جست‌وجوی استاد و پژوهشگر

Gabriel Kerekesمشاهده پروفایل

پژوهشگر ارشد

• Geodesy
• Terrestrial Laser Scanning
• Engineering Surveying
• +۵ مورد دیگر

Gabriel Kerekes serves as a Postdoctoral Researcher and Group Leader of 3D Data Acquisition and Monitoring at the Institute of Engineering Geodesy Stuttgart (IIGS), part of the Faculty of Aerospace Engineering and Geodesy at the University of Stuttgart. He is actively affiliated with the Cluster of Excellence IntCDC (Integrative Computational Design and Construction for Architecture), contributing to cutting-edge research in computational construction methodologies. Dr. Kerekes specializes in terrestrial laser scanning (TLS), engineering surveying, and 3D data acquisition systems. His research addresses critical challenges in point cloud processing, stochastic modeling of geodetic measurements, and robotic total station networks for real-time construction monitoring. He develops innovative solutions for deformation analysis, geometric quality control of bio-based building elements, and integration of multi-sensor systems in architectural applications, with significant contributions to biomimetic shell construction and infrastructure monitoring projects. No scientific awards or fellowships were documented in the provided materials. Dr. Kerekes has supervised ten Master's and Bachelor's students since 2018, with research spanning TLS intensity analysis, atmospheric error modeling, and robotic positioning systems. His grant-funded projects include RP 16-2 (Spider Crane Robotic Platform) and AP 7 (Integrated Space-Time Point Cloud Modeling) under the IntCDC cluster. As leader of the 3D Data Acquisition and Monitoring team, he drives advancements in geodetic measurement technologies for next-generation construction processes.

• Robotics
• Computer Vision
• Industrial Automation
• +۴ مورد دیگر

Oleksandr Semeniuta is an active researcher specializing in robotics, computer vision, and industrial automation systems. His work demonstrates consistent contributions to the field with publications spanning from 2015 to 2022, indicating ongoing academic or research activity. His collaborations with researchers like Petter Falkman across multiple publications suggest established research partnerships in the robotics domain. Dr. Semeniuta's research interests center around practical applications of robotics and computer vision in industrial settings. His work encompasses event-driven architectures for data processing and robot control, stereo calibration methods for 3D reconstruction, and resource-aware machine learning for IoT security. His research shows a strong focus on developing practical frameworks and systems that address real-world challenges in manufacturing and industrial automation. The publication trends reveal a consistent focus on robotics systems engineering, with particular emphasis on vision-based solutions and event-driven architectures. His work bridges theoretical computer vision concepts with practical industrial applications, especially in automotive manufacturing and robotic deburring systems. The research demonstrates progression from foundational work on dataflow programming for vision systems to more complex implementations of machine learning on edge devices for security applications. While specific awards are not documented in the available publication records, the consistent output in reputable venues including IEEE conferences and PeerJ Computer Science indicates recognition within the academic community. His work appears in both journal and conference publications, suggesting a balanced approach to disseminating research findings through different academic channels. Dr. Semeniuta's collaborative network includes researchers from various institutions, with Petter Falkman appearing as a frequent co-author across multiple projects. This suggests participation in research teams focused on industrial robotics and computer vision applications. His work on frameworks like EPypes indicates contributions to software infrastructure that enables more efficient development of robotic systems and data processing pipelines.

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +۳ مورد دیگر

Claudia Redenbach is a Professor and Dean of Mathematics at RPTU Kaiserslautern, Germany. She is affiliated with the Department of Mathematics and leads the Statistics Working Group (AG Statistik), with additional connections to the Department of Statistics and the Graduate School 'Mathematics as a Key Technology.' Position: Dean of Mathematics Institution: RPTU Kaiserslautern Location: Building 48, Room 534, Gottlieb-Daimler-Straße, 67663 Kaiserslautern Contact: claudia.redenbach@rptu.de | +49 (0)631 205 3620 Professor Redenbach's research focuses on the intersection of stochastic geometry, spatial statistics, and image analysis with applications in materials science. Her work bridges theoretical mathematics with practical engineering applications, particularly in analyzing microstructures of concrete, foams, composites, and other materials. She has developed innovative methods for analyzing spatial point patterns, directional data, and 3D image data from various microscopy techniques including CT, FIB-SEM, and light-sheet microscopy. Her recent publications (2024-2025) reveal a strong emphasis on computational methods for materials analysis, including crack detection in concrete, fiber orientation analysis, artifact removal in imaging, and the development of mathematical morphology techniques for directional data. She frequently collaborates with materials scientists and engineers, with K. Schladitz appearing as a consistent co-author across numerous publications. Her work demonstrates a consistent focus on developing mathematical tools that solve practical problems in materials characterization and analysis. Professor Redenbach has made significant contributions to spatial statistics, particularly in anisotropy analysis of point patterns, and to the application of stochastic geometry models for material microstructure analysis. Her research spans both theoretical developments in statistical methods and their practical implementation for real-world materials science problems.

• Computer Vision
• Artificial Intelligence
• Extended Reality
• +۴ مورد دیگر

Prof. Dr. Helge Rhodin is a faculty member at the University of Bielefeld, serving as Head of the Visual AI for Extended Reality Group within the Faculty of Engineering. His research activities are centered at the Center for Cognitive Interaction Technology (CITEC), a central academic institute at the university focused on interdisciplinary research in cognitive systems, robotics, and human-computer interaction. His office is located in CITEC building room 3-225, with secretariat contact via susanne.strunk@uni-bielefeld.de. Professor Rhodin's research focuses span multiple cutting-edge areas in computer vision and artificial intelligence. His primary interests include Computer Vision, Artificial Intelligence, Extended Reality, Human-Computer Interaction, 3D Reconstruction, and Motion Capture. His work demonstrates strong interdisciplinary connections between the university's 'Socio-Technical World' strategic research area, which examines how humans, robots, and AI interact in complex environments. Analysis of Professor Rhodin's recent publications reveals a consistent focus on advancing techniques for human and object representation in virtual and augmented environments. His work spans from fundamental computer vision techniques like keypoint detection and motion capture to advanced applications in digital twin generation, neural rendering, and animal behavior analysis. Notably, his research shows practical applications in sports science (particularly skiing analysis) and biological motion tracking. Within the university governance structure, Professor Rhodin serves as a member of both the Habilitation Committee and the Faculty Conference of University Professors within the Faculty of Engineering, indicating his active participation in academic leadership and quality assurance processes.

Thomas Villmannمشاهده پروفایل

استاد

• Computational Intelligence
• Machine Learning
• Vector Quantization
• +۷ مورد دیگر

Thomas Villmann is a Professor of Computational Intelligence and Techno-Mathematics at Mittweida University of Applied Sciences in Germany. He serves as Deputy Spokesperson for the Mathematics Department, AI Coordinator at the university, Director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM), and President of the German Chapter of the European Neural Network Society (GNNS). His academic credentials include the German 'Dr. rer. nat. habil.' designation, indicating both doctoral and habilitation qualifications in natural sciences. Professor Villmann's research focuses on computational intelligence with particular emphasis on vector quantization, learning vector quantization, neural networks, and interpretable machine learning. His work spans theoretical developments in mathematical foundations of machine learning algorithms as well as practical applications in bioinformatics, remote sensing, medical diagnostics, and autonomous systems. He has developed mathematically sound methods for data-based analysis (clustering), decision support systems, data-based prediction models, and visualization of complex data. His recent publication record demonstrates a strong trend toward interpretable and explainable AI, with emphasis on vector quantization techniques applied across diverse domains including medical diagnostics (particularly breast cancer detection), fairness in machine learning, satellite remote sensing, and autonomous vehicle systems. Villmann's work consistently bridges theoretical mathematics with practical applications, maintaining a focus on making machine learning models more transparent, reliable, and ethically sound. As Director of SICIM and leader of the Computational Intelligence Research Group, Professor Villmann oversees an integrated research ecosystem focused on problem-oriented intelligent data analysis. His institutes aim to stimulate interest in computational intelligence among students and young researchers while supporting public institutions, authorities, and companies in data analysis both regionally and internationally. His leadership extends to organizing academic events and workshops, including serving as president of the German Chapter of the European Neural Network Society.

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.

Ciarán Doneganمشاهده پروفایل

پژوهشگر ارشد

• Regulatory Genomics
• Explainable AI
• Geometric Deep Learning
• +۱ مورد دیگر

Ciarán Donegan is a Research Fellow in the Machine Learning group at the Technical University of Berlin and BIFOLD (Berlin Institute for the Foundations of Learning and Data), with a concurrent guest researcher position in the Ohler Lab at the Max Delbrück Center for Molecular Medicine. His primary research focuses on applying deep learning methodologies to elucidate gene regulation mechanisms within molecular biology. Education: M.A.I. in Electronic & Computer Engineering from Trinity College Dublin (2021) B.A.I. in Electronic & Computer Engineering from Trinity College Dublin (2020) His research program integrates Regulatory Genomics with cutting-edge machine learning techniques, emphasizing Explainable AI frameworks and Geometric Deep Learning architectures. This interdisciplinary approach targets complex biological systems, particularly in modeling transcriptional regulation and epigenetic modifications through neural network interpretability. Applications extend to disease mechanism analysis and therapeutic target identification in life sciences contexts. Publication trends reveal dual-domain expertise: the 2022 sports broadcasting paper demonstrates real-time computer vision capabilities for automated production systems, while the 2021 VPU-specific CNN research pioneers hardware-aware neural architecture search. These works collectively underscore a methodological thread in optimizing deep learning for constrained environments—whether computational (edge devices) or operational (live sports)—while maintaining biological research as his core focus area. No scientific awards were documented in the source materials. Advisory activities and grant funding details remain unspecified in available records. He operates within collaborative research ecosystems spanning TU Berlin's Machine Learning group, BIFOLD's interdisciplinary data science initiatives, and the Ohler Lab's genomic regulatory studies at the Max Delbrück Center, facilitating cross-institutional innovation in AI-driven life sciences.

• Virtual Reality
• Extended Reality (XR)
• Computer Graphics
• +۵ مورد دیگر

Prof. Dr. Kiran Varanasi serves as a Professor in Virtual and Extended Reality at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences. His research focuses on 3D shape processing, real-time facial animation, and convolutional neural network applications in computer vision. University: Leipzig University of Applied Sciences School: Faculty of Computer Science and Media Academic Rank: Professor Research Interests: Varanasi's work bridges virtual reality , machine learning , and interactive systems . Key areas include: 3D reconstruction using deep learning Facial animation pipelines Light field data processing Surface defect classification with CNNs Interactive character control Publication Trends (2018–2021): His recent articles emphasize monocular 3D reconstruction , real-time feature extraction , and immersive VR environments , often leveraging convolutional networks and geometric warping techniques. Advising and Grants: No specific students or advisory roles are listed in the provided text. The faculty participates in collaborative projects like website redesign for the Egyptian Museum, though Varanasi's direct involvement isn't specified. Labs and Teams: The faculty includes multimedia labs and research groups in digital transformation, but Varanasi's direct affiliations with these units are not explicitly stated.

Nir Sochenمشاهده پروفایل

استاد

• Differential Geometry
• Image Processing
• Image Analysis
• +۵ مورد دیگر

Prof. Nir Sochen serves as a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences. Based in Schreiber building, room 103, he maintains active contact via email (sochen@math.tau.ac.il) and phone (972-3-6409622), reflecting his current faculty status with no indication of retirement or emeritus designation. His research integrates Differential Geometry with Image Processing, Image Analysis, and Computational Vision, developing mathematical frameworks for visual data interpretation. Previous work spanned Conformal Field Theories, Integrable Systems, String Theory, and Statistical Mechanics, demonstrating cross-disciplinary expertise bridging theoretical physics and applied mathematics. This evolution highlights his focus on geometric methods for real-world computational challenges. No scientific awards are documented in the available sources. While student advising details appear accessible through dedicated departmental pages (referenced via students.html link), specific mentorship activities and research grants remain unlisted in the current text corpus.

• Hamiltonian Systems
• Model Reduction
• Geometric Optimization
• +۴ مورد دیگر

Dr. Thomas Bendokat is a researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. He specializes in computational methods for systems and control theory, with a focus on geometric approaches to dynamical systems and manifold structures. Research Interests: Hamiltonian systems, symplectic geometry, Grassmann and Stiefel manifolds, model reduction, geometric optimization, and data-driven identification of nonlinear systems. Contact: Email: bendokat@mpi-magdeburg.mpg.de His work bridges theoretical mathematics with practical applications in gas network modeling, computer vision, and structure-preserving computational methods. He has contributed to open-source software like phgasnets for port-Hamiltonian system simulations.

• Machine Learning
• Computer Vision
• Autonomous Driving
• +۴ مورد دیگر

Ross Greer is an Assistant Professor in the Department of Computer Science & Engineering at the University of California Merced. He leads the Mi³ Lab, focusing on machine intelligence, human-agent interaction, and safe autonomous systems. Education : B.S. and B.A. in EECS, Engineering Physics, and Music from UC Berkeley (2015), M.S. in Electrical & Computer Engineering from UC San Diego (2018), Ph.D. in Electrical & Computer Engineering at UC San Diego (2021) under Mohan Trivedi and Shlomo Dubnov. His research explores computational intelligence for open-world adaptability, robustness to rare events, and safety in chaotic environments. Key applications include autonomous driving, driver state analysis, trajectory prediction, and AI-assisted musical creativity. Recent publications emphasize vision-language models, active learning for 3D object detection, and safety metrics. Awards include the 2024 Interdisciplinary Research Award, Henry Booker Award for Ethical Engineering, and multiple best poster/grand prizes. Scientific Awards : 2024 Interdisciplinary Research Award 2024 Henry Booker Award for Exemplary Ethical Engineering Postdoctoral Networking Fellowship (Germany's Academic Exchange Service) Grand Prize (AWS Automotive Day competition at IEEE Intelligent Vehicles Symposium, 2023) Best Poster Awards (2021/2023 Jacobs Research Expo) He also co-authored the textbook Deep and Shallow: Machine Learning in Music and Audio (Chapman & Hall, 2023) and serves as a music director for UCSD's Symphonic Student Association and UC Merced's marching band.

Benedikt Wirthمشاهده پروفایل

استاد

• Optimisation and calculus of variations
• image processing
• scientific computing
• +۹ مورد دیگر

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Digital Twinning
• Building Information Modeling (BIM)
• Computer Vision
• +۳ مورد دیگر

Mansour Mehranfar is a Researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich (TUM). He contributes to advancing AI applications in digital twinning of the built environment through the AI4Twinning research initiative, focusing on automated generation of semantic building models from point cloud data and imagery. His research centers on Digital Twinning, Building Information Modeling (BIM), and Computer Vision, with specific expertise in point cloud processing, semantic segmentation, and 3D reconstruction. He develops AI-driven frameworks that integrate deep learning with geometric modeling to convert raw sensor data into semantically enriched digital representations of buildings and infrastructure components. Recent publications reveal a strong emphasis on staircase modeling, indoor space documentation, and domain adaptation techniques. Key innovations include hybrid top-down/bottom-up approaches for Manhattan-world structures, parametric prototype model fitting, and multi-task learning frameworks that simultaneously handle scene parsing and 3D reconstruction from single images. Dr. Mehranfar actively supervises Master's theses, guiding students in topics such as load-bearing wall detection, staircase modeling automation, and BIM change management. He teaches Engineering Databases at TUM and collaborates within the university's BIM-Lab ecosystem, leveraging facilities for robotic fabrication and mobile machinery research.

• Mathematics
• Theoretical Computer Science
• Biomedical Research
• +۳ مورد دیگر

Stefaan Vaes is a Professor at the University of Göttingen, actively serving as an advisor and referee for numerous PhD theses since at least 2002. His academic role is prominently documented in the university's eDiss repository, spanning research areas such as Mathematics, Theoretical Computer Science, and Biomedical Sciences. Key student theses include topics like approximation of center-valued Betti numbers (2009), 3D imaging of immune cell reactions in pancreatic cancer (2024), and 2D-Shear-wave elastography for liver stiffness evaluation (2025). His advisory work reflects interdisciplinary expertise, covering advanced mathematical theories, biomedical applications, and cutting-edge imaging techniques. Research interests include: Mathematical analysis of geometric structures Computational modeling in robotics and computer vision Biomedical applications of polymerization and immunotherapy Neuroscience and neurodegenerative disease analysis Medical imaging technologies (e.g., STED microscopy, micro-CT) Dynamic systems in Riemannian manifolds While no specific scientific awards are documented in the provided texts, his long-term mentorship (2002-2025) across diverse disciplines highlights his sustained academic impact.

Torsten Sattlerمشاهده پروفایل

استاد پژوهشی

• 3D computer vision
• Machine learning
• Image-based localization
• +۱۰ مورد دیگر

Torsten Sattler is a Senior Researcher at the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) at the Czech Technical University in Prague (CTU), where he heads the Spatial Intelligence group. Previously, he was a tenured Associate Professor at Chalmers University of Technology in Sweden and spent five years as a PostDoc and Senior Researcher at ETH Zurich in Switzerland. He received his PhD from RWTH Aachen University in Germany. His research focuses on the intersection of 3D computer vision and machine learning, with specific interests in image-based localization, 3D mapping and reconstruction, neural scene representations, and applications in robotics and AR/VR. He aims to make localization and mapping algorithms more robust by incorporating higher-level scene understanding. Dr. Sattler has published extensively at top computer vision conferences including CVPR, ICCV, and ECCV, with recent papers focusing on robust visual localization in changing environments, neural scene representations, and 3D reconstruction. His work has direct applications in autonomous systems and augmented reality. Best Paper Candidate at CVPR 2021 Best Paper Award at Photogrammetric Image Analysis 2019 Multiple Outstanding Reviewer Awards from 2015-2021 Ranked among top-10 computer scientists in Czech Republic by Research.com He currently supervises five PhD students and has served in various leadership roles including program chair for ECCV 2024, general chair for 3DV 2022, and area chair for multiple major conferences. His lab benefits from connections to the RICAIP Centre, one of the largest EU projects in AI and Industry 4.0, providing access to state-of-the-art infrastructure and industrial collaborations.