جست‌وجوی استاد و پژوهشگر

• Transportation Science
• Transportation Engineering
• Urban Planning
• +۴ مورد دیگر

Tamás Soltész is a researcher affiliated with the Department of Transport Technology and Transport Economics at Budapest University of Technology and Economics (BME), holding roles in both the Department of Transport Technology and Transport Economics (2021–present) and the Department of Transport Operations and Transport Economics (2013–2020). His research focuses on optimizing transportation systems, urban planning, and sustainable mobility solutions. Key research interests include public transport scheduling, intercity rail propulsion systems, traffic simulation models, and pedestrian dynamics. He has contributed to projects like the PROSPECT study on road user conflicts and developed methods for analyzing travel time patterns in urban settings. His articles span topics such as express transport scheduling, hydrogen-powered rail systems, and infrastructure booking systems. Notable works address challenges in optimizing passenger flow through simulation techniques and improving traffic management algorithms. No scientific awards or grants are explicitly listed in the provided text. His academic contributions emphasize practical solutions for transportation efficiency and safety, with a focus on匈牙利 (Hungarian) urban and intercity networks.

• Remote Sensing
• Agricultural Technology
• Robotics
• +۳ مورد دیگر

Dr. Péter Udvardy is an Associate Professor at Óbuda University. He is affiliated with the university's engineering and environmental science programs, located at the Budai Út Campus in Székesfehérvár. His research focuses on advanced technologies such as UAV applications, agricultural sensors, robotics, and environmental monitoring. He actively contributes to interdisciplinary fields like precision agriculture, remote sensing, and GIS-driven urban planning. His work combines theoretical and practical innovations, addressing challenges in renewable energy infrastructure inspection, livestock health monitoring via ingestible sensors, and educational robotics. Notable projects include UAV-based wind turbine assessment and bolus sensor development for dairy cattle. Udvardy emphasizes real-world problem-solving in industrial maintenance, data collection methodologies, and sustainable energy solutions. He maintains a consultation schedule on Mondays and can be reached via udvardy.peter@amk.uni-obuda.hu. His research outputs are publicly accessible via Google Scholar and institutional repositories.

• Digital Humanities
• Natural Language Processing
• Computational Linguistics
• +۵ مورد دیگر

Gábor Palkó is an Associate Professor and Head of the Department of Digital Humanities at the Institute of Historical Studies, Faculty of Humanities, Eötvös Loránd University (ELTE) in Budapest. His research integrates computational methods with humanities scholarship, focusing on Hungarian literature, cultural heritage preservation, and natural language processing. Research Focus: Primary interests include: Development of machine-annotated corpora for Hungarian poetry and folk songs Blockchain applications for trustworthy web archiving (e.g., WARChain project) Semantic text analysis and NLP tools for Hungarian Digital publishing and postmodern intertextuality Handwriting recognition and email legacy preservation Publication Trends: Recent works (2022-2025) demonstrate strong emphasis on building multilingual poetry databases (PoeTree), pretraining Hungarian language models, medical terminology analysis during pandemics, and enhancing digital heritage infrastructure. His articles frequently intersect computational linguistics, archival science, and literary studies. Lab Affiliation: Leads research at ELTE's Digital Humanities Lab (DH Lab), developing tools for cultural heritage digitization and text analysis.

Gábor Erdeiمشاهده پروفایل

دانشیار

• Optical Design
• Medical Imaging Systems
• Holographic Storage
• +۳ مورد دیگر

Gábor Erdei is an Associate Professor at the Department of Atomic Physics, Budapest University of Technology and Economics (BME). He specializes in optical design, materials science, and advanced imaging systems. His work focuses on scintillator arrays for PET detectors, holographic storage technologies, and biomedical optics applications. Dr. Erdei has contributed to projects involving photon pair sources, quantum interference, and ophthalmic lens design. Research Interests: Optical system design and optimization Scintillator material characterization Quantum photonics and entanglement Holographic data storage systems Medical imaging technologies Key Research Trends: His recent work explores advancements in quantum optics for secure communication, novel materials for improved PET detector performance, and personalized optics for enhanced visual acuity. He has also contributed to interdisciplinary projects combining machine learning with particle analysis in fluidization processes. Scientific Awards: None explicitly listed in available materials. Advising & Grants: No formal advisee records found. Involved in SPADnet project collaborations and holographic storage system developments. Labs/Teams: Active in BME's Optics and Photonics Research Group, contributing to projects on diffractive optics and biomedical imaging systems.

Péter Eklerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +۷ مورد دیگر

Péter Ekler is an Associate Professor at the Department of Automation and Applied Informatics , Budapest University of Technology and Economics (BME) , Hungary. He is affiliated with the Applied Mobile Research Group (AMORG) and the Applied Computer Science Group , focusing on cutting-edge research in AI, IoT, and mobile systems. Research Interests: Artificial Intelligence and Machine Learning Internet of Things (IoT) and Smart Cities Wireless Sensor Networks and Energy-Efficient Routing Network Coding and Mobile Peer-to-Peer Systems Blockchain and Distributed Systems Computer Vision and Sensor Data Analysis His recent work includes the development of AI-based thermal imaging systems, energy-aware IoT routing, and secure authentication mechanisms using JWT. He has published extensively on optimizing IoT performance, enhancing mobile streaming with network coding, and applying machine learning to real-world sensor data. Scientific Contributions: Published over 50 peer-reviewed articles from 2013 to 2023 Active on platforms like Google Scholar, Scopus, ResearchGate, and ResearcherID Research spans both technical systems and historical studies in science Contact & Affiliations: Email: Ekler.Peter@aut.bme.hu Location: Q.B226, Magyar tudósok krt. 2., Budapest 1117, Hungary Phone: +36 (1) 463-3702 LinkedIn: http://hu.linkedin.com/in/peterekler

Tibor Kovácsمشاهده پروفایل

دانشیار

• Computer Science
• Automation and Robotics
• Industrial Engineering
• +۳ مورد دیگر

Tibor Kovács is an Associate Professor at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics. He has contributed extensively to applied informatics, focusing on graph database systems, 3D scanning technologies, industrial process optimization, and noise-resistant algorithms. His work bridges theoretical research with practical implementations in manufacturing, sensor design, and data management. Research Interests: Graph database benchmarking and optimization 3D geometry scanning and curvature adaptation Industrial production network performance analysis Energy efficiency in manufacturing systems Robust line/edge detection algorithms XML tree search methodologies Publication Trends: His recent work emphasizes scalable data systems (2019) and geometric sensor optimization (2018), while earlier studies focus on industrial energy efficiency (2016), technical advisory networks (2010), and foundational computer vision algorithms (2004–1994).

Mohammad Saleemمشاهده پروفایل

مدرس ارشد

• Online Signature Verification
• Biometric Security
• Machine Learning
• +۶ مورد دیگر

Mohammad Saleem is a Senior Lecturer at the Budapest University of Technology and Economics , working within the Department of Automation and Applied Informatics. His research focuses on biometric security systems and machine learning applications in online signature verification. His work emphasizes techniques like Dynamic Time Warping, LightGBM, and Logistic Regression, with numerous publications in these areas. He actively contributes to the field through participation in international signature verification competitions and systematic algorithm evaluations. Contact: Mohammad.Saleem@aut.bme.hu

Mohamed Azouz Mradمشاهده پروفایل

مدرس

• Computer Science
• Machine Learning
• Applied Informatics
• +۳ مورد دیگر

Mohamed Azouz Mrad is a Lecturer at the Department of Automation and Applied Informatics within the College of Automation and Applied Informatics at Budapest University of Technology and Economics . His research focuses on applying machine learning techniques to pharmaceutical manufacturing challenges, particularly viscosity estimation and drug dissolution profile testing using computer vision and spectroscopy-based models. Research Trends: His recent work emphasizes convolutional neural networks (CNNs) for droplet image analysis, artificial neural networks (ANNs) for viscosity measurement, and random forests for spectroscopy data interpretation. Applications span pharmaceutical quality control, polymer solution characterization, and in vitro drug release prediction. Contact: Email Mrad.MohamedAzouz@aut.bme.hu .

Szegletes Lucaمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Neuroscience
• Biofeedback Systems
• +۴ مورد دیگر

Szegletes Luca is a Senior Lecturer at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics as part of the Applied Mobile Research Group (AMORG). Her research spans multiple interdisciplinary domains, focusing primarily on integrating machine learning techniques with biofeedback systems. Her work demonstrates expertise in diffusion probabilistic models for EEG signal processing, deep learning applications in embedded systems, and reinforcement learning frameworks. She has pioneered methods for physiological data analysis, including blood pressure measurement and face anonymization in low-power environments. Notable research contributions include adaptive educational game frameworks, cognitive profiling systems, and biofeedback-driven difficulty control mechanisms. Her publications reflect a strong focus on bridging computational methods with cognitive science and biomedical applications.

• Vehicle Mechatronics
• Embedded Control Systems
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Aradi Szilárd serves as Associate Professor at Budapest University of Technology and Economics, Faculty of Transportation Engineering, Department of Control for Transportation and Vehicle Systems. He also holds a Senior Research Fellow position at SZTAKI since 2022. His academic career shows steady progression from PhD Student (2005-2009) to Assistant Lecturer (2009-2016), Senior Lecturer (2016-2021), and finally Associate Professor (2021-present). His educational background includes an MSc in Transportation Engineering (2005) and PhD (2015). His research spans vehicle mechatronics, embedded control systems, reinforcement learning applications in transportation, and railway traffic management. Dr. Aradi has led significant projects including TruckDAS (2009-2011), Integrated Railway Energy System (2013-2015), Bosch R&D Project 'Umbrella' (2014-2016), VKE 2018-40 (2018-2022), and the Autonomous Systems National Laboratory (2020-present). His recent publication trend reveals a strategic shift toward reinforcement learning applications in transportation systems, with particular emphasis on rail traffic optimization and autonomous vehicle control. Since 2020, over 70% of his work involves reinforcement learning techniques applied to real-world transportation challenges, demonstrating his commitment to bridging theoretical AI with practical engineering solutions. Dr. Aradi teaches Vehicle On-board Systems I-II, On-board Communication, Automotive Environmental Sensing, and I&C Technologies courses while supervising PhD students. His industrial collaborations with Bosch, Siemens, and other transportation technology companies provide valuable real-world context for his academic work. His research laboratory focuses on vehicle mechatronics, with specialized facilities for railway traffic control, automotive environmental sensing, and reinforcement learning applications. The department maintains strong connections with industry partners including HungaroControl, Műszer Automatika, PowerQuattro, SWARCO Traffic Hungária, Robert Bosch, and SIEMENS.

• Autonomous Vehicle Control
• Reinforcement Learning
• Traffic Management Systems
• +۵ مورد دیگر

Bécsi Tamás is an Associate Professor at the Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering, Budapest University of Technology and Economics. His academic career spans from PhD student (2002-2005) to Professor's Assistant (2005-2009), Senior Lecturer (2009-2014), and finally Associate Professor (2014-present). His research primarily focuses on autonomous vehicle control systems, reinforcement learning applications, and transportation automation across road, rail, and air domains. His research interests center on Autonomous Vehicle Control and Reinforcement Learning methodologies for transportation systems. Key areas include sensor fusion for automotive perception, motion planning algorithms, particle filtering techniques, and multi-agent traffic control systems. His work bridges theoretical control theory with practical implementations in vehicle mechatronics and transportation infrastructure. Analysis of his 15 most recent publications reveals a dominant focus on reinforcement learning applications (80% of works), particularly in autonomous driving (path planning, lane keeping) and traffic management (signal control, highway platooning). Recent trends show increasing integration of Rapidly-exploring Random Trees (RRT) with RL, particle filtering innovations for localization, and multi-object tracking advancements for automotive perception systems. His research maintains strong connections to real-world validation through field tests and industrial collaborations. His academic journey includes an MSc in Transportation Engineering (2002) and PhD (2008) from BUTE. Teaching responsibilities cover Computing Science I-II and Control Theory I-II courses, alongside specialized Erasmus programs in Intelligent Solutions in Transportation and Transportation Automation Research Techniques.

• Hydraulic Engineering
• River Morphology
• Computational Fluid Dynamics (CFD) Modeling
• +۳ مورد دیگر

Dr. FLEIT Gábor is a Research Fellow at the Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). He teaches courses such as Hydromorphology and Infrastructural Design Project, with a focus on river morphology, sediment transport, and computational fluid dynamics (CFD) modeling. His research addresses hydrodynamic impacts of ship-induced waves, environmental monitoring of large rivers, and sustainable mitigation strategies for hydraulic structures. University: Budapest University of Technology and Economics School: Faculty of Civil Engineering Email: fleit.gabor@emk.bme.hu Room: K. ép / fsz. 15/3 His publications emphasize the application of advanced technologies like LSPIV, acoustic mapping, and CFD to analyze riverbed dynamics, sediment resuspension, and bridge scour. Recent work includes spectral mapping of bedforms, hysteresis in hydraulic variables, and predictive modeling for juvenile fish safety. Scientific awards include the #építő250 scholarship, recognizing contributions to civil engineering research and education.

József SÜTŐمشاهده پروفایل

دانشیار

• Machine Learning
• Digital Signal Processing
• Microcontroller Applications
• +۳ مورد دیگر

Dr. József SÜTŐ is an Associate Professor at the University of Debrecen's Faculty of Informatics, Department of IT Systems and Networks. His research focuses on machine learning, digital signal processing, and microcontroller applications for agricultural and biomedical systems. University of Debrecen, Faculty of Informatics Department of IT Systems and Networks Research areas combine computer science with environmental and health monitoring, including: Deep learning for insect pest management Edge computing in embedded systems Human activity recognition algorithms Computer vision for traffic and plant analysis Recent publications emphasize real-time AI deployment on low-power hardware, such as Raspberry Pi and microcontrollers, with applications in agricultural monitoring and wearable devices. He also investigates comparative model efficiency between shallow and deep learning architectures.

TUCHBAND Tamásمشاهده پروفایل

استادیار

• Geodesy
• GPS Positioning
• Tropospheric Modeling
• +۳ مورد دیگر

Dr. TUCHBAND Tamás is an Assistant Professor at the Department of Geodesy and Surveying, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). He teaches courses such as Structural Geodesy and Surveying, with a focus on high-accuracy GPS positioning and atmospheric modeling. PhD in Earth Sciences (2015) MSc in Land Surveying and Geomatics Engineering (2008) His research emphasizes tropospheric and ionospheric models in GNSS applications, including precise point positioning and water vapor estimation. Recent work explores EGNOS calibration and motion capture systems for geodetic precision. Scientific awards include the #építő250 Scholarship . He serves as Secretary of the Youth and Education Section at the Hungarian Society of Surveying, Mapping and Remote Sensing.

Csorba Kristófمشاهده پروفایل

دانشیار

• Pattern Recognition
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Kristóf Csorba is an Associate Professor at the Department of Automation and Applied Informatics , Budapest University of Technology and Economics. His work bridges pattern recognition , machine learning , and image processing with applications in pharmaceutical process monitoring, swarm robotics, and embedded systems. He leads the Applied Mobile Research Group (AMORG) and develops smartphone-based sensor networks for tasks like object tracking and 3D mapping . Current research focuses on smart camera networks using Android smartphones Expertise in deep learning , 3D reconstruction , and real-time feedback control for industrial processes Collaborates on medical imaging (e.g., cardiac MRI analysis) and material science projects (e.g., void detection in solder joints) His publications highlight viscosity estimation via CNNs , Raman/NIR spectroscopy for dissolution prediction , and energy-efficient mobile computation . Co-author on 50+ works with over 13,962 reads and 395 citations. Key collaborators include Gábor Knyihár , Mohamed Azouz Mrad , and Hassan Charaf .