leuven , and National University of Singapore . His lab focuses on eye movement analysis , human motion forecasting , and task-driven input modeling .

Alois Christian Knollمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۶ مورد دیگر

Prof. Alois Christian Knoll is a full professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology. His academic career includes roles at Bielefeld University and leadership in major EU initiatives like the Human Brain Project and ECHORD++. He specializes in robotics, AI, and autonomous systems, with a focus on medical robotics, sensor-based systems, and neuromorphic engineering. Knoll has supervised over 100 doctoral theses and authored/co-authored over 1,000 publications. Education: Diploma in Electrical Engineering (University of Stuttgart, 1985); PhD in Computer Science (Technical University of Berlin, 1988); Habilitation (TU Berlin, 1993). He has been at TUM since 2001, leading the Robotics, AI, and Real-Time Systems department. Research interests span autonomous systems, neuro-IT integration, and traffic simulation. Key projects include fortiss (Bavarian State Institute for Computer Science) and TUM-CREATE (Singapore collaboration). Awards include IEEE Fellow, University of Tokyo Fellow, and the Carl-Ramsauer-Prize (1990). Current roles include editorships in robotics journals, leadership in EU flagship projects, and teaching across multiple programs. His work bridges computer science, neuroscience, and engineering, with applications in healthcare, automotive systems, and urban mobility.

• Computer Vision
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andreas Geiger is a Professor and Head of the Department of Computer Science at the University of Tübingen, Germany. He leads the Autonomous Vision Group (AVG) within CyberValley and is a core faculty member of the Tübingen AI Center. His roles also include PI in the ML in Science Excellence Cluster and the CRC Robust Vision, as well as ELLIS Fellow and coordinator of the ELLIS PhD program. He specializes in machine learning models for computer vision, robotics, and autonomous systems, with applications in self-driving cars, VR/AR, and scientific document analysis. Educational background: While not explicitly detailed, his positions imply a Ph.D. in Computer Science or related field. His work spans interdisciplinary collaborations with institutions like ETH Zürich, Microsoft, and the University of Bonn. Research focuses on 3D scene understanding, Gaussian splatting, generative models, and reliable autonomous systems. Notable contributions include the KITTI dataset and foundational work in neural radiance fields. Awards include the Sage 10-Year Impact Award (2024), ERC Starting Grant (2019), and IEEE PAMI Young Researcher Award (2018). Key projects include the Scholar Inbox paper recommender platform, ReSim (reliable world simulation), and advancements in 3D scene generation (e.g., UrbanCAD, PrITTI). His lab maintains a strong focus on open-source tools and datasets, such as the CARLA Route Generator. Grants and funding include support from Vector Stiftung (MINT innovation program) and EU initiatives like the ML in Science Cluster. His team collaborates internationally, with recent work presented at CVPR, SIGGRAPH, and NeurIPS.

• Machine Learning
• Reinforcement Learning
• Robotics
• +۳ مورد دیگر

Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.

Yuanbo Xiangliمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Deep Generative Modeling
• Photorealistic Scene Reconstruction
• +۳ مورد دیگر

Yuanbo Xiangli is a postdoctoral researcher at Cornell University , advised by Prof. Noah Snavely. Previously, he obtained his Ph.D. from the Multimedia Lab in the Department of Information Engineering at the Chinese University of Hong Kong (CUHK) , supervised by Prof. Dahua Lin. His research focuses on 3D computer vision and deep generative modeling for urban scene reconstruction. 3D scene reconstruction from sparse images Neural rendering and Gaussian splatting Deep generative modeling for urban environments Multi-source geospatial data processing City-scale modeling and synthetic datasets His recent work includes advanced NeRF extensions (BungeeNeRF, GridNeRF), Gaussian splatting enhancements (GSDF, Scaffold-GS), and urban scene datasets (MatrixCity, OmniCity). A pioneer in combining classical vision techniques with modern deep learning approaches. ICLR 2020 Spotlight Award Collaborates with leading researchers in photorealistic rendering, including Noah Snavely and Dahua Lin. Develops systems enabling efficient 3D reconstruction from diverse data sources like satellite imagery and street-level panoramas.

• Business Administration
• Operations Research
• Supply Chain Management
• +۶ مورد دیگر

Prof. Dr. Julia Rieck is a Full Professor of Business Administration at the University of Hildesheim , leading the Department of Business Administration and Operations Research within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. As Dean of the Faculty , she oversees academic programs, quality management, and research initiatives. Her roles include academic advising for the Business Information Systems (B.Sc./M.Sc.) programs and active participation in examination boards and quality committees. Education: PhD in Political Science (Dr. rer. pol.) with summa cum laude (2008), Habilitation at Clausthal University of Technology (2014), and studies in Business Mathematics (Diploma, University of Hamburg, 2003) and Mathematics (Georg-August-University Göttingen, 2000). Research: Focuses on Operations Research , Supply Chain Management , Project Planning , and Logistics . Her work integrates mathematical modeling , machine learning , and real-world applications , particularly in disaster response , dynamic transportation , and sustainable e-commerce . Projects: Leads third-party funded initiatives like "IT für die sorgende Gesellschaft" (AI in healthcare/social sectors) and contributes to the HULLS real-lab (AI in aging societies). Collaborates with regional companies (e.g., Youco, ADITUS) and institutions (HAWK, University of Hannover). Teaching: Emphasizes practical application through case studies, industry partnerships, and the IT-Speed Dating event for student-company connections. Her courses cover project resource planning , logistics , and digital transformation . Labs & Teams: Active in the Institute of Business Administration & Business Information Systems , contributing to the KET Kompetenzwerkstatt (entrepreneurship support) and interdisciplinary teams in AI and sustainability research.

• Process Mining
• Business Process Management
• Stochastic Processes
• +۴ مورد دیگر

Dr. Sander Leemans is a Professor at RWTH Aachen University leading the Business Process Management Foundations and Engineering research group. His work focuses on advancing process mining theory and practice with emphasis on stochastic modeling and conformance verification. Leemans' research centers on process mining, business process management, and stochastic process modeling. He investigates conformance checking techniques for probabilistic models, process discovery algorithms, and the integration of exogenous data into process analysis. His work bridges theoretical foundations with practical applications in healthcare, robotic process automation, and inter-organizational systems. Recent publications reveal a concentrated research trajectory in stochastic conformance checking, where Leemans develops methods for matching observed traces to stochastic process models using alignment techniques, entropy metrics, and partial-order reasoning. He also pioneers object-centric process mining frameworks and explores silent transitions in labeled Petri nets, significantly enhancing the precision and applicability of process mining in real-world scenarios. The Business Process Management Foundations and Engineering group under Leemans' leadership drives innovation in process mining through rigorous theoretical development and open-source tooling, maintaining RWTH Aachen's position at the forefront of business process intelligence research.

Yan Zhangمشاهده پروفایل

مدرس

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.

• Information Systems
• Databases
• Knowledge Graphs
• +۷ مورد دیگر

Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.

Gerhard Weberمشاهده پروفایل

استاد

• Accessible Computing
• Assistive Technologies
• Human-Computer Interaction
• +۴ مورد دیگر

Prof. Gerhard Weber holds the Chair in Human-Computer Interaction at Technische Universität Dresden, Germany. Previously, he served as Chair for Human-Centered Interfaces at Christian-Albrechts-Universität zu Kiel (2000–2007) and Professor for Operating Systems and Graphical User Interfaces at Harz University of Applied Sciences (1996–2000). His research focuses on accessible computing, assistive technologies, haptics, and multimodal interaction. Key projects include development of tactile charts (SVGPlott), robotic guidance systems (HapticRein), and indoor navigation solutions for visually impaired users. Current work explores voice interfaces for social robots, autism-inclusive technologies, and accessibility maturity models for higher education institutions. Over 70 publications span conferences like CHI, IEEE, and ACM, emphasizing practical applications in assistive tech. Education & Professional Journey: 2007–Present: Chair in Human-Computer Interaction, TU Dresden 2000–2007: Chair for Human-Centered Interfaces, Kiel University 1996–2000: Professor of Operating Systems and GUIs, Harz University Research Interests: Prof. Weber's work bridges theory and practice in accessibility, emphasizing tactile interfaces, inclusive design, and assistive robotics. Recent projects include: Mosaik : Enabling blind users to create and share graphics via audio-tactile tools Cloud4All : Personalized web accessibility solutions Range-IT : Real-time object detection for navigation aids Advising & Grants: Managed €3.2M in EU and national grants (2011–2020) Supervised 12+ graduate projects on assistive tech Labs & Teams: Leads TU Dresden's Human-Computer Interaction Lab, collaborating with industry partners like Siemens and rehabilitation centers to deploy assistive systems in real-world settings.

Weert Canzlerمشاهده پروفایل

پژوهشگر

• Mobility Research
• Traffic Research
• Energy Policies
• +۲ مورد دیگر

Dr. Weert Canzler is a senior researcher and head of the Research Group "Digital Mobility and Social Differentiation" at the Berlin Social Science Center (WZB), where he has been affiliated since 1994. His career spans leadership roles including Deputy Chair of WZB's Academic Council (2000-2007), Senior Researcher in Science Policy (2009-2019), and current Speaker of the Leibniz Research Alliance "Energiewende" since 2013. His work critically examines mobility transitions through social science lenses, integrating energy policy, technological innovation, and societal transformation. Dr. Canzler's academic credentials include: Diploma in Political Science (minor in Economics and Law) from the Free University of Berlin (1985) Ph.D. in Sociology from the Technical University of Berlin (1995) Habilitation in Social Science Based Mobility Research from the Technical University of Dresden (2015) His research centers on the societal dimensions of mobility systems, with particular focus on the Energiewende 's impact on transportation. He investigates how digitalization reshapes travel behavior, the cultural entrenchment of automobility, and policy frameworks for equitable mobility transitions. His approach bridges sociology, urban planning, and technology assessment, emphasizing that sustainable mobility requires reimagining social practices alongside infrastructure. Current work analyzes autonomous vehicle fleets, parking policy reforms, and the psychological dimensions of driving freedom in congested cities. Analysis of Dr. Canzler's 2022-2025 publications reveals intensifying focus on post-pandemic mobility patterns and multi-crisis contexts (climate, energy, geopolitical). His scholarship increasingly addresses implementation barriers for autonomous shuttles, the political economy of parking reform, and narrative-building for mobility transitions. A distinctive thread examines winners/losers dynamics in public space reallocation, with growing attention to educational outreach for future mobility literacy. His interdisciplinary methodology combines qualitative analysis with policy evaluation. While specific grant awards aren't documented in available sources, Dr. Canzler's leadership of major research initiatives implies significant funding oversight. As head of a WZB research group and Leibniz Alliance speaker, he directs collaborative projects involving multiple institutions. His extensive co-authorship patterns suggest active mentorship of junior researchers, though formal student advising isn't explicitly referenced in his public profile. Dr. Canzler co-founded WZB's mobility research group in 1998 with Andreas Knie and currently leads the "Digital Mobility and Social Differentiation" team. Through the Leibniz Research Alliance, he coordinates nationwide energy transition research across 18 institutes. His teams maintain strong policy linkages with German federal ministries and international bodies, frequently translating research into practical guidance for urban planners and transport authorities.

• Business Process Management
• Process Mining
• Process Analytics
• +۴ مورد دیگر

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.

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.

Armin Heinzlمشاهده پروفایل

استاد

• Information Systems
• Business Informatics
• Digital Innovation
• +۴ مورد دیگر

Prof. Dr. Armin Heinzl is a leading academic in the field of Information Systems and Business Informatics, holding the Chair of General Management and Information Systems at the University of Mannheim since 2002. His research focuses on digital innovation, healthcare IT, and platform ecosystems, with significant contributions to understanding AI integration in teams and decision-making processes. Chair of General Management and Information Systems, University of Mannheim (2002–present) Former Chair of Business Informatics, University of Bayreuth (1996–2002) His research spans: Digital Innovation (Platform Ecosystems, Agile Methodologies, Conversational Agents) IT Outsourcing and Cloud Computing Healthcare IT (Mobile Health Applications, AI-Driven Decision Automation) Recent work examines generative AI's role in team decision-making and stroke detection using 5G technology. He has received prestigious awards including the AIS Fellow Award and AISD First Award. Scientific recognitions include: AIS Fellow Award for contributions to Information Systems AISD First Award for novel research methods He supervises students in the Chair of General Management and Information Systems, with co-authored studies appearing in top journals like the Journal of Organizational Behavior . His lab supports innovative projects such as the 5G-powered "RettungsNetz-5G" for emergency stroke care.