جستجوی استاد راهنمای دانشگاهی

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems and Director of the Institute for Intelligent Systems at Esslingen University of Applied Sciences, Germany, within the Department of Computer Science and Engineering. His leadership role positions him at the forefront of intelligent systems research in applied academic settings. His research spans autonomous systems, computer vision, and robotics with emphasis on visual-inertial SLAM, semantic segmentation, and collective perception. Key contributions address real-world challenges in unstructured environments like agricultural fields and urban settings through efficient perception systems. Recent work focuses on lightweight monocular solutions, sensor fusion techniques, and computational efficiency optimization for autonomous vehicles. Analysis of his 2024-2025 publications reveals strong trends in collective perception infrastructure, NeRF/Gaussian Splatting integration for SLAM, and multi-sensor dataset development. His research consistently benchmarks computational costs against accuracy improvements while creating valuable resources like the OPNV public transportation dataset and Rover multi-season SLAM corpus. As Director of the Institute for Intelligent Systems, Enzweiler leads initiatives advancing autonomous driving technologies through practical implementations and industry-relevant research frameworks.

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

Andreas Dengel is a Professor of Computer Science at the Rhineland-Palatinate University of Technology (RPTU) and Managing Director of the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Smart Data & Knowledge Services research area and the DFKI Deep Learning Competence Center, with additional professorship rights at Osaka Metropolitan University since 2009. He earned his doctorate in Computer Science from the University of Stuttgart in 1989 following undergraduate studies in Computer Science with Economics at RPTU (then TU Kaiserslautern). His academic promotions progressed from C3 to W3 Professor at RPTU between 1993-2013. Professor Dengel's research centers on Machine Learning and Pattern Recognition with applications in Earth Observation, document analysis, and semantic technologies. His work bridges theoretical AI with industrial implementation, notably through NVIDIA-certified deep learning frameworks and multi-institutional Earth Observation projects. He pioneered quantified learning approaches for satellite data interpretation and human behavior modeling. Recent publications (2025) demonstrate his focus on diffusion models for geospatial data, multi-view learning for missing Earth Observation data, and gaze-based confidence detection systems. These works highlight cross-disciplinary integration of deep learning with environmental science and human-computer interaction. His extensive honors include: ICDAR Outstanding Achievement Award (2019) Order of the Rising Sun with Gold Rays (2021) Order of Merit of Rhineland-Palatinate (2022) IAPR Fellow distinction NVIDIA Pioneer Award He has secured over €100 million in third-party funding and supervised 500+ theses. His MIND graduate school supports 20 industry-sponsored students, while international exchange programs with Japanese institutions foster cross-border research. As FFPA Chairman and acatech member, he shapes national AI policy including Germany's "Lernende Systeme" platform. His leadership spans DFKI's Deep Learning Competence Center (NVIDIA Excellence Program awardee) and research groups developing Robust Machine Learning for defense systems, Ageing Smart environments, and Earth Digital Twin technologies through EU and national projects.

• Machine Learning
• Deep Learning
• Pattern Recognition
• +۶ مورد دیگر

Dr. Annika Weiser is a Scientific Associate at the Institute for Technology Assessment and Systems Analysis (ITAS) at Karlsruhe Institute of Technology, where she serves as Scientific Coordinator of the Research Group 'Designing Real-World Laboratory Research' since 2023. Her work focuses on transdisciplinary sustainability research and real-world laboratory methodologies, with particular emphasis on urban transformation processes and sustainable mobility systems. Her research interests center on real-world laboratory research, transdisciplinary sustainability approaches, and innovative teaching methods. She investigates how experimental research settings can facilitate sustainability transformations, particularly examining the political impacts of real-world laboratories in urban contexts and the role of socio-technical experiments in mobility transitions. Her work bridges theoretical frameworks with practical applications through transdisciplinary collaboration between researchers and societal actors. Dr. Weiser's recent publications reveal a strong focus on methodological innovation in sustainability research, particularly examining how real-world laboratories generate long-term impacts, how transdisciplinary research is shaped by contextual factors, and how digital innovations can support sustainability transformations. Her work spans multiple domains including urban planning, mobility systems, and sustainability education. Her professional journey demonstrates deep commitment to sustainability research methodology, with expertise spanning real-world laboratory design, transdisciplinary research coordination, and sustainability education innovation. Dr. Weiser actively contributes to multiple significant research projects including TRAMIGO (transformation paths for sustainable regional mobility), CulturalRoad (examining cultural factors for cooperative mobility), tdAcademy (transdisciplinary research training), SiNfo (integrative sustainability research), ADMoS-Future (autonomous driving in future mobility systems), and LinkLab (connecting real-world laboratory research with disciplinary approaches).

• Real-world laboratory research
• Transdisciplinary sustainability research
• Sustainability transformation
• +۴ مورد دیگر

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +۴ مورد دیگر

Nikola Milosevic is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , affiliated with the International Max Planck Research School NeuroCom and working within the Methods and Development Group Neural Data Science and Statistical Computing . His research spans interdisciplinary topics at the intersection of artificial intelligence, neuroscience, and computational modeling. His work focuses on active matter systems , reinforcement learning architectures , and neural data science methodologies . He has developed the ARC toolbox for artificial speech rhythmicity controls and contributed to explainable AI frameworks. Recent publications highlight his interests in embodied cognition, trust region optimization, and deep learning applications for biomedical imaging. Nikola's methodological expertise includes information processing , representation learning , and algorithmic safety in computational systems. His research combines theoretical approaches with practical implementations in Python and machine learning frameworks.

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Dr. Anas Abdelrazeq serves as Chief Engineer at the Chair of Intelligence in Quality Sensing , RWTH Aachen University. His work focuses on AI integration in manufacturing, machine vision technologies, and quality management innovation. Current research spans generative AI , reinforcement learning , and data quality optimization Key projects include AI-driven product development and MetaVision industrial metaverse studies Recent publications address: Smart measurement strategies through machine learning Generative AI for non-destructive testing Curiosity-driven AI for job shop scheduling Machine vision applications in SMEs He contributes to the Cluster of Excellence Internet of Production and leads initiatives in 3D metrology and AI in Manufacturing as part of RWTH AI Week. The chair actively engages in developing digital process platforms for future construction sites and industrial applications.

• Artificial Intelligence
• Machine Learning
• Industrial Machine Vision
• +۷ مورد دیگر

Stefan Hägele is a researcher at the Chair of Media Technology, Technical University of Munich, affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI). He completed his B.Sc. and M.Sc. in Electrical and Computer Engineering at TUM, graduating with distinction in 2022. B.Sc. in Electrical and Computer Engineering, TUM (2019) M.Sc. in Electrical and Computer Engineering, TUM (2022) His research focuses on signal processing applications in communications, radar, and image processing, combined with applied machine learning. Key areas include mmWave radar analysis, WiFi-based indoor positioning, privacy-preserving rehabilitation systems, and complex-valued neural networks. Recent publications highlight his work in radar-based object classification (using MIMO architectures), material recognition (SMCNet), visible light positioning (VLP-KAN), and skeleton estimation for rehabilitation (PoinTS). His contributions also appear in projects like 6G-Life, DFG Teleoperation over 5G, and CeTI (Tactile Internet).

• Signal Processing
• Communications Engineering
• Radar Signal Processing
• +۷ مورد دیگر

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +۵ مورد دیگر

Michael Eichelbeck is a PhD candidate at the Technical University of Munich , affiliated with the Cyber Physical Systems group under Prof. Matthias Althoff. He joined in October 2021 after completing his Master’s degree at Imperial College London with a thesis on non-cooperative decentralized optimization. Research Interests : Safe control for power systems Merging reinforcement learning with formal validation Optimal power flow optimization Wind power forecasting Electric vehicle charging behavior prediction Deep learning for residential load profiles Selected Publications explore formal verification of graph neural networks, safe reinforcement learning frameworks, and action masking techniques. His teaching contributions include courses on Machine Learning for Power Systems and Verification of Cyber-Physical Systems .

• Reinforcement Learning
• Power Systems
• Formal Verification
• +۴ مورد دیگر

Laure Zanna is the Joseph B. Keller and Herbert B. Keller Professor in Applied Mathematics at New York University, with joint appointments in the Department of Mathematics at the Courant Institute and the Center for Data Science. Her research bridges physical oceanography, climate physics, and machine learning. PhD (2009), Harvard University MSc (2003), Weizmann Institute of Science BSc (2001), Tel Aviv University Her work focuses on understanding the ocean's role in climate systems, combining numerical simulations , turbulence modeling , machine learning , and data-driven approaches to study processes like ocean heat uptake , carbon cycling , and sea level rise . She leads M²LInES, an international collaboration applying scientific machine learning to improve climate models. The 15 most recent publications highlight trends in deep learning for climate emulation , stochastic parameterization , and causal inference in spatiotemporal climate fields . Key areas include mesoscale eddy modeling , air-sea flux uncertainty , and multi-scale climate interactions . Nicholas P. Fofonoff Award (2020), American Meteorological Society Zanna’s leadership in M²LInES and her interdisciplinary approach to climate modeling emphasize the integration of data science and geophysical fluid dynamics . She collaborates with institutions like GFDL and WHOI, advancing methodologies for climate prediction and ocean reanalysis .

• Climate Dynamics
• Physical Oceanography
• Machine Learning
• +۴ مورد دیگر

Dr. Lavinia Patara is a leading researcher at the GEOMAR Helmholtz Centre for Ocean Research in Kiel, Germany, where she has worked since 2012. Her work focuses on Ocean Circulation and Climate Dynamics , particularly in the Southern Ocean, and its interplay with Marine Biogeochemistry . She employs high-resolution ocean models (e.g., ORION10 at 0.1° resolution) and advanced data analysis techniques to investigate how wind stress, buoyancy forcing, and mesoscale eddies regulate ocean ventilation, carbon uptake, and climate feedbacks. Education: Ph.D. in Environmental Sciences (2010), University of Bologna, Italy M.Sc. in Environmental Sciences (2005), University of Milano-Bicocca, Italy Research Highlights: Patara's work has revealed that recent Southern Ocean climate changes since the 1980s have increased interior ventilation through wind-buoyancy interactions. She demonstrated that ongoing Antarctic warming may strengthen the North Atlantic Meridional Overturning Circulation and showed how mesoscale eddy fields respond differently to climate variability across ocean basins. Current projects include a DFG-funded initiative on Southern Ocean carbon sinks and development of machine learning methods to detect carbon biomes via adaptive clustering. Advising and Grants: She has supervised numerous graduate students, including Sweety Mohanty (PhD), Helene Hollitzer (Master's), and Mariana Pacheco (Master's thesis on eddy properties). Her research has been funded by the German Research Foundation (DFG), the MarDATA graduate school, and Italian Ministry of Research projects.

• Southern Ocean Circulation
• Marine Biogeochemistry
• Climate Change
• +۵ مورد دیگر

Dr. Yves Bühler is a leading researcher at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) , heading the Alpine Remote Sensing group within the Alpine Environment and Natural Hazards unit. His work focuses on remote sensing applications for snow and avalanche research, rock-ice avalanches, and alpine mass movement monitoring using drones, SAR satellites, and machine learning. Education: PhD in Remote Sensing (University of Zurich, 2005-2009), High-School Teaching Certification (MAS SHE, 2003-2005). Key projects: Large-scale avalanche mapping in India, drone-based snow depth monitoring, and thermomechanical modeling for hazard indication. Research Trends : Recent publications highlight advancements in machine learning for avalanche segmentation , multi-sensor displacement monitoring , and climate change integration into hazard models . His work bridges geomechanics , remote sensing technology , and operational risk mitigation across Switzerland, Himalayas, and international collaborations. Scientific Contributions : He has received a Research Fellowship at the University of Otago and led leadership programs at FHNW. His team developed RAMMS::LSHIM for large-scale hazard mapping and pioneered low-cost lidar for avalanche monitoring.

• Remote Sensing
• Snow and Avalanche Research
• Alpine Mass Movements
• +۳ مورد دیگر

Prof. Dr. Katharina Kohls serves as a Professor at Ruhr University Bochum specializing in system security, with research spanning mobile network vulnerabilities (4G/5G), privacy/anonymity systems, and internet censorship detection. Her work addresses critical security flaws in cellular infrastructure and anonymity networks through empirical attack simulations and defensive techniques. Her primary research domains include: Mobile Network Security : Investigating protocol vulnerabilities in 4G/5G including fake base stations, bidding-down attacks, and voice call eavesdropping (ReVoLTE/IMP4GT) Privacy & Anonymity : Analyzing traffic on Tor, location leaks in messaging apps, and information leakage in mobile VPNs Internet Measurement : Leading censorship monitoring initiatives like Censored Planet for longitudinal internet freedom analysis Analysis of her 2019-2024 publications reveals escalating focus on 5G security challenges and machine learning applications for intrusion detection, with consistent contributions to identifying practical attack vectors against real-world communication systems. Her methodology combines large-scale simulations with field measurements to expose systemic vulnerabilities. No scientific awards or student advising information was documented in the provided materials. Her contact details (room MC 4.137, tel +49 (0)234 32-27814) confirm active faculty status without indication of specialized labs or research teams in the source data.

• System Security
• Mobile Network Security
• Privacy and Anonymity
• +۳ مورد دیگر

Dr. Mahsa Taheri is a Scientific Associate (faculty-level researcher) in the Department of Mathematics at the University of Hamburg , embedded within the Faculty of Mathematics, Computer Science and Natural Sciences. She belongs to the research section Mathematical Statistics and Stochastic Processes and focuses on the emerging area Mathematics of data-based methods . Research Interests: Her work sits at the intersection of mathematical statistics, stochastic processes, and machine-learning theory . She develops non-asymptotic guarantees for modern learning algorithms, studies regularization and sparsity in deep neural networks, and quantifies statistical-computational trade-offs in high-dimensional regimes. Publication Trends: Across 13 peer-reviewed contributions (2016-2025) she consistently addresses the theoretical foundations of learning algorithms, with a marked emphasis on sample complexity, regularization, sparsity, and efficiency in neural networks and high-dimensional regression. The 2025 preprints extend this agenda to diffusion models and tail-index estimation. Scientific Awards: None explicitly reported in the available material. Advising & Grants: No advisee list or grant information was provided in the source text. Labs & Teams: She is part of the Mathematics of data-based methods research team within the Mathematical Statistics and Stochastic Processes unit; no dedicated laboratory name is mentioned.

• Mathematical Statistics
• Stochastic Processes
• Machine Learning Theory
• +۴ مورد دیگر

Marcus Otto serves as a Researcher and deputy head of the "Knowledge in Transition" department at the Georg Eckert Institute, a leading independent research institution specializing in international textbook studies. His scholarly work bridges historical analysis with contemporary educational challenges, focusing on how textbooks reflect and shape societal understandings of historical events, national identities, and cultural transformations. Dr. Otto earned his doctorate with an interdisciplinary thesis on the genealogy of political inclusion in France following master's studies in history, sociology, and modern history at Bielefeld University. His academic journey includes teaching European history at Bielefeld University (2001-2008), participation in the Collaborative Research Center "The Political as a Space of Communication in History," and membership in international research groups examining Islamic culture and modern society. His research program centers on the representation of European identity formation, particularly through the lens of crisis semantics in 20th-century textbooks. He investigates how educational materials construct narratives around decolonization, migration, religious pluralism, and societal transformation. His methodological approach combines discourse analysis, historical research, and postcolonial theory to unpack how textbooks mediate complex historical processes for students. Analysis of his extensive publication record reveals consistent engagement with questions of representation and power in educational media. His work demonstrates increasing focus on contemporary challenges including refugee integration, queer diversity, and the evolving role of textbooks in digital learning environments, while maintaining deep engagement with historical case studies of France and Germany. Dr. Otto has led significant research projects including the DFG-funded "Decolonization and Memory Politics. Textbooks in the Context of Social Conflicts in France (1962-2009)" and currently serves as Deputy Project Manager for "Queer Diversity and Heteronormativity in Schoolbooks" (2023-2026). His editorial leadership includes co-founding the journal "Body Politics – Zeitschrift für Körpergeschichte" (2013) and editing special issues on postcolonial memory politics in educational media. As deputy head of the "Knowledge in Transition" department, Otto contributes to advancing international dialogue about educational media while maintaining active scholarly engagement through conference presentations, workshops, and collaborative research initiatives examining how textbooks navigate contested historical narratives and contemporary social challenges.

• History of Education
• Textbook Research
• European Identity Studies
• +۵ مورد دیگر