جستجوی استاد راهنمای دانشگاهی

• Hydrogeology
• Karst Aquifers
• Groundwater Flow Modeling
• +۳ مورد دیگر

Dr. HAJNAL Géza is an Associate Professor at the Budapest University of Technology and Economics (BME), Faculty of Civil Engineering, where he serves in the Department of Hydraulic and Water Resources Engineering. His office is located in Room K. ép / mf. 12/7, and he can be contacted via email at hajnal.geza@emk.bme.hu or phone at +36 1 463 2362. His teaching portfolio includes active courses such as Hydraulic Engineering, Water Management (BMEEOVVAT43) and Hydrometric Field Course (BMEEOVVAI44). Previously, he taught Hidrogeology (BMEEOGMMET3) and Hydrogeology of Subsurface Water (BMEEOGMDT81). HAJNAL's research specializes in hydrogeology , with emphases on karst aquifer dynamics, groundwater flow modeling, climate impacts on hydrology, and hydraulic engineering. His work integrates field measurements (e.g., drip-water monitoring in Buda Castle Cave) with advanced numerical modeling to address complex hydrological challenges in Hungarian watersheds. Analysis of his 15 most recent publications (2013–2025) reveals dominant themes: 73% focus on karst/fractured aquifers , 20% on hydrological modeling techniques , and 7% on socio-environmental conflicts. Recurrent technical subfields include seepage flow validation, transmissivity determination, and rainfall-runoff sensitivity. No scientific awards, grants, student advisees, or lab affiliations are documented in the provided materials.

László Lengyelمشاهده پروفایل

استاد

• Model-Driven Engineering
• Internet of Things (IoT)
• Software Obfuscation
• +۷ مورد دیگر

László Lengyel is a Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics . His work bridges theoretical and applied computer science, focusing on industrial automation, IoT systems, and model-driven engineering. Research interests include Model transformations and domain-specific languages IoT device management and multi-domain integration Software obfuscation and cybersecurity Graph algorithms and distributed computing (MapReduce) Real-time data analysis in manufacturing Automotive sensor networks His recent publications reflect expertise in model-driven IoT architectures , granule manufacturing automation , and MapReduce-based graph analysis , with a focus on industrial and automotive applications. He contributes to open-source frameworks like SensorHUB and explores gamification in driver behavior systems.

Tamás Tettamantiمشاهده پروفایل

استاد

• Road Traffic Control
• Intelligent Transportation Systems
• Traffic Simulation
• +۳ مورد دیگر

Tamás Tettamanti is a Professor at Budapest University of Technology and Economics, serving as Deputy Head of the Department of Control for Transportation and Vehicle Systems. He holds a PhD (2013), Habilitation (2023), and DSc (2023) in Transportation Engineering, with expertise in road traffic modeling and control. 2007–2010: PhD Student 2010–2013: Assistant Lecturer 2014–2018: Senior Lecturer 2019–2024: Associate Professor 2025–present: Full Professor Education: High School Graduation (2001), DEUG (2004), MSc in Transportation Engineering (2007), Jazz Trumpet Graduate (2008) PhD (2013), Habilitation (2023), DSc (2023) Research focuses on road traffic modeling, intelligent transportation systems (ITS), autonomous vehicle integration, and emission-aware traffic control. His work bridges theoretical developments with real-world applications, including wireless traffic light systems and deep learning for urban mobility peaks. Scientific Awards: BME PhD Research Prize (2012) Literary Awards from Hungarian Scientific Association for Transport (2013, 2017, 2021, 2023) Bolyai János Research Scholarship (2017–2020) Michelberger Master Prize (2022) BME Jubilee Medal (2023) He has led major projects such as Dynamic Adaptive Traffic Control Services (2020–2024) and Deep Learning Anticipated Urban Mobility Peaks (DARUMA) (2021–2024), with industry collaborations including Google-BME Traffic Lab.

Balázs Vargaمشاهده پروفایل

پژوهشگر ارشد

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +۳ مورد دیگر

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

• Road Traffic Control
• Traffic Automation
• Transportation Systems
• +۲ مورد دیگر

Dr. Varga István is a Full Professor at the Budapest University of Technology and Economics (BME) in the Faculty of Transportation Engineering and Vehicle Engineering, and a Scientific Advisor at the HUN-REN Institute for Computer Science and Control. He holds a D.Sc. from the Hungarian Academy of Sciences (2020), a Ph.D. (2007), and an M.Sc. in Transportation Engineering (1998). His primary research focuses on road traffic control and traffic automation systems , with expertise in intelligent transportation solutions and vehicle mechatronics. His research integrates control theory with practical applications in urban mobility, including dynamic traffic light systems, autonomous vehicle impacts, and emission modeling. Recent work explores V2X communication, platooning technologies, and microscopic traffic simulation for urban optimization. He has led major projects such as the national innovation project 'Dynamic, adaptive traffic control services based on digital data sources' (2020-2024) and industrial collaborations with Knorr-Bremse, Siemens, and nuclear energy sectors. Awards include the Hungarian Academy of Sciences Young Prize (2007) and multiple recognitions for technological innovation. Educational activities include courses on Road Traffic Control, Mathematical Methods, and Vehicle System Modeling. Laboratory leadership includes the Road Traffic Control Lab with industry partnerships like Bosch and SWARCO.

Gergely Mezeiمشاهده پروفایل

دانشیار

• Model-driven engineering
• Multi-level modeling
• Formal verification
• +۶ مورد دیگر

Dr. Gergely Mezei is an Associate Professor and Deputy Head of the Department of Automation and Applied Informatics at the Budapest University of Technology and Economics (BME) , Hungary. He is affiliated with the Applied Computer Science Group within the department and is actively involved in research and education in model-driven engineering and formal methods. His research interests lie at the intersection of multi-level modeling , formal verification , and model transformation systems . He has contributed extensively to the development of modeling languages and tools, particularly in the context of DMLA (Deep Multi-Level Architecture) and Melanee , focusing on enabling rigorous, scalable, and verifiable modeling practices. Dr. Mezei's work emphasizes performance optimization in model transformations, visual modeling frameworks , and domain-specific languages . His research spans both theoretical foundations and practical tool implementations, with a strong focus on bridging the gap between formal semantics and usable modeling environments. His recent publications reflect a consistent focus on advancing the state-of-the-art in multi-level modeling and formal verification , with contributions to international workshops such as MULTI and MPM . These works explore challenges in model validation, transformation correctness, and the usability of modeling tools in complex systems engineering. Contact: Email: gmezei@aut.bme.hu Office: Q.B228, Budapest University of Technology and Economics, 1117 Budapest, Magyar tudósok krt. 2., Hungary Phone: +36 (1) 463-3491

• Robotics
• Mechatronics
• Control Theory
• +۴ مورد دیگر

Péter Korondi is a Professor at the University of Debrecen in Hungary, affiliated with the Department of Electrical Engineering and Mechatronics . His work spans robotics, control theory, and industrial automation, with a focus on bio-inspired systems and human-robot interaction. Research Interests : Robotics, Mechatronics, Control Theory, Human-Robot Interaction, Industrial Automation, Sensor Fusion Recent Article Trends : Sliding mode control, friction compensation in micro-telemanipulation, path planning for mobile robots, smart industrial systems Collaborations : Co-authored works with Gabor Sziebig, Ferenc Tajti, Géza Szayer, and international researchers in IEEE Transactions , Sensors , and Acta Polytechnica Hungarica . Technological Focus : Development of rehabilitation devices, holonomic drive systems, and ethorobotics models inspired by animal behavior.

Jürgen Zieglerمشاهده پروفایل

استاد

• Human-Computer Interaction
• Recommender Systems
• Explainable AI
• +۹ مورد دیگر

Jürgen Ziegler is a Senior Full Professor at the Department of Computer Science and Applied Cognitive Science within the Faculty of Computer Science at the University of Duisburg-Essen. He leads the Interactive Intelligent Systems Group, focusing on human-computer interaction, recommender systems, and explainable AI. His work emphasizes transparency, user control, and interdisciplinary collaboration with industry partners. He earned his doctoral degree from the University of Stuttgart, specializing in formal user interface design methodology. Prior to his current role, he headed the Competence Center for Software Technology and Interactive Systems at the Fraunhofer Institute for Industrial Engineering (IAO) in Stuttgart. He also served as Editor-in-Chief of the journal i-com - Journal of Interactive Media from 2001 to 2021. Ziegler’s research bridges academic and industrial domains, with applications in e-commerce, health, social media, and automotive systems. He investigates how to make intelligent technologies more transparent and user-controllable, particularly in conversational recommender systems, personalized interfaces, and social media analytics. His work integrates visualization techniques and semantic data models to enhance user experience. His recent publications focus on multistakeholder evaluation frameworks, explainable AI, and the integration of conversational agents with traditional interfaces. These studies explore domains like health promotion, smart environments, and education, using methods such as knowledge graphs, generative AI, and interactive sliders for feature dependency visualization. Ziegler founded and co-chairs the German Special Interest Group on User-Centred Artificial Intelligence. He has contributed to funded projects like SPIDER, FairWays, and PAnalytics, which address polarization in social networks, interactive recommendation, and health support systems. His leadership roles include organizing international workshops on explainable user models and user-centered AI. He supervises PhD students and advises on projects related to recommender systems, mental models, and user interaction preferences. His team collaborates on multi-method approaches for transparent decision support and the development of tools for measuring user perception of recommendation transparency. Labs and teams under his direction include the Interactive Intelligent Systems Group, which develops demonstrators like AR-based shopping advisors and hybrid recommendation frameworks. His work emphasizes both theoretical advancements and practical implementations across diverse application scenarios.

Charaf Hassanمشاهده پروفایل

استاد

• Distributed Systems
• Network Coding
• Internet of Things (IoT)
• +۱۲ مورد دیگر

Charaf Hassan is a Professor and Head of Department at the Budapest University of Technology and Economics, specifically in the Department of Automation and Applied Informatics. His work spans interdisciplinary domains, focusing on distributed systems, network coding, and IoT technologies. His research interests include Distributed Systems and Domain-Specific Modeling Network Coding and Mobile Peer-to-Peer Systems Model-Driven Development for Multiplatform Applications Machine Learning in Fluid Dynamics and Pharmaceutical Analysis Recent publications highlight trends in applying convolutional neural networks to viscosity estimation, model-driven methodologies for IoT, and network coding in cloud storage. He teaches advanced courses in distributed systems and software architectures at the university level.

Balázs Rakosمشاهده پروفایل

دانشیار

• Nanotechnology
• Biophysics
• Optical Computing
• +۵ مورد دیگر

Dr. Balázs Rakos is an Associate Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics . His research bridges nanotechnology, biophysics, and optical computing. His work focuses on: Integrating photoswitchable proteins with photonic devices for optical computing Developing infrared energy harvesting systems using nanoantennas and MIM diodes Modeling dipole-dipole and Coulomb-coupled protein arrays for molecular electronics Designing self-adapting pixel antenna systems for dynamic signal processing His publications from 2025–2015 reveal a trajectory from infrared sensor technologies to biophotonics and renewable energy applications , with a recurring emphasis on nanoscale biomolecular systems .

Attila Imreمشاهده پروفایل

استاد

• thermodynamics
• energetics
• physical chemistry
• +۹ مورد دیگر

Dr. Attila Imre is a Full-Time Professor in the Department of Power Machines and Systems at Budapest University of Technology and Economics (BME) . He also serves as a Part-Time Scientific Advisor at the MTA Energy Research Center . His research spans thermodynamics, energetics, physical chemistry, and ecology, with a focus on working fluids, phase transitions, and energy systems. Doctor of Science (DSc), Hungarian Academy of Sciences (2015) PhD in Physics, ELTE (1996) DrUniv in Solid-State Physics, ELTE (1994) MSc in Physics, ELTE Faculty of Physics (1990) Imre’s research integrates theoretical and applied studies in thermodynamic cycles, metastable fluids, and supercritical systems. He has pioneered work on working fluid selection, cycle efficiency, and cold energy utilization, particularly in organic Rankine cycles and power-to-methane technologies. His 15 most recent publications emphasize artificial intelligence in energy systems, economic analyses of sustainability, and thermodynamic modeling of extreme-state fluids. Key trends include AI-driven performance mapping, techno-economic evaluations of waste heat recovery, and ecological applications of fractal geometry. Scientific Awards : Humboldt Scholarship, Johannes Gutenberg University, Mainz, Germany (1999) Imre has held prestigious postdoctoral roles at the University of Tennessee and senior research positions in Hungarian institutions. His work bridges energy engineering, computational modeling, and interdisciplinary challenges in translation and cultural studies.

Csaba Hegedűsمشاهده پروفایل

دانشیار

• Operations Management
• Risk Management
• Quality Control
• +۳ مورد دیگر

Dr. Csaba Hegedűs is an active Associate Professor in the Department of Supply Chain Management at the University of Pannonia, Hungary, with office in Building "A", Room 121. He teaches core modules including Business Process Modeling and Re-engineering, Reliability and Risk Management, Quantitative Methods, and Production Quality Control, reflecting his operational research focus. Contact details are email: hegedus.csaba@gtk.uni-pannon.hu; phone: +36 88 624 – 106. Educational background: MSc in Engineering Management (2008) PhD (year unspecified) His research centers on Operations Management with interdisciplinary applications in industrial risk and uncertainty. Key areas include: Risk-based quality control systems Measurement uncertainty in conformity assessment Matrix-driven project planning frameworks Statistical process control under risk conditions Survival analysis of IT project management Decision support for industrial risk mitigation Publication trends (2013-2023) reveal sustained innovation in risk-modified control charts and matrix-based project libraries, bridging Operations Research, Statistics, and Industrial Engineering to address real-world uncertainty in quality decisions. Scientific awards: No documented awards in provided materials As Associate Professor, he likely supervises graduate research but no students are listed. His project experiences and GMT consultation role suggest applied industry collaborations, though specific grants or advising details remain unreported. Consultation hours require email pre-negotiation (Mondays 15:00-16:00). No dedicated laboratories or formal research teams are referenced in the available information.

Asztalos Márkمشاهده پروفایل

دانشیار

• Model-Driven Engineering
• Software Engineering
• Graph Rewriting Systems
• +۳ مورد دیگر

Márk Asztalos is an Associate Professor at the Budapest University of Technology and Economics , affiliated with the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . He leads research in the Visual Modeling Group, focusing on model-driven engineering, graph rewriting systems, and domain-specific languages. Research Interests: Model transformation verification, text-based modeling, graph pattern matching, and cloud/mobile system modeling. Contact: E-mail: Asztalos.Mark@aut.bme.hu , Office: Q.B226, Department of Automation and Applied Informatics, BME. His publications emphasize model transformation verification (2010-2015), graph rewriting techniques for pattern matching (2015-2017), and domain-specific language design (2014). Recent work (2020) analyzes model integration challenges in model-driven methodologies. Contact details: Address: Budapest 1117, Magyar tudósok krt. 2, Hungary Phone: +36 (1) 463-3702

• Geotechnical Engineering
• Rock Mechanics
• Tunneling
• +۳ مورد دیگر

Dr. Vásárhelyi Balázs is an Associate Professor at the Department of Engineering Geology and Geotechnics, Budapest University of Technology and Economics (Faculty of Civil Engineering). He has been teaching courses such as Engineering geological and geotechnical project , Geology , Tunneling , and Rock Mechanics since the early 2010s, with a focus on rock mass characterization and geotechnical stability. Position: Associate Professor Department: Engineering Geology and Geotechnics Email: vasarhelyi.balazs@emk.bme.hu Office Hours: Mondays 11-12 His research connects geological strength parameters with engineering applications through projects like the Mátra Gravitational and Geophysical Laboratory measurements. He has explored the relationships between Geological Strength Index (GSI) , Hoek-Brown criterion , and Q-system for rock mass classification, with particular emphasis on granitic and sedimentary rocks. Recent publications analyze: Stress thresholds for crack initiation in granitic rocks using machine learning Thermal effects on limestone and sandstone mechanical properties Modified RQD applications for rock mass characterization Brittle-to-ductile transition stress in rock masses Impact resistance correlations with compressive strength Scientific recognition includes the Építő250 Scholarship . His work spans empirical, analytical, and numerical approaches to geotechnical challenges in tunneling and slope stability.

Gábor Kövesdánمشاهده پروفایل

مدرس

• Software modernization
• Model-driven engineering
• Domain-specific languages
• +۲ مورد دیگر

Gábor Kövesdán is an Assistant Lecturer at the Budapest University of Technology and Economics (BME) , working within the Department of Automation and Applied Informatics and the Applied Computer Science Group . His primary focus lies in software engineering, particularly in areas such as software modernization, model-driven engineering, and domain-specific languages. His research interests encompass: Software Modernization - Leveraging machine learning techniques to modernize legacy systems Model-Driven Engineering - Developing frameworks and tools for model-based software development Domain-Specific Languages (DSL) - Creating specialized languages and code generation frameworks for various application domains Multi-Platform Development - Enabling code generation across different platforms and architectures His work demonstrates a strong emphasis on bridging theoretical concepts with practical applications, particularly in developing tools and methodologies that enhance software development efficiency and maintainability. His recent publications (2014-2021) show a consistent trajectory in advancing software engineering practices, with particular strength in model-driven approaches, code generation techniques, and the application of design patterns in software development. The research spans from foundational work on DSL classification to practical implementations like Android development frameworks.