جستجوی استاد راهنمای دانشگاهی

Manfred Dorninger serves as Associate Professor in the Department of Meteorology and Geophysics at the University of Vienna's Faculty of Earth Sciences, Geography and Astronomy. His research spans meteorological modeling, renewable energy systems, and atmospheric observation techniques. His primary research interests focus on weather forecasting in complex terrain , cloud physics and classification , lightning phenomena , and renewable energy integration . Recent work leverages machine learning for ground-based cloud observations and improves photovoltaic efficiency under variable cloud conditions. His fingerprint analysis reveals significant contributions to Complex Terrain (100%), Lightning (72%), and Weather Forecast (42%) research domains. Analysis of his 111 publications shows increasing emphasis on AI-driven meteorological analysis (2024-2025), with recent papers developing neural network ensembles for cloud classification and novel metrics for ensemble forecast verification. His work bridges fundamental atmospheric science with practical applications in renewable energy and weather impact assessment. He has led significant research projects including NWP-Modellverifikation (2008-2011) and actively participates in the MesoVICT (Mesoscale Verification in Complex Terrain) initiative. His 120 recorded activities include 78 scientific talks and 38 poster presentations, demonstrating extensive knowledge dissemination. Dorninger maintains strong collaborative networks with researchers like Markus Rosenberger and Martin Weißmann, focusing on computational meteorology and atmospheric observation systems. His laboratory work centers on ground-based optical radar systems and neural network applications for weather parameter analysis.

• Meteorology
• Atmospheric Science
• Weather Forecasting
• +۵ مورد دیگر

Markus Höglinger-Rauscher is a researcher at the University of Applied Sciences Upper Austria, active in the Center of Excellence Automotive/Mobility and Center of Excellence Medical Engineering/TIMed Center. His work focuses on advanced material characterization techniques and computational methods. Affiliations: Research Center Wels, Smart Production Computed Tomography focal area Research Interests: His primary fields include Computed Tomography , Machine Learning , and Non-destructive Testing with applications in Carbon Fiber Reinforced Polymers and Thermoelectric Modules . He specializes in industrial X-ray imaging, neural network-based segmentation, and multi-modal material analysis. Recent Publications: In 2024, he published two conference posters on thermoelectric module inspection using infrared thermography and acoustic microscopy. In 2023, he contributed to a journal article on U-Net neural networks for pore segmentation in composites, leveraging X-ray CT data. Projects: He is a co-investigator in the HyperMAT project (2023-2028) for hyperspectral material characterization and the TC-HiResFoot project (2022-2025) applying high-resolution imaging to diabetic foot complications. His collaborations span institutions in Upper Austria, focusing on advanced procedures and aerospace applications.

• Computed Tomography
• Machine Learning
• Materials Engineering
• +۳ مورد دیگر

Lars Mehnen serves as a Senior Lecturer and Researcher at the University of Applied Sciences Technikum Wien (UAS Technikum Wien) in Austria, where he has been teaching since 2019 in Computer Science and previously from 2003-2019 in Biomedical Engineering. His academic career includes significant roles at Vienna University of Technology as University Assistant (2004-2007) and Research Assistant (1998-2002), along with leadership positions in major international space initiatives including QB50 (2010-2013), GENSO (2006-2010), and SSETI (1999-2006). TU-Wien: Dipl.Ing. in Computer Engineering and Medical Informatics Dr. Techn. from Institute for Fundamentals and Theory of Electrical Engineering and Institute of Technical Mathematics Mehnen's research spans multiple domains with particular expertise in Artificial Intelligence, Evolutionary Algorithms, and Sensor Technology. His work in explainable and non-parametric evolutionary algorithms represents cutting-edge contributions to AI methodology. In biomedical engineering, he has developed innovative magnetoelastic skin curvature sensors for cardiovascular monitoring and other medical applications. His teaching portfolio is exceptionally diverse, covering Statistics (parametric and non-parametric), Artificial Intelligence, Aerodynamics (particularly for sports equipment), Physics for game engineering, and extensive programming topics including Java, C/C++, Python, and functional programming languages. His publication record from 2001-2024 shows a clear evolution from early work on magnetostrictive bilayer sensors toward more recent applications of machine learning in biological classification, medical diagnostics, and educational technology. The 2024 publication on ChatGPT's diagnostic accuracy for medical conditions demonstrates his engagement with the latest AI developments. His research consistently bridges theoretical computer science with practical applications in healthcare, sports engineering, and space technology. Mehnen has been deeply involved in European collaborative projects throughout his career, including QB50 (EC F7), GENSO (ESA initiative), and B-Sens (EC-Project in 5th framework). These projects involved coordination with numerous universities and institutions across Europe, demonstrating his ability to lead international research collaborations. His work on the SSETI Express satellite project resulted in the successful launch of a micro-satellite at the end of 2005. At UAS Technikum Wien, Mehnen contributes to curriculum development across multiple domains including statistics, AI, programming, and physics. His research on paraglide control systems demonstrates application of his expertise to sports safety technology, while his recent work in teaching analytics shows commitment to improving educational outcomes through data-driven approaches.

• Artificial Intelligence
• Evolutionary Algorithms
• Machine Learning
• +۵ مورد دیگر

Linus Franke is a postdoctoral researcher at Inria Sophia Antipolis in France, affiliated with the GraphDeco research group under George Drettakis. He previously completed his PhD at the Chair of Visual Computing , Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Germany under Marc Stamminger. His research focuses on Computer graphics Perceptual rendering Computer vision Machine learning Neural rendering Novel view synthesis Key trends in his recent publications include neural rendering , 3D Gaussian splatting , radiance fields , and real-time rendering with applications in virtual reality and light field displays. His work often integrates point clouds , differentiable rendering , and GPU optimization techniques. His personal website at lfranke.github.io highlights projects like VET (Visual Error Tomography) and TRIPS (Trilinear Point Splatting for Real-Time Rendering).

• Computer Graphics
• Perceptual Rendering
• Computer Vision
• +۳ مورد دیگر

Radomir Dinic is a lecturer and researcher at Salzburg University of Applied Sciences, specializing in Multimedia Technology. He holds a BSc and MSc in Media Informatics from the same institution. Research Interests: His work focuses on Augmented and Virtual Reality applications Computer vision for health monitoring Mobile food portion estimation systems Game development for educational purposes Scientific Recognition: Austrian CG Award (2015, 2016) State of Salzburg Science Award (2017) Key Projects: YoKaisho (AR board game hybrid) NIVA (VR exploration art game) EatAR (nutritional assessment AR app) SmartSignCapture (sign language to 3D avatars) Ecomedicine VR (therapeutic waterfall simulation)

• Computer Vision
• Mixed Reality
• Game Development
• +۷ مورد دیگر

Matias Valdenegro-Toro serves as Assistant Professor for Machine Learning at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen. His research focuses on enabling robots to achieve human-like perception, with expertise spanning Deep Neural Networks, Reinforcement Learning, and underwater robotic vision systems. Previously, he held positions as a Researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen and as a Marie Curie Fellow at Heriot-Watt University's Ocean Systems Lab. His academic credentials include a PhD in Electrical Engineering from Heriot-Watt University (2019), a Master of Science in Autonomous Systems from Hochschule Bonn-Rhein-Sieg (2014), and an Engineering Degree from Universidad Tecnologica Metropolitana (2009), preceded by four years of professional industry experience. Valdenegro-Toro's research integrates Machine Learning and Robotics to solve real-world perception challenges, particularly in underwater environments where his PhD work developed neural network systems for detecting submerged marine debris. His commitment to environmental protection drives his focus on ocean conservation through robotic technology, while his technical contributions emphasize Uncertainty Quantification in neural networks and robust Robot Vision solutions. His scientific recognition is highlighted by the prestigious Marie Curie Fellowship , awarded through the European Commission-funded Robocademy Initial Training Network (FP7-PEOPLE-2013-ITN). As an educator, he supervises Master's students in Hochschule Bonn-Rhein-Sieg's Autonomous Systems program and has taught at the University of Bremen. He actively contributes to the academic community through invited tutorials on computer vision paper writing at ICCV/CVPR and mentoring workshops for LatinX researchers at AAAI/AISTATS, reflecting his dedication to inclusive AI education. His collaborative research spans institutions including DFKI's Robotics Innovation Center and Heriot-Watt University's Ocean Systems Lab, focusing on practical deployment of perception systems for autonomous underwater vehicles in marine conservation applications.

• Machine Learning
• Robotics
• Computer Vision
• +۵ مورد دیگر

Cornelia Lex is an Associate Professor at Graz University of Technology's Institute for Vehicle Engineering. Her work focuses on vehicle dynamics, tire-road interaction, and human-machine interaction in automated driving systems. She employs advanced modeling and control strategies for tire mechanics, friction estimation, and safety-enhancing driver assistance technologies. Academic Affiliation: Graz University of Technology Research Areas: Vehicle dynamics, tire modeling, friction estimation, automated driving Contact: cornelia.lex@tugraz.at Her research spans tire wear emissions, sensor fusion for vehicle state estimation, and environmental impact analysis of transportation systems. Recent publications address tire wear particle distribution, online tire modeling, and water detection systems for automated driving. She leads projects funded by the Austrian Society of Automotive Engineers (ÖVK) and contributes to multidisciplinary development of driving simulators and driver assistance systems.

• Vehicle Dynamics
• Automotive Engineering
• Tire-Road Interaction
• +۴ مورد دیگر

Michael Rauter serves as a Research Associate at the University of Applied Sciences Wiener Neustadt (FHWN) within the Faculty of Health and the Competence Center for Preclinical Imaging and Medical Technology. His work bridges computer science and medical applications, focusing on advanced imaging techniques for radiotherapy and assisted living technologies. Dr. Rauter's research interests include: Medical imaging for precision radiotherapy GPU-accelerated image processing Virtual and mixed reality applications in healthcare Computer vision for medical diagnostics Assisted living technologies and home care systems His recent publications show a clear evolution from computer vision applications in security systems toward medical applications. The 2024 paper on accelerating transfer function updates for volume rendering represents the current focus on optimizing medical visualization techniques, while earlier works from 2013 demonstrate foundational expertise in GPU-accelerated computer vision. Dr. Rauter is actively involved in multiple research projects funded by organizations including FFG (Austrian Research Promotion Agency) through the Active Assisted Living Programme. His current projects include PAIR (2022-2026) and Applied Molecular Imaging (2021-2026), with previous contributions to Care about Care (2021-2023) and CARU Cares (2019-2022). As a member of the Competence Center for Preclinical Imaging and Medical Technology, Dr. Rauter contributes to research that integrates advanced imaging modalities with radiotherapy, aiming to improve cancer treatment through personalized precision approaches. His work combines technical expertise in computer vision and GPU programming with medical applications to advance healthcare technologies.

• Medical Imaging
• Radiotherapy
• Virtual Reality
• +۵ مورد دیگر

Armin Dadras serves as a Junior Researcher at the University of Applied Sciences St. Pölten within the Media Computing Research Group of the Institute of Creative Media Technologies and Department of Media and Digital Technologies. His academic qualifications include: Bachelor of Arts (BA) Bachelor of Science (BSc) Master of Science (MSc) His research bridges computer vision and biomedical engineering, specializing in interpretable geometric feature extraction for photography composition analysis and deep learning applications in medical imaging. Current work focuses on rule-of-thirds detection algorithms and glottis segmentation failure identification in endoscopic videos. Publications reveal a strong interdisciplinary trajectory merging media technologies with healthcare solutions, particularly through computational photography and speech pathology diagnostics. Dadras actively contributes to the Media Computing Research Group, driving innovation in media technology applications through advanced computing methodologies. No documented information exists regarding student supervision or research grant acquisitions.

• Computer Vision
• Medical Image Processing
• Deep Learning
• +۲ مورد دیگر

Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.

• Machine Learning
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Univ.-Prof. Dr. Uli Meyer is a prominent sociologist specializing in innovation, digitalization, and organizational studies. He currently serves as University Professor and Head of the Department of Sociology with a focus on Innovation and Digitalization (SID) at the Institute of Sociology, Johannes Kepler University Linz. His academic career spans multiple prestigious institutions including Technical University of Berlin, Munich Center for Technology in Society, and Ruhr University Bochum. Professor Meyer completed his doctorate in Sociology at Technical University of Berlin with the dissertation "Innovation Paths: Evolution and Institutionalization of Complex Technology." His interdisciplinary background in sociology, computer science, and economics informs his unique approach to studying technology-society relations. His research focuses on the social dimensions of digital transformation, particularly examining how organizations make sense of and implement technologies like Industrie 4.0. Professor Meyer investigates the interplay between technological innovation and social change, with particular attention to work organization, organizational dynamics, and the social construction of technological futures. His approach integrates theoretical perspectives from organizational sociology, institutional theory, and science and technology studies to understand how digitalization reshapes work practices, organizational structures, and societal relations. Professor Meyer's recent publications (2019-2024) demonstrate a consistent focus on organizational recombination processes, sensemaking around technological futures, and the everyday practices of digitalization. His work reveals how organizations navigate complexity and uncertainty in the digital age through creative adaptation rather than wholesale transformation. Organizational recombination and social change Sensemaking processes around technological futures Digital transformation of work practices Power dynamics in innovation ecosystems Methodological approaches to studying digitalization Throughout his career, Professor Meyer has secured research funding for projects examining digitalization and innovation. His leadership roles, including heading the Department of Sociology with a focus on Innovation and Digitalization at JKU Linz and directing the Post-Doc Lab Reorganizing Industries at TU Munich, demonstrate his standing in the field. He has collaborated with scholars across Europe on projects related to innovation society and technological transformation, contributing to both theoretical advances and practical understanding of how organizations navigate digital change.

• Innovation Sociology
• Sociology of Digital Work
• Artificial Intelligence
• +۵ مورد دیگر

Siavash Arjomand Bigdeli serves as an Associate Professor of Computer Vision at the Technical University of Denmark, following prior employment as a scientist at the Swiss Center for Electronics and Microtechnologies (CSEM). His research focuses on: Ante-/Post-Hoc explainability of machine learning models Integration of statistical models in learning/inference processes Philosophical methodologies in artificial intelligence development Advanced computer vision techniques for visual understanding Recent publications reveal consistent specialization in image restoration and stereo vision, employing deep learning architectures and probabilistic graphical models to solve core challenges in visual data reconstruction and temporal coherence. His work demonstrates strong interdisciplinary connections between theoretical machine learning, practical computer vision applications, and epistemological considerations in AI systems.

• Computer Vision
• Explainable Machine Learning
• Statistical Models in AI
• +۳ مورد دیگر

Md Atiqur Rahman Ahad is a Professor at the University of Dhaka (DU) and a Specially Appointed Associate Professor at Osaka University. His academic career spans multiple institutions across Bangladesh, Australia, and Japan. His educational background includes: B.Sc.(Honors) and Masters from University of Dhaka Masters from University of New South Wales PhD from Kyushu Institute of Technology Professor Ahad is a recognized expert in computer vision and activity analysis. His research focuses on: Computer Vision and Image Processing Sensor-based Activity Analysis Human-Media Interaction Gesture and Activity Recognition Imaging Technologies His recent publication trends show a strong emphasis on activity recognition using various sensor modalities and computer vision techniques. He has published extensively on packaging activity recognition, gait analysis, and hand gesture recognition, demonstrating expertise in both theoretical foundations and practical applications of vision-based activity understanding. Professor Ahad serves in editorial roles for academic journals: Associate Editor for Human-Media Interaction, Frontiers in Computer Science Guest Associate Editor for Big Data Networks, Frontiers in Big Data

• Computer Vision
• Sensor-based Activity Analysis
• Imaging
• +۳ مورد دیگر

Alexander Buchelt serves as a Researcher in the Department of Computer Science and Security at University of Applied Sciences St. Pölten, contributing to the Data Science and Artificial Intelligence (BA) program. His industry background includes data analytics roles at Hutchison Drei Austria (2014-2018) and MindTake Research GmbH (2019-2020), followed by academic experience as a Student Assistant at FH St. Pölten (2020-2022). His research spans artificial intelligence with emphasis on natural language processing for German content moderation, fractal-based neural network architectures, and AI policy frameworks. Current work demonstrates strong focus on real-world implementation including drone applications in forest ecology, multilingual social media analysis, and regulatory compliance for emerging AI systems. Technical approaches integrate machine learning with domain-specific challenges across environmental science, media analysis, and telecommunications. Publication trends since 2022 reveal increasing emphasis on ethical AI deployment and practical system robustness, with 2024-2025 works addressing content moderation safeguards, neural network innovation, and environmental monitoring solutions. His research consistently bridges theoretical AI development with tangible societal applications, particularly in European regulatory contexts and German-language environments. As part of the Department of Computer Science and Security, Buchelt contributes to Austria's applied research ecosystem focused on practical technology solutions. His work aligns with the university's mission in developing industry-relevant AI applications while addressing emerging challenges in security, ethics, and cross-domain implementation.

• Artificial Intelligence
• Data Science
• Natural Language Processing
• +۳ مورد دیگر