جست‌وجوی استاد و پژوهشگر

John Hindmarchمشاهده پروفایل

پژوهشگر

• 3D Digitization
• Cultural Heritage Conservation
• Photogrammetry
• +۳ مورد دیگر

John Hindmarch is a Researcher at the University of Bamberg's Chair of Digital Heritage Technologies, specializing in 3D digitization for cultural heritage. He holds an Engineering Doctorate (EngD) from UCL Centre for Virtual Environments, Interaction and Visualisation (2016), an MRes in Virtual Environments (2011), and a philosophical foundation from Cambridge (1995). Formerly a lecturer at University College London (2011-2017), he has contributed to digital documentation projects at institutions like the Science Museum and Courtauld Institute. PhD: 3D Digitization for Cultural Heritage (UCL, 2016) MRes: Virtual Environments, Imaging and Visualisation (UCL, 2011) MA: Electronic Communication and Publishing (UCL, 2010) BA: Philosophy (Cambridge, 1995) His research focuses on multimodal 3D imaging workflows, photogrammetry accuracy, and digital documentation of baroque architecture. Recent work explores digital auras of heritage objects and open data ontologies for cultural institutions. Hindmarch has led projects like VirScan3D and Corpus der barocken Deckenmalerei in Deutschland , integrating 3D technologies into educational frameworks. Key publications span 3D ceiling painting analysis, sensing tool selection for monuments, and digital pedagogy in Germany's unique M.Sc. program in Digital Heritage Technologies. He collaborates with Mona Hess and Matteo Burioni on spatial documentation systems and historical reconstruction.

Alexander Verlمشاهده پروفایل

استاد

• Industrial Robotics
• Control Engineering
• Mechatronics
• +۵ مورد دیگر

Prof. Dr.-Ing. Alexander Verl is a leading academic at the University of Stuttgart , serving as Principal Investigator at the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) . His work bridges control engineering , industrial robotics , and digital twin technologies to enhance precision in manufacturing systems. Research Interests : Improving positioning accuracy of industrial robots through transmission error modeling and compliance compensation. Developing adaptive preload control mechanisms for cable-driven parallel robots and rack-and-pinion systems. Advancing IT/OT convergence via Time-Sensitive Networking (TSN) and cloud-edge integration. Creating digital twin platforms for real-time simulation and quality monitoring in CNC machining. Exploring deep learning applications for perception of deformable linear objects in automation. Recent Work Trends show expertise in: smart manufacturing , Industry 4.0 , and data-driven control systems . Publications emphasize practical validation through industrial testbeds (e.g., KUKA KR210–2 robotics, CNC machine simulations) and theoretical contributions to elastokinematic models and nonlinear dynamics . Advising & Grants : Collaborates extensively with researchers like Armin Lechler and Michael Neubauer. Projects funded through academic-industry partnerships in automotive production, precision engineering, and Gaia-X-based data ecosystems.

Laura Balangéمشاهده پروفایل

پژوهشگر

• Computational Design
• Quality Assurance in Construction
• Sustainable Architecture
• +۳ مورد دیگر

Laura Balangé is a doctoral researcher at the University of Stuttgart's Institute of Engineering Geodesy (IIGS), Faculty 6: Aerospace Engineering and Geodesy. She contributes to the Cluster of Excellence IntCDC through computational design and quality assurance research. Her work focuses on: Holistic Quality Modeling for sustainable construction Terrestrial Laser Scanning applications Coreless Filament Winding fabrication Modular Timber Shell structures Graded Concrete component monitoring Multi-Domain Co-Design frameworks Notable contributions include: 2025: Point cloud datasets for Biomimetic Shell construction 2024: Extended interrelation analysis in quality modeling 2023: Data normalization methods for co-design 2022: Production monitoring for fibrous structures 2021: Graded concrete component verification 2020: Foundational technical quality concepts She supervises academic works in: Geometric quality control Fiber cross-section analysis BIM modeling Neural network object detection Concrete positioning systems Timber fabrication quality Active in the IntCDC cluster, she collaborates with teams across architecture, engineering, and computational design domains.

Yao-Jen Changمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۳ مورد دیگر

Yao-Jen Chang is a researcher specializing in computer vision and machine learning, with significant contributions to 3D reconstruction, image registration, and biometric authentication systems. His work often involves collaboration with Tsuhan Chen at Cornell University and explores active learning frameworks, multi-view object recognition, and reinforcement learning applications. Key research areas: 3D modeling, multimedia security, and video processing Collaborations include National Tsing Hua University and Cornell University Research Trends Recent publications focus on: Deep reinforcement learning for image alignment User-interactive 3D reconstruction Biometric key generation for security Multi-view object recognition algorithms

• Computer Networking
• Bio-inspired Computing
• Network Management
• +۶ مورد دیگر

William Donnelly is a professor at Waterford Institute of Technology in Waterford, Ireland, with a distinguished research career spanning over three decades. His work demonstrates a clear evolution from traditional telecommunications and network management to bio-inspired computing approaches, and more recently to precision agriculture applications and computer graphics. His primary research interests focus on Computer Networking , Bio-inspired Computing , and Precision Agriculture . Early in his career, he specialized in telecommunications management networks (TMN) and service management. He then pioneered work applying biological concepts like chemotaxis and quorum sensing to networking problems, developing bio-inspired routing protocols and service management frameworks. In the 2010s, his research shifted toward precision agriculture applications, particularly dairy farming, where he applied wireless sensor networks, fog computing, and edge analytics to monitor animal behavior and optimize farming practices. Most recently, he has transitioned into computer graphics, focusing on real-time rendering techniques with publications in 2023-2024 on spatiotemporal sampling methods. Analysis of his publication trends reveals a researcher who consistently identifies emerging technological challenges and applies innovative cross-disciplinary approaches to solve them. His work demonstrates remarkable adaptability, moving from telecommunications standards to biological metaphors, then to agricultural technology applications, and finally to computer graphics - always maintaining a focus on optimization, efficiency, and practical implementation. Throughout his career, Donnelly has maintained strong collaborative relationships, particularly with Sasitharan Balasubramaniam and Dmitri Botvich, with whom he has co-authored numerous papers exploring bio-inspired networking approaches. His recent collaborations in computer graphics include Alan Wolfe, Judith Bütepage, and Jon Valdés.

• Wireless Communication
• 5G/6G Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Abhishek Roy is a researcher at Samsung Electronics, Suwon, South Korea , with a PhD in Software Department, Sungkyunkwan University (2010) . His work focuses on 5G/6G wireless networks , Internet of Things (IoT) , and machine learning-based network optimization . His research interests span beamforming , discontinuous reception (DRX) , device-to-device (D2D) communication , and network slicing , often integrating AI/ML for predictive analytics. Key contributions include optimizing NR-Unlicensed spectrum , enhancing V2X communication efficiency, and developing O-RAN frameworks for future networks. Recent publications analyze cross-frequency beam prediction (2024), GPS-based beam selection (2023), and predictive service automation in O-RAN (2022). His work intersects network resource management with smart grid integration and disaster connectivity .

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.

• Database Systems
• Data Management
• Recommender Systems
• +۴ مورد دیگر

Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.

Elizabeth von Hauffمشاهده پروفایل

استاد

• Materials Science
• Solid State Physics
• Biosensors
• +۵ مورد دیگر

Professor Elizabeth von Hauff holds the Chair of Coating Technologies for Electronics at the Institute for Solid State Electronics (IFE), Faculty of Electrical and Computer Engineering, Technical University of Dresden, appointed on June 1, 2021. She concurrently serves as Director of the Fraunhofer Institute for Electron Beam and Plasma Technology FEP in Dresden, leading dual institutional roles that bridge academic research and applied industrial development. Her research operates at the interdisciplinary nexus of physics, chemistry, and biology, with core expertise in biosensors, wearable electronics, and operando characterization of layers and interfaces. Von Hauff's work spans fundamental investigations of charge transport mechanisms in novel materials to practical applications in electron beam technology and plasma processing. Her recent publications demonstrate significant contributions to thin film deposition techniques, radiation dosimetry systems, and sustainable energy technologies. Analysis of her 81 Scopus-indexed publications reveals evolving research trajectories—from early work on organic photovoltaics and charge transport in polymer systems toward current leadership in electron beam applications, thermochromic coatings, and cardiovascular biomaterials. Her recent work shows increasing emphasis on sustainable technologies, including carbon neutrality planning for photovoltaics systems and wastewater treatment using electron beam technology. Von Hauff directs collaborative research between TU Dresden and Fraunhofer FEP, overseeing teams developing advanced coating technologies for electronics, medical applications, and environmental solutions. Her leadership encompasses both fundamental materials characterization and industrial-scale process development, particularly in roll-to-roll manufacturing and 3D-printed functional materials.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۳ مورد دیگر

Dr. Robert Haschke serves as a Professor and Responsible Investigator in the Cognitive Systems and Social Interaction Group at Bielefeld University's Faculty of Engineering. He is affiliated with the Center for Cognitive Interaction Technology (CITEC) and serves on the Examination Board for the Intelligent Interactive Systems Master's program. His office is located at CITEC 2-035, and he can be reached at rhaschke@techfak.uni-bielefeld.de or +49 521 106-12122. Professor Haschke's research spans multiple domains of robotics and artificial intelligence, with particular emphasis on tactile sensing systems, robotic manipulation, and human-robot interaction. His work explores advanced methods for enabling robots to perceive their environment through touch, with applications in assistive robotics and industrial automation. He investigates how machines can learn from human interactions and adapt their behavior through reinforcement learning and sensor fusion techniques. His research bridges theoretical advances with practical implementations, focusing on transferring knowledge from simulation to real-world robotic systems. Analysis of Professor Haschke's recent publications reveals a strong trajectory toward more sophisticated tactile perception systems and their integration with language and vision for natural human-robot collaboration. His work consistently addresses the simulation-to-reality gap, developing methods for transferring control policies from virtual environments to physical robots. The research shows increasing integration of multimodal sensing (tactile, visual, linguistic) to enable more capable and adaptable robotic manipulation in unstructured environments. As an educator, Professor Haschke teaches advanced courses including Robot Manipulators (39-Inf-RM), Advanced Artificial Intelligence (39-M-Inf-AI-adv_a), Advanced Artificial Intelligence (focus) (39-M-Inf-AI-adv-foc), and Basics of Artificial Intelligence (39-M-Inf-AI-bas). His teaching reflects his research expertise, providing students with both theoretical foundations and practical skills in robotics and AI. Professor Haschke is an integral member of Bielefeld University's Cognitive Systems and Social Interaction Group within CITEC. His work contributes significantly to the university's Socio-Technical World research area, particularly in developing capabilities that enable agents (humans, robots, and AI systems) to act, communicate, and learn in complex environments. His research group focuses on creating robotic systems that can interact naturally with humans through advanced perception and adaptive control mechanisms.

Holger Hesseمشاهده پروفایل

استاد

• Energy Storage Systems
• Smart Grids
• Battery Technology
• +۳ مورد دیگر

Prof. Dr. Holger Hesse serves as Professor and Head of the Institute of Energy and Drive Technology at Kempten University of Applied Sciences' Faculty of Mechanical Engineering, appointed to the Research Professorship in Smart Energy Systems on September 1, 2022. Previously, he was Deputy Head at the Chair of Electrical Energy Storage Technology at Technical University of Munich (TUM). His educational background includes: PhD in Physics on organic photovoltaics from LMU Munich and University of Wollongong, Australia Research stays at UC Santa Barbara and Cambridge University Hesse's research focuses on energy storage systems with emphasis on: Optimization of battery systems for grid services and EV charging infrastructure Modeling of battery degradation and aging-aware control strategies Carbon footprint analysis of storage applications Economic evaluation in evolving energy markets Machine learning for state estimation and lifetime prediction Analysis of his 2023-2025 publications reveals strong trends toward deep reinforcement learning for energy management, probabilistic aging prediction, and real-time optimization of heterogeneous storage systems. Key developments include thermal-aging integrated control, market-adaptive revenue stacking, and environmental impact quantification through frameworks like Energy System Network. As head of the Institute of Energy and Drive Technology, Hesse leads the Stationary Energy Storage Systems (SES) research group, advising graduate students and collaborating with industry partners on smart energy system development and sustainable mobility solutions.

• Battery Energy Storage Systems
• Power Control Optimization
• Battery Degradation Analysis
• +۲ مورد دیگر

Vivek Tanjavooru is a Research Fellow at the Institute of Energy and Drive Technology within the Allgäu Research Center at Kempten University of Applied Sciences. He specializes in energy systems, battery optimization, and thermal modeling, with a focus on industrial applications and grid technologies. His research spans battery energy storage systems, including power split control algorithms, degradation-aware dispatch optimization, and state-of-charge balancing with spatial thermal analysis. Recent work emphasizes real-time simulation feedback and heterogeneous battery systems, particularly for second-life applications. Key trends in his publications highlight integration of thermal-electric grid dynamics, additive manufacturing for automotive components, and predictive maintenance strategies. He collaborates on projects involving energy efficiency, sustainable systems, and industrial innovation.

Sven Buchholzمشاهده پروفایل

استاد

• Applied Computer Science
• Data Management
• Data Mining
• +۶ مورد دیگر

Prof. Dr.-Ing. Sven Buchholz serves as a Professor in the Department of Computer Science and Media at Brandenburg University of Technology in Brandenburg an der Havel, Germany. His office is located in Building C, Room C.2.18 at Magdeburger Straße 50, with contact information including telephone +49 3381 355-482 and email sven.buchholz@th-brandenburg.de. Specializing in Applied Computer Science , Prof. Buchholz focuses on data management and data mining with a distinctive research trajectory in geometric algebra applications. His scholarly work demonstrates expertise across multiple domains including neural network architectures using Clifford algebra, solving complex partial differential equations, protein structure prediction, and computer vision systems. The evolution of his research shows progression from foundational theoretical work on Clifford neurons in the early 2000s to increasingly interdisciplinary applications in recent years. Analysis of his publication history reveals a significant research focus spanning over 15 years, with a notable resurgence of activity in 2024. His recent work demonstrates sophisticated applications of geometric algebra to solve challenging problems across physics (Maxwell's equations, Navier-Stokes equations), molecular biology (protein structure prediction), and robotics (camera pose estimation). This interdisciplinary approach connects mathematical theory with practical implementations in scientific computing and artificial intelligence. Prof. Buchholz maintains an active research profile with multiple high-impact publications in 2024, indicating ongoing contributions to the advancement of geometric algebra applications in computational science. His work bridges theoretical mathematics with practical machine learning implementations, contributing to both academic knowledge and potential real-world applications in scientific computing and data analysis.

Ulrich Bükerمشاهده پروفایل

استاد

• Artificial Intelligence
• Autonomous Driving
• Computer Vision
• +۳ مورد دیگر

Prof. Ulrich Büker is a faculty member at TH OWL since 2022, leading the Intelligent Systems research group at inIT (Institute for Industrial Information Technology). He became deputy head of inIT in 2024 and Vice Dean of Department 5 in 2025. His work bridges academic research and industrial applications in intelligent systems. Education : Diploma in Computer Science (University of Paderborn), Doctorate and Habilitation in Electrical Engineering and Information Technology (University of Paderborn). Research Interests include: Application-oriented artificial intelligence for automation and mobility systems. Autonomous driving with focus on scenario generation and vision-based control. Computer vision for robotics and industrial applications. Embedded systems in battery electric vehicles. Article Trends demonstrate expertise in autonomous systems, computer vision, and industrial applications, spanning from foundational 2000s work on hybrid object models to recent LLM-driven autonomous driving scenario generation . Key sub-fields include neural networks, image processing, and vehicle energy management. Professional Contributions include: Industrial leadership roles at Aptiv , Delphi , and Hella . Membership in German Informatics Society (GI) and German Society for Pattern Recognition (DAGM) . Patents in image segmentation (DE000010250781, EP000001416441).

• Combinatorial Optimization
• Quadratic Programming
• Binary Optimization
• +۵ مورد دیگر

Cédric Elloumi is a Professor at the CEDRIC Laboratory within Conservatoire National des Arts et Métiers (CNAM), specializing in combinatorial optimization and mathematical programming. With a continuous publication record since 1992, he has established himself as a leading researcher in quadratic programming, binary optimization, and facility location problems. His research interests focus on developing exact and approximate methods for discrete optimization problems, particularly through convex reformulation techniques. Elloumi has made significant contributions to the p-center and p-median problems, quadratic assignment problems, and more recently, quantum-inspired optimization methods. His work bridges theoretical advancements with practical applications in network design, energy systems, and telecommunications. Analysis of his recent publications (2022-2025) reveals a continued focus on facility location problems, with increasing attention to robust optimization under uncertainty and emerging applications in quantum computing. His research demonstrates consistent methodological innovation, particularly in reformulation techniques that transform difficult non-convex problems into tractable forms. Throughout his career, Elloumi has maintained extensive collaborations with researchers including Billionnet, Lambert, Alès, and Plateau, resulting in numerous publications in top-tier optimization journals such as Journal of Global Optimization, Computers and Operations Research, and Mathematical Programming.