جست‌وجوی استاد و پژوهشگر

Alexander Reitererمشاهده پروفایل

استاد

• Monitoring of Large-Scale Structures
• Sensor and Data Fusion
• Infrastructure Sustainability
• +۳ مورد دیگر

Prof. Dr. Alexander Reiterer is a full Professor at the Chair for Monitoring of Large-Scale Structures at the Institute for Sustainable Systems Engineering (INATECH) , University of Freiburg, Germany. He also serves as Head of the Division 'Object and Shape Detection' at the Fraunhofer Institute for Physical Measurement Techniques IPM. His research focuses on multi-sensor systems, precision monitoring of artificial and natural large-scale structures, and leveraging machine learning for data interpretation to enable sustainable infrastructure operation. Education: Doctorate in Applied Geodesy (Vienna University of Technology, 2004) Research Highlights: Develops holistic calibration approaches for sensor systems, integrates data fusion techniques, and pioneers autonomous robotic monitoring systems (e.g., project Amy ). His work spans infrastructure domains like roads, railways, and ecosystems, funded by the DFG and German Federal Ministry of Digital and Transport. Applied innovations include explainable machine learning for civil engineering simulations ( MoCES ) and environmental studies ( SPENSER , STREEM ). Scientific Awards: Karl Rinner Prize (2006) Education Award (2009) Humboldt Fellowship (2010) IAG Fellow (2011) Fraunhofer Prize (2019) KI-Champion (2021) Contact: alexander.reiterer@inatech.uni-freiburg.de

Kai Oliver Arrasمشاهده پروفایل

استاد

• Artificial Intelligence
• Autonomous Systems
• Robotics
• +۱ مورد دیگر

Kai Oliver Arras is a Professor at the Institute for Artificial Intelligence (Institut für Künstliche Intelligenz) at the University of Stuttgart. His work is specifically associated with the Autonomous Systems research area within the Institute. Professor Arras's research focuses on Artificial Intelligence with particular emphasis on Autonomous Systems and Robotics. His work contributes to the Institute's mission of researching fundamental AI questions, reflecting on societal implications, and advancing the transfer of AI applications to industry and society. The Institute for Artificial Intelligence at the University of Stuttgart, where Professor Arras is based, was recently founded with key focus areas on 'AI for simulations' and 'Intelligent Robotics,' aligning with his research in autonomous systems. The University of Stuttgart actively engages in AI research collaborations with industry partners, as evidenced by joint projects such as the PhD position in machine learning for safety monitoring in autonomous driving with Mercedes-Benz AG. Professor Arras's work in Autonomous Systems likely intersects with these collaborative efforts that bridge academic research and practical applications in the automotive and robotics sectors.

• 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.

• Intelligent Transportation Systems
• Driver Behavior Analysis
• Machine Learning
• +۴ مورد دیگر

Xia Zhao is a Professor at Beijing University of Technology's College of Metropolitan Transportation, specializing in Intelligent Transportation Systems. With over 150 publications spanning from 2002 to 2025, Dr. Zhao has established a strong research profile in transportation engineering, machine learning, and human-machine interface technologies. The research program integrates computational methods with transportation applications, focusing particularly on driver behavior analysis, urban rail transit systems, and intelligent vehicle technologies. Dr. Zhao's research interests center on applying advanced computational techniques to transportation challenges. The work spans intelligent transportation systems, with emphasis on driver state monitoring, lane change prediction, and passenger flow forecasting in urban transit networks. A significant portion of the research applies deep learning and ensemble methods to analyze driver distraction, fatigue detection, and human-machine co-driving systems. The research program demonstrates strong interdisciplinary connections between computer science, transportation engineering, and human factors. The publication record shows a clear evolution from foundational work in neural networks and power systems toward increasingly sophisticated applications in transportation contexts. Recent work (2023-2025) demonstrates a strong focus on multi-modal data fusion, personalized modeling approaches, and safety-critical applications in transportation. The research shows consistent collaboration with Engang Tian and other Chinese institutions, with growing international collaboration in computer vision and healthcare applications of machine learning techniques. Dr. Zhao has supervised multiple graduate students working on transportation-related machine learning projects, with research supported by Chinese national and provincial funding agencies focused on intelligent transportation systems and urban infrastructure development. The work contributes significantly to Beijing University of Technology's reputation in metropolitan transportation research. The research laboratory maintains strong connections with transportation authorities and automotive industry partners, facilitating real-world validation of developed models through access to urban transit data and driving simulators. Current work focuses on integrating multi-sensor data for real-time driver state assessment and developing predictive models for urban mobility patterns.

• Vehicular Edge Computing
• Internet of Things Security
• Wireless Networks
• +۶ مورد دیگر

Liang Zhao is a prominent professor at Shenyang Aerospace University's School of Computer Science, with extensive research contributions in vehicular edge computing, IoT security, and wireless communications. Previously earning a PhD from Napier University's School of Computing in 2011, Zhao has established himself as a leading researcher in mobile computing through numerous high-impact publications in IEEE journals and conferences. Dr. Zhao's research focuses on cutting-edge challenges in vehicular networks, UAV communications, and edge computing systems. His work bridges theoretical advances in deep reinforcement learning with practical applications in transportation and IoT systems, particularly addressing task offloading optimization, resource allocation, and security challenges in mobile environments. Recent projects explore innovative solutions for LEO satellite constellations, RIS-assisted communications, and federated learning applications across distributed systems. Analysis of Zhao's recent publication trends reveals a strong emphasis on practical implementations of AI techniques for mobile computing challenges, with significant contributions to vehicular edge computing frameworks, secure IoT protocols, and energy-efficient UAV communications. His research consistently addresses real-world constraints like network limitations, harsh environments, and heterogeneous system requirements. Dr. Zhao maintains an exceptionally productive research trajectory with 246 publications spanning from 2009 to 2025, demonstrating sustained contributions to the field. His collaborative network is extensive, with particularly strong partnerships with Ammar Hawbani (113 co-authored papers), Na Lin (41 papers), and Xingfu Wang (40 papers), suggesting well-established research groups and international collaborations. While specific grant information isn't detailed in the publication record, the scale and scope of Zhao's research output across multiple institutions suggest significant research funding support for projects in mobile computing, vehicular networks, and edge computing systems.

Thomas Hamacherمشاهده پروفایل

استاد

• Urban Energy Systems
• Renewable Energy Integration
• Nuclear Fusion
• +۳ مورد دیگر

Prof. Thomas Hamacher is a Full Professor of Renewable and Sustainable Energy Systems at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. He leads the Munich Institute of Integrated Materials, Energy and Process Engineering (MEP) and previously served as Director of the Munich School of Engineering (MSE). His research focuses on urban energy systems, renewable energy integration, nuclear fusion, and energy modeling methodologies. He holds a PhD in Physics from the University of Hamburg and has extensive experience at the Max Planck Institute for Plasma Physics. Education: Studied Physics at Universities of Bonn, Aachen, and Columbia University (NY). Doctorate from University of Hamburg (1990s). Career highlights include leadership roles in energy research groups and academic administration. Research interests include fusion energy applications, decarbonization strategies, and systemic energy transitions. He is a member of the Environmental Science Center (WZU) at Augsburg University. Key research themes emphasize multi-sector energy systems, grid stability with renewables, and global energy policy. His work bridges technical innovation with socio-technical system analysis to address climate goals. Recent publications focus on digital twin applications, battery storage optimization, and hydrogen economy potentials. Active in international energy modeling collaborations like ETSAP TCP.

Markus Kowarschikمشاهده پروفایل

پژوهشگر

• Interventional Imaging
• Blood Flow Quantification
• Perfusion Imaging
• +۷ مورد دیگر

Dr. Markus Kowarschik is a researcher affiliated with the Chair of Computer Science Applications in Medicine at Technical University of Munich under Prof. Nassir Navab. His work focuses on interventional imaging, blood flow quantification, and tomographic reconstruction. He contributes to labs like DHM (Deutsches Herzzentrum München), NARVIS, and IFL, advancing medical imaging and AI applications in healthcare. His teaching includes courses on medical procedures, robotics, and deep learning in medical contexts. Recent research emphasizes AI-driven solutions for endovascular procedures, motion compensation in imaging, and 3D pose estimation. His research interests span medical image analysis, computer vision, and generative models applied to surgical data science. Key projects involve developing datasets for benchmarking, improving X-ray imaging systems, and integrating simulation for medical training. He has no listed awards but maintains an active publication record in top-tier journals and conferences. Labs and collaborations include the NARVIS Lab (navigation and robotics), DHM for cardiac imaging, and GenAI initiatives exploring generative models. His work bridges clinical needs with computational methods, addressing challenges in radiation dosimetry, robotic control systems, and real-time imaging analysis.

• Cloud-Native Network Functions (CNFs)
• Kubernetes Orchestration
• Telecom Cloud Infrastructure
• +۴ مورد دیگر

Razvan-Mihai Ursu is a Researcher at the Chair of Communication Networks (Lehrstuhl für Kommunikationsnetze) at the Technical University of Munich, led by Prof. Wolfgang Kellerer. His research focuses on cloud-native network functions (CNFs), Kubernetes-based orchestration, energy-efficient telecom cloud architectures, and network security in open RAN systems. He holds a Master’s degree in Electrical Engineering and Information Technology from TUM (2023) and has been actively involved in the Chair since June 2023. Key research areas include: Design and optimization of Kubernetes orchestration for telecom core networks Energy-efficient redundancy models for telco applications Simulation frameworks for multi-tier Kubernetes deployments (e.g., using Shadow simulator) Security vulnerabilities in O-RAN nearRT-RIC platforms Autonomous network adaptation via digital twins He supervises master’s theses on topics such as CNF orchestration tools evaluation, energy optimization in Kubernetes clusters, and security testing of FlexRIC platforms. His work aligns with projects like the 6G Future Lab Bavaria and the ERC Network Flexibility initiative. Current projects involve advancing simulations for Kubernetes-based systems and developing mathematical models to optimize telecom cloud redundancy for energy efficiency while maintaining service availability.

Christian Manteuffelمشاهده پروفایل

پژوهشگر

• Precision Livestock Farming
• Animal Behavior
• Animal Welfare Assessment
• +۴ مورد دیگر

Dr. Christian Manteuffel is a Researcher at the Leibniz Institute for Farm Animal Biology (FBN) in Dummerstorf, Germany, where he leads work in the Service Group Smart Livestock Farming. His research focuses on developing innovative approaches to animal management through precision livestock farming technologies, with particular emphasis on swine behavior and welfare. Manteuffel's research interests center around the intersection of animal behavior, technology, and welfare assessment. He has pioneered work in signal feeding systems for sows, developing automated conditioning procedures that allow pigs to actively participate in their feeding management. His research explores how animals can become 'actors' rather than 'extras' in farming systems, investigating animal-robot interactions and cooperative farming approaches. A significant portion of his work addresses critical welfare issues in pig production, particularly piglet crushing prevention through vocalization analysis and parturition detection using radar sensors. His research bridges fundamental animal behavior studies with practical applications for commercial farming operations. Analysis of Manteuffel's publication record from 2010-2023 reveals a consistent focus on precision livestock farming technologies applied to swine production. His work demonstrates an evolution from basic behavioral studies toward increasingly sophisticated technological implementations. Key thematic areas include automated conditioning systems, animal welfare assessment methodologies, and sensor-based monitoring approaches. His recent publications increasingly address the conceptual framework of 'cooperative livestock farming,' suggesting a paradigm shift from purely monitoring animals to developing systems where animals actively participate in their management. Manteuffel's research has practical implications for improving animal welfare while maintaining productivity in commercial pig farming operations. His work on signal feeding systems represents a novel approach to sow management that could transform traditional feeding practices. The integration of behavioral science with technological innovation characterizes his research approach, emphasizing both animal-centered perspectives and practical farming applications.

Stefan Leueمشاهده پروفایل

استاد

• Formal Methods
• Software and Systems Engineering
• Model Checking
• +۷ مورد دیگر

Stefan Leue is a Professor for Software and Systems Engineering at the Department of Computer and Information Science at the University of Konstanz since 2004. He serves as a member of the Extended Directorate of the Centre for Human | Data | Society (elected in 2022) and is part of the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour since 2018. His academic background includes a doctorate from the University of Bern (1995) and prior professorial positions at the University of Freiburg (2000-2004) and University of Waterloo (1995-2000). Leue's research focuses on formal methods in software engineering, particularly in the design and analysis of complex systems. His expertise spans embedded software systems, verification of AI-based software, automotive software engineering, causality analysis, and safety-critical systems. He leads several research projects including Neural Network Repair, Causality in Systems (QuantUM and CausCheck), SCADNet, TarTar, and DiRePro. His research group has produced significant work in model checking, directed search algorithms, real-time systems verification, and probabilistic system analysis. Current research trends emphasize the application of formal methods to AI safety, particularly in neural network verification and repair, as well as extending formal techniques to collective behavior modeling and safety-critical automotive systems. Steering Committee Member, SPIN Symposium on Model Checking of Software since 2007 Member, Cluster of Excellence Centre for the Advanced Study of Collective Behaviour since 2018 IEEE Computer Society member ACM member Gesellschaft für Informatik (German Informatics Society) Professor Leue has supervised numerous PhD and Master's students, many of whom now hold prominent positions in industry and academia. His research group maintains active collaborations with industry partners in automotive and safety-critical systems domains. Current work focuses on bridging formal methods with machine learning to address safety challenges in autonomous systems.

Mohammad Hasan Yeganegiمشاهده پروفایل

پژوهشگر

• Robotics
• Control Theory
• Machine Learning
• +۳ مورد دیگر

Mohammad Hasan Yeganegi serves as a Researcher at the Applied and Theoretical Aspects of Robot Intelligence (ATARI) Lab, a collaborative research initiative between the Technical University of Munich and the University of Trento. He is currently pursuing his Ph.D. in Materials, Mechatronics, and Systems Engineering at the University of Trento, focusing on advanced control methodologies for robotic systems. His academic background includes: B.Sc. in Mechanical Engineering from Bu-Ali Sina University (2016) M.Sc. in Mechanical Engineering from K. N. Toosi University of Technology (2019) Yeganegi's research sits at the intersection of optimal control and machine learning, with concentrated expertise in legged locomotion systems. His doctoral work specifically investigates generalization of learned control policies and integration of safety constraints through novel learning frameworks, aiming to develop robust autonomous robots capable of operating in unstructured real-world environments. This research bridges theoretical control algorithms with practical robotic applications. As an active member of the ATARI Lab under Prof. Majid Khadiv, he contributes to cutting-edge projects in robot intelligence while maintaining strong institutional ties with both the University of Trento and Technical University of Munich.

• Computational Linguistics
• Artificial Intelligence
• Text Generation
• +۶ مورد دیگر

Sujian Li is an active researcher in computational linguistics and natural language processing, with recent contributions to advanced large language model applications. Their work spans multiple critical areas including hierarchical memory frameworks for Wikipedia generation, self-refining entity grounding systems, and long-context embedding model extensions. Key Research Areas: Continual learning in NLP, multimodal reasoning, cross-lingual knowledge transfer, and factual consistency evaluation. Notable Methods: MOG framework for structured generation, ISR self-refinement scheme, LongAttn token-level analysis, and IPR step-level process refinement. Article Trends show a focus on improving LLM robustness through adversarial training, enhancing coherence via discourse-level graph modeling, and developing benchmarks like WIKIGENBENCH for real-world evaluation. Their research also addresses knowledge integration in biomedical multilingual models (KBioXLM) and mathematical parsing via tree-structured decoding. Collaborations include leading researchers like Yifan Song, Dawei Zhu, and Wenhao Wu across institutions and projects.

Gisa Ascherslebenمشاهده پروفایل

استاد

• Developmental Psychology
• Child Development
• Self-Regulated Learning
• +۴ مورد دیگر

Prof. Dr. Gisa Aschersleben is a faculty member at Saarland University, where she leads the Chair of Developmental Psychology. Her work focuses on child development and self-regulated learning, particularly during key educational transitions such as from primary to secondary school or from high school to university. She is actively involved in research and teaching, and her team conducts longitudinal studies to understand how children and adolescents develop cognitive and emotional competencies. Her research interests include: Developmental Psychology Child and Adolescent Development Self-Regulated and Autonomous Learning Educational Transitions Adaptive Self-Regulation Psychological Diagnostics in Educational Contexts The research conducted under her leadership, such as the Kinderprojekt and EduMove , aims to develop practical school-based programs that support students during critical transition phases. These projects are part of the DFG-funded graduate college FlexBaR (Flexibility and Balance as Characteristics of Adaptive Self-Regulation), highlighting her role in a nationally recognized research network. Prof. Aschersleben's team includes scientific staff such as M.Sc. Annika Paulus, indicating active supervision and mentorship. While no specific awards or publications are listed in the provided text, her leadership in funded research projects suggests a strong academic profile. She also oversees the Psychological Diagnostic Practice Office, linking research with practical applications. Her lab, based in Building A1.3, 2nd floor on the Saarland University campus, runs studies involving families and children, with participation facilitated through a public registration form. This reflects a community-engaged research approach.

Daniel Großmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Mobility Systems
• Computer Science
• +۱ مورد دیگر

Daniel Großmann is a Professor and Vice President for Research at Technische Hochschule Ingolstadt (THI). His academic focus is in Computer Science and Data Processing, with specialized involvement in the AImotion research group dedicated to AI-driven mobility solutions. He maintains an active research profile with publications spanning 2014–2025. Research Focus: His work integrates artificial intelligence with transportation systems, emphasizing data-driven approaches to mobility challenges. Key domains include: Machine learning applications for autonomous systems Data analytics in transportation networks AI infrastructure for smart cities Human-machine interaction in mobility contexts

Sabah Badri-Höherمشاهده پروفایل

استاد

• Digital Signal Processing
• Underwater Robotics
• Sonar and Image Processing
• +۴ مورد دیگر

Sabah Badri-Höher is a Professor at the Kiel University of Applied Sciences (UAS Kiel) in the Department of Digital Signal Processing. She leads the AUV team 'TomKyle' in collaboration with GEOMAR Helmholtz Centre for Ocean Research Kiel and oversees projects like MAUS (autonomous underwater vehicles) and UFOTriNet (automated fish detection). Research Focus: Her work spans digital signal processing, underwater robotics, sonar data interpretation, acoustic communication, and computer vision. Key applications include AUV navigation, fish tracking, and underwater sensor networks. Publications: Recent articles emphasize sonar-based fish detection, AUV collision avoidance, cooperative swarm navigation, and signal processing for underwater environments. Trends include machine learning integration (e.g., Kalman filters, deep learning) and multi-sensor fusion. Student Advising & Projects: She supervises doctoral candidates in signal processing, computer vision, and underwater systems. Major grants include: MAUS: Development of interconnected AUVs for coordinated missions. UFOTriNet: Automated fish population monitoring using low-light cameras/sonar. Labs & Teams: Heads the DSV research group, collaborating with GEOMAR on student AUV teams. The lab focuses on underwater robotics, sensor algorithms, and participates in competitions like SAUC-E.