جست‌وجوی استاد و پژوهشگر

• Laser Scanning
• Point Cloud Processing
• Geospatial Information Systems
• +۳ مورد دیگر

Tamás Lovas serves as Associate Professor at the Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics. He teaches advanced courses in Building Information Modeling, Laser Scanning, Remote Sensing, and Intelligent Transportation Systems while supervising diploma theses in Surveying and Geoinformatics Engineering. Education: 1994: High school graduation, Városmajor High School, Budapest 1999: Certified Surveyor and Geoinformatics Engineer, Budapest University of Technology, Faculty of Civil Engineering 2005: PhD (Earth Sciences), Budapest University of Technology and Economics, Faculty of Civil Engineering Research Interests: Dr. Lovas specializes in laser scanning technologies and geospatial data processing with emphasis on airborne and terrestrial point cloud analysis. His work bridges civil engineering applications and computational methods, particularly in infrastructure monitoring and digital representation. Processing, classification, and modeling of airborne laser scanned data Accuracy testing of terrestrial laser scanning Processing and modeling of terrestrial laser scanned data Comparative study of spatial data acquisition technologies His 2022-2025 publications demonstrate accelerating integration of artificial intelligence in point cloud processing, with significant contributions to road surface extraction, urban land cover classification, and BIM automation. Current research trends show strong focus on digital twin development for autonomous vehicles and infrastructure management. Scientific Awards: Republic Scholarship (1998-1999) Karlsruhe Chancellor's Scholarship (1999) ERASMUS scholarship (2000) Korányi Fellowship (2001-2002) János Bolyai Research Scholarship (2008-2011) OHV 1st place (2008) Dean's commendation for ERASMUS committee work (2014) For Students Award - Teaching Department (2017) Advising and Grants: Dr. Lovas mentors students through diploma theses and TDK research projects on topics including object survey with amateur sensors and hull modeling. His research funding includes the prestigious János Bolyai Research Scholarship and international fellowships supporting collaborations with institutions like The Ohio State University. Labs and Teams: As founding member and supervisory board member of the Hungarian BIM Association, he drives industry-academia collaboration. His leadership roles include Deputy Dean of Education at the Faculty of Civil Engineering and responsibility for English language training programs, facilitating international academic exchange.

• Geospatial Information
• Remote Sensing
• GIS
• +۴ مورد دیگر

Attila Juhász serves as an Associate Professor within the Department of Photogrammetry and Geoinformatics at the Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). His academic responsibilities include teaching courses such as Digital Surface Modeling, Geospatial Information, Civil Engineering Informatics, and Monitoring and GIS, alongside supervising thesis work through course BMEEOFTTATZ. He maintains active research in geospatial technologies with emphasis on historical applications. His research program centers on applying geospatial information systems and remote sensing to reconstruct historical military operations, particularly World War II events. He develops methodologies for bomb crater detection, fortification mapping, and battlefield analysis using LiDAR and multi-temporal GIS. A significant contribution involves solving temporal data management challenges in historical reconstructions, enabling accurate modeling of evolving military landscapes. His work bridges technical geospatial innovation with humanities scholarship in archaeology and military history. Analysis of his publication record reveals consistent specialization in automated detection of World War II artifacts through LiDAR and spatial data fusion. His research demonstrates progressive technical sophistication from early 2000s case studies to current AI-enhanced object recognition systems. The work establishes critical frameworks for conflict archaeology and has produced notable reconstructions including Budapest defense lines and the Attila-line fortifications. No scientific awards or major fellowships are documented in the available sources. Dr. Juhász supervises thesis research as evidenced by course BMEEOFTTATZ, but specific student names and research grant details remain unreported in the source material. His teaching portfolio indicates extensive mentorship across undergraduate and graduate geospatial curricula. He maintains operational affiliation with the Endre Németh Measurement Station and Education Center, which provides critical infrastructure for geospatial data collection, LiDAR processing, and field-based historical reconstruction projects. This facility supports both his research initiatives and practical student training in advanced surveying techniques.

Dániel Bence Erősمشاهده پروفایل

مدرس

• Data Science
• Data Visualization
• Machine Learning
• +۵ مورد دیگر

Dániel Bence Erős serves as a teaching assistant at the University of Debrecen's Faculty of Informatics within the Department of Data Science and Visualization. His institutional affiliation places him within Hungary's prominent academic hub for computational sciences, working from office I122 in the Faculty of Informatics building at Kassai Street 26, Debrecen. The Department of Data Science and Visualization conducts research and education across critical computational domains including data processing pipelines , advanced visualization techniques , neural network architectures , and geometric modeling systems . Their work spans both theoretical frameworks and practical implementations in machine learning, image analysis, and spatial data systems. As part of this department, Erős contributes to academic instruction within Hungary's growing AI and data science education ecosystem. The department maintains active research in medical imaging, cryptographic systems, and computational geometry through its diverse faculty team. His professional activities operate within the institutional framework of the University of Debrecen, a major Central European research university with strong computational science programs. The department maintains collaborative relationships across multiple technical domains including medical informatics, blockchain systems, and 3D visualization technologies.

Henrietta Tománمشاهده پروفایل

استادیار

• Medical image processing
• Fusion systems
• Quasigroups
• +۳ مورد دیگر

Dr. Henrietta Tomán serves as an Assistant Professor in the Department of Data Science and Visualization at the University of Debrecen's Faculty of Informatics, where she bridges advanced mathematical theory with practical medical imaging applications. Her work integrates abstract algebraic structures with cutting-edge AI systems to solve critical healthcare challenges. Her research portfolio spans three interconnected domains: Medical image processing (particularly ensemble-based segmentation and quality assessment for ophthalmic diagnostics) Geometric structures (quasigroups, loops, and differentiable manifolds) Stochastic optimization for resource-constrained AI systems Analysis of her publication trajectory reveals evolving expertise: early work (2010-2014) established foundations in geometric loop theory applied to image processing, while recent research (2020-2024) pioneers stochastic fusion techniques for medical image ensembles under computational constraints. Her most significant contributions involve translating mathematical abstractions into robust clinical decision-support tools, particularly in diabetic retinopathy detection and epidemic modeling. Current work demonstrates increasing focus on real-time AI systems that maintain accuracy under hardware limitations, reflecting urgent needs in telemedicine and mobile health applications. Dr. Tomán maintains active collaboration within the Doctoral School of Informatics and contributes to Hungary's national research initiatives in medical AI, with consistent publication output in top-tier venues spanning computer vision, medical imaging, and mathematical computing.

• Photogrammetry
• Geoinformatics
• AI in Geospatial Data Processing
• +۵ مورد دیگر

Dr. Molnár Bence is an Associate Professor at the Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics. His research focuses on AI-supported point cloud processing, accuracy analysis of medical imaging techniques, engineering geodesy, deformation analysis, and web-based photogrammetric applications. Courses taught: Civil Engineering Informatics (BMEEOFTAT42), Database Systems (BMEEOFTMI51), Field Course of Structural Geodesy (BMEEOAFAS42), Information Technologies (BMEEOFTMF-1) Research areas: Laser scanner data acquisition and point cloud processing, MS Kinect-based positioning and modeling, web applications for engineering tasks Contact: Room K. ép / I. em. 31/7; Consultation hours: Wednesday 13:00-14:00

• Photogrammetry
• Geoinformatics
• Artificial Intelligence
• +۳ مورد دیگر

Dr. SOMOGYI József Árpád is an Associate Professor at the Department of Photogrammetry and Geoinformatics within the Faculty of Civil Engineering at Budapest University of Technology and Economics. He serves as the Faculty Coordinator for NEPTUN and is a member of the Informatics Faculty Committee. His research focuses on photogrammetry, geoinformatics, and artificial intelligence applications in civil engineering. Notable areas include autonomous vehicle mapping, laser scanning, UAV-based modeling, and point cloud processing. Scientific awards include the Magyar Felvételi Scholarship #építő250 . His work has been applied to transportation infrastructure, geological risk assessment, and historical building reconstruction.

• Logistics Optimization
• Industry 4.0
• Sustainability in Supply Chains
• +۵ مورد دیگر

Hello Tamas is an Associate Professor at the Department of Constitutional Law within the Faculty of Political Science and Law at the University of Miskolc, Hungary. He has served in this faculty since 2004, progressing from university assistant professor to associate professor in 2014, with continuous contributions to research and education. Education: Ságvári Endre High School (1995) Lawyer degree from University of Miskolc (2001) Ph.D. in Political Science and Law (2004) Research Focus: His work spans logistics optimization, Industry 4.0 integration, and sustainable operations management. Key areas include vehicle routing problems, milkrun supply systems, and cyber-physical logistics frameworks. Technological Specialties: Real-time maintenance optimization IoT applications in logistics Drone-based delivery systems Mathematical modeling for supply chains Sustainability in manufacturing Human-machine resource assignment Methodological Approach: Combines algorithmic optimization (Weiszfeld, Wagner-Whitin, Silver-Meal) with practical implementations across automotive manufacturing, food industry, and healthcare logistics.

• Energy Management
• Renewable Energy
• Sustainable Development
• +۶ مورد دیگر

Dr. Valéria Nagy is an Associate Professor at the University of Szeged's Faculty of Engineering, specializing in energy systems, sustainable development, and technical innovation. Her interdisciplinary research bridges mechanical engineering, environmental science, and energy policy, with particular expertise in renewable energy applications and land use planning. She teaches courses including Mechanics, Renewable Energy Sources, and Energy Management. Education: Budapest University of Technology and Economics (PhD in Mechanical and Energy Systems) Budapest University of Technology and Economics (Certified Mechanical Engineer) University of Szeged (Environmental Protection Specialist with Law Degree) Budapest University of Technology and Economics (Certified Engineering Teacher) Her research focuses on technical innovation in energy systems, sustainable land use practices, and the application of microwave technology in biofuel production. She has developed an interdisciplinary approach that connects energy systems with environmental sustainability and engineering ethics. Dr. Nagy's publication record demonstrates a consistent focus on sustainable energy solutions, with recent work exploring AI applications in energy management (2025), interdisciplinary research methodologies (2023), and miniature energy systems (2022). Her research trajectory shows increasing integration of digital technologies with traditional energy engineering, while maintaining strong connections to practical applications in sustainable land use and renewable energy production. Professional Affiliations: Hungarian Academy of Sciences - public body member Hungarian Combustion Science Committee Energy Management Scientific Association Hungarian Biogas Association Dr. Nagy has supervised numerous thesis projects related to energy management, industrial risk management, and technical innovation. Her international experience includes study tours to Loughborough University, Sapienza University of Rome, and Justus-Liebig University in Germany, focusing on renewable energy systems and risk management. She actively participates in Researchers' Night events through 2025, demonstrating ongoing engagement with public science communication.

• Computed Tomography
• Geoinformatics
• Image Processing
• +۴ مورد دیگر

Dr. KAPITÁNY Kristóf is an Associate Professor at the Department of Photogrammetry and Geoinformatics within the Faculty of Civil Engineering at Budapest University of Technology and Economics. His office is located in Room K. ép / I. em. 31/8, with consultation hours every Wednesday from 12:00-13:00. He teaches courses including Civil Engineering Informatics (BMEEOFTAT42) and Numerical Methods (BMEEOFTMK51, BMEEOAFMB51). His research centers on advanced imaging and computational techniques for civil engineering, with key focus areas: Object reconstruction from image series (CT/X-ray analysis of cerebral vasculature, concrete, asphalt, and artworks) Geospatial analysis of urban systems (e.g., bike-sharing networks) Material science applications (organic insulation via SEM, fiber-reinforced concrete) AI-driven efficiency in construction (algorithmic design, digital twins) Recent publications (2015-2024) demonstrate strong emphasis on computed tomography for material diagnostics, with growing exploration of AI strategies. Key domains include structural material degradation analysis, non-destructive testing, geospatial data processing, and heritage conservation, reflecting consistent innovation in imaging methodologies. Awards include the #építő250 Scholarship for academic merit. No information is currently available regarding student supervision, research grants, or laboratory affiliations.

Andrea Beatrix Csoba-Bodonyiمشاهده پروفایل

مدرس

• Data Visualization
• Data Science
• Machine Learning
• +۵ مورد دیگر

Andrea Beatrix Csoba-Bodonyi serves as a teaching assistant at the University of Debrecen, Faculty of Informatics, specifically within the Department of Data Science and Visualization. Her office is located in room I128 on the 1st floor of the Faculty of Informatics building at 4028 Debrecen, Kassai Street 26, with contact number +36 52 512 900 extension 75128. Her academic work aligns with the department's core research domains: Data Visualization Data Science Machine Learning and Deep Learning Image Processing Geospatial Information Systems Geometry and Computer Graphics The Department of Data Science and Visualization, led by Dr. András Hajdu (DSc, university professor), maintains an active research profile across these computational disciplines while delivering comprehensive educational programs. As a teaching assistant, Csoba-Bodonyi contributes to both undergraduate and graduate instruction within this dynamic academic environment. The department features a diverse faculty composition including multiple assistant professors, associate professors, and teaching assistants specializing in medical imaging, geometric modeling, cryptographic systems, and AI applications, creating a robust interdisciplinary research ecosystem at the University of Debrecen.

János Tóthمشاهده پروفایل

استادیار مدعو

• Ensemble Systems Optimization
• Big Data Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. János Tóth is an Adjunct Assistant Professor at the Department of Data Science and Visualization within the Faculty of Informatics at the University of Debrecen. His research focuses on optimization of ensemble systems and big data analytics. Institution: University of Debrecen School: Faculty of Informatics Department: Data Science and Visualization Rank: Adjunct Assistant Professor Email: toth.janos@inf.unideb.hu Research Interests: Dr. Tóth specializes in optimizing ensemble systems for medical image analysis and big data processing. His work spans: Parameter optimization techniques for ensemble models Integration of classical image processing with deep learning Medical data visualization solutions Applications in diabetic retinopathy detection Handling unstructured Hungarian medical reports Cloud-based machine learning pipelines Publication Trends: Over the past decade, Dr. Tóth's research has shown consistent focus on medical image analysis (particularly fundus and Pap smear imaging) combined with ensemble learning approaches. His work includes algorithm optimization (2014-2022), data visualization techniques (2012-2024), and natural language processing for Hungarian medical reports (2024). Recent publications (2024) demonstrate continued innovation in machine learning applications for both healthcare and business domains.

• Geoinformatics
• Civil Engineering
• Transportation Systems
• +۴ مورد دیگر

Dr. LOVAS Tamás is an Associate Professor and Head of the Department of Photogrammetry and Geoinformatics at the Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). He also serves as a Representative and Coordinator for the Specialization in Construction Information Technology Engineering (MSc) within the Faculty Council. His expertise spans geoinformatics, civil engineering, and transportation systems, with a focus on laser scanning, point cloud integration, and smart infrastructure solutions. Education: While formal education details are not explicitly provided, his academic role and publications suggest a strong background in civil engineering and geomatics. His courses include topics like Intelligent Transport Systems (BMEEOFTMF61), ITS GIS (BMEEOFTMF62), and Laser Scanning (BMEEOFTDT81). Research Interests: Dr. Lovász’s work centers on leveraging geospatial technologies for infrastructure analysis, including urban land cover classification , automated road surface segmentation , and scan-to-BIM workflows . He pioneers applications in transportation safety, autonomous vehicle HD mapping, and disaster risk assessment using RPAS and LiDAR. His team develops AI-driven strategies for point cloud data and digital twins, enhancing infrastructure monitoring and urban planning. Key Achievements: He has been awarded the Magyar Felvételi információ #építő250 ösztöndíj and contributed to projects like the ZalaZONE automotive proving ground’s HD mapping workflow. His research bridges academic innovation with practical engineering solutions, such as optimizing electric vehicle charging station placement and assessing rockfall hazards in volcanic regions. Grants & Advising: His work includes funded projects on laser scanning for bridge load testing and steel section inspection. While specific grant details are not listed, his publications reflect sustained engagement with industry-relevant research. He advises on complex construction IT projects and guides students through MSc specializations in construction information technology. Labs & Teams: His department collaborates with the Vásárhelyi Pál Doctoral School of Civil Engineering and Earth Sciences , focusing on geomatics, geotechnical engineering, and infrastructure systems. His team integrates cutting-edge technologies like LiDAR, photogrammetry, and AI to advance civil engineering practices.