جست‌وجوی استاد و پژوهشگر

Márk Jelasityمشاهده پروفایل

استاد

• Distributed Systems
• Peer-to-Peer Computing
• Gossip Protocols
• +۷ مورد دیگر

Márk Jelasity is a Full Professor in the Department of Algorithms and AI at the University of Szeged, Hungary, where he has been working since 2016. Previously, he served as a research advisor (equivalent to full professor) and senior research scientist at the Research Group on Artificial Intelligence (RGAI) of the Hungarian Academy of Sciences. His career includes numerous international research positions at institutions in Sweden, Norway, France, Italy, and the Netherlands. Professor Jelasity's research spans distributed systems, peer-to-peer computing, gossip protocols, and decentralized machine learning. His work bridges theoretical foundations with practical applications, particularly in the areas of self-organizing systems and privacy-preserving computation. His research has significant implications for smart grid technologies, secure distributed systems, and robust machine learning. His publication record shows a clear evolution from foundational work in gossip protocols and peer-to-peer systems toward cutting-edge research in decentralized machine learning, adversarial robustness, and privacy-preserving AI. Recent publications demonstrate his leadership in comparing gossip learning with federated learning approaches and exploring novel techniques for enhancing robustness in neural networks. Bolyai Plaquette (2015) 10 years best paper award at ACM/IFIP/USENIX Middleware Conference (2014) Best paper award at IEEE International Conference on Peer-to-Peer Computing (2014) Best paper award at IEEE International Conference on Self-Adaptive and Self-Organizing Systems (2013) Scientific Award of the Faculty of Science and Informatics, University of Szeged (2013) Fulbright Scholarship to visit Cornell University (2013) Multiple Bolyai Scholarships (2007-2014) Professor Jelasity has been actively involved in the academic community as an organizer of major conferences including DAIS'16 (TPC co-chair), SASO 2010 (General Co-Chair), and SASO 2007 (TPC co-chair). His leadership in the field is evidenced by his extensive publication record in top venues and his role in editing special issues and conference proceedings.

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.

Balázs Nagyمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Balázs Nagy serves as an Associate Professor and Head of the Department of Medieval History within the Faculty of Humanities at Eötvös Loránd University (ELTE) in Budapest, Hungary. His academic profile uniquely bridges historical scholarship and advanced engineering disciplines, maintaining an active research agenda across both domains from his office at 1088 Budapest, Múzeum körút 6–8. His primary research interests span Robotics , Artificial Intelligence , Machine Learning , and Ethorobotics , alongside History and Medieval Studies . This interdisciplinary focus manifests in work on deep learning for telerobotic control, evolutionary algorithms for robot navigation, and ethologically inspired behavior systems, often integrating sensor technologies like MARG for movement analysis. Analysis of his 2016-2025 publications reveals a consistent trajectory in computational intelligence applied to robotics, with increasing emphasis on deep learning (2022-2025) and ethorobotics. His work demonstrates strong methodological continuity in sensor fusion and behavior modeling, while showing evolving applications from mobile navigation to biological behavior analysis.

• 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.

Gergely Vakulyaمشاهده پروفایل

دانشیار

• Cybersecurity
• Agricultural Technology
• Sensor Networks
• +۳ مورد دیگر

Dr. Gergely Vakulya is an Associate Professor and Research Fellow at Óbuda University. He specializes in interdisciplinary research spanning cybersecurity, agricultural technology, sensor networks, and image processing. His work integrates hardware design, algorithm development, and real-world applications. Dr. Vakulya’s recent focus includes developing rumen bolus sensors for dairy cattle health monitoring, gamification of cybersecurity training, and innovative methods in camera exposure time measurement. He is affiliated with Óbuda University’s Budai Road and Pirosalma Street campuses, with an office at building F room 316. His research frequently addresses challenges in data collection, sensor fusion, and embedded systems. Research Interests: Dr. Vakulya’s expertise includes cybersecurity frameworks (e.g., CTF challenges), agricultural IoT systems (e.g., rumen bolus sensors for livestock monitoring), and image processing techniques (e.g., genetic algorithms for shape approximation). His work on visible light communication (VLC) and wireless sensor networks highlights his contributions to communication protocols and energy-efficient systems. Recent trends in his publications emphasize cross-disciplinary approaches, such as applying AI methods to agricultural sensor data and optimizing sensor networks for real-time applications. Advising & Grants: No specific advising relationships or grants are listed in available materials. His research infrastructure is likely supported through institutional and collaborative projects. Labs/Teams: While not explicitly stated, his research likely involves collaborations within Óbuda University’s engineering and computer science departments. His work on rumen sensors and VLC systems suggests potential affiliations with robotics, biomedical engineering, or smart agriculture research groups.

• 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.

Márta Seebauerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Science
• Civil Engineering
• +۵ مورد دیگر

Dr. Márta Seebauer is an Associate Professor at Alba Regia Faculty of Óbuda University. Her research spans interdisciplinary areas including artificial intelligence applications in energy systems, structural engineering reliability analysis, financial risk modeling, and sensor technology for food quality assessment. She maintains an active publication record with work showcased in IEEE symposiums and specialized journals. Email: seebauer.marta@uni-obuda.hu Consultation Hours: Thursdays 14:00-15:00 Her research interests emphasize practical applications of computational methods in civil, environmental, and smart city technologies. Recent work includes neural network-based energy monitoring systems and asbestos risk mitigation strategies during building demolition.

Gyula Simonمشاهده پروفایل

استاد

• Sensor Networks
• Localization Systems
• Signal Processing
• +۳ مورد دیگر

Prof. Gyula Simon is a faculty member at Óbuda University's Alba Regia Faculty, where he holds the rank of Professor. His research focuses on sensor networks, localization systems, and signal processing. He has contributed extensively to advancements in technologies like TDOA-based positioning, VLC communication, and acoustic source tracking. With over two decades of academic work, his publications span applications ranging from agricultural sensors to fault-tolerant indoor positioning systems. Key research interests include developing robust localization algorithms for both outdoor and indoor environments, optimizing wireless sensor networks for energy efficiency and reliability, and integrating emerging technologies like visible light communication (VLC) for beaconing and tracking. His work bridges theoretical signal processing with practical implementations in fields like smart agriculture and security systems. Recent articles highlight innovation in hyperbolic localization methods, sparse sampling techniques for camera systems, and semi-automatic plant growth monitoring. Despite prolific publication output, no scientific awards are explicitly listed. Consultation hours are arranged via email at simon.gyula@amk.uni-obuda.hu, with his office located at Building F Room 316 in Székesfehérvár.

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.

• High-Performance Data Transfer
• Parallel Data Processing
• Memory-Safe Programming (Rust)
• +۳ مورد دیگر

Róbert Tornai serves as an Associate Professor in the Department of Data Science and Visualization at the Faculty of Informatics, University of Debrecen, Hungary. His institutional affiliation encompasses active participation in the department's core mission of advancing data processing, visualization, and computational methodologies within Hungary's academic landscape. His primary research focuses on high-performance data transfer in supercomputing environments, parallel data processing using memory-safe Rust programming, and virtual collaboration system development. These interconnected domains emphasize optimizing data-intensive workflows while ensuring system security and user accessibility, reflecting contemporary challenges in distributed computing infrastructure. Analysis of his 15 most recent publications reveals dominant trends in high-speed connectionless networking protocols (2020-2025), where he investigates performance optimization, error detection, and encryption for file transfer systems. Significant secondary themes include biometric security applications (iris/voice recognition) and GPU-accelerated image processing techniques leveraging WebAssembly and Vulkan API, demonstrating technical versatility across networking, security, and visualization domains. His scholarly output consistently addresses practical implementation challenges in data transfer and secure systems, with recent work extending into educational technology applications of 3D printing. This trajectory indicates sustained engagement with evolving computational paradigms while maintaining focus on real-world system performance and security requirements.

• 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.

László Kovácsمشاهده پروفایل

استادیار

• Digital Image Processing
• Medical Image Analysis
• Retinal Image Analysis
• +۷ مورد دیگر

László Kovács serves as an Assistant Professor at the Faculty of Informatics, University of Debrecen, where he focuses on data science research and development. He is also recognized as an Nvidia Deep Learning Institute Certified Instructor and Ambassador, contributing to the advancement of AI education. His work is prominently featured through the Autonomous Vehicle Research Lab at the University of Debrecen. His primary research interests include Digital Image Processing , Fusion Systems , and High Performance Computing , with particular emphasis on medical applications. Kovács has developed data-driven solutions that address market, industrial, and social demands of artificial intelligence across various sectors including healthcare. His work demonstrates how the Faculty of Informatics at the University of Debrecen prioritizes data science in both educational and research contexts. Analysis of his publication record reveals a strong focus on medical image analysis, particularly in ophthalmology applications. His research consistently applies advanced computational techniques to solve diagnostic challenges in retinal imaging and melanoma detection. The publications show a progression from foundational algorithm development to practical medical applications, demonstrating his commitment to translating theoretical computer science into real-world healthcare solutions. Scientific Awards and Recognition: Ányos Jedlik PhD candidate scholarship of the National Excellence Program (2014-2015) János Csere Apáczai PhD scholarship of the National Excellence Program (2013-2014) Uiversitas scholarship from University of Debrecen (2012) Special prize at XXX-th National Scientific Students' Associations (2012) Prize of the Dean for Masters Students (2010) Kovács has supervised PhD research projects including OTKA NK101680 on melanoma detection and HURO/1001/283/2.3.1 on image-based recommendation systems. His teaching portfolio is extensive, covering High Level Programming Languages, High Performance Parallel Computing Tools, Parallel Image Processing, and Data Science. He has also established thesis topics in distributed programming, fusion systems, high-performance computing architectures, and medical diagnostic applications. His research group maintains an active GitHub presence through the Autonomous Vehicle Research Lab.

Szilvia Szeghalmyمشاهده پروفایل

مدرس

• Digital Image Processing
• Imbalanced Learning
• Machine Learning
• +۳ مورد دیگر

Dr. Szilvia Szeghalmy is a Lecturer at the University of Debrecen, Faculty of Informatics, Department of Computer Science. She can be contacted at +36 52 512 900 75116 or via email at szeghalmy.szilvia@inf.unideb.hu, located in room I116, floor 1 of the Faculty of Informatics building. Her research focuses on: Digital Image Processing Imbalanced Learning Recent publications highlight trends in: Imbalanced data handling and noise filtering techniques (2024) Medical imaging applications (2024) Driver-assistance systems and traffic sign recognition (2018-2019) Human-computer interaction using sensors (2013-2014)

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.

Jozsef Fiserمشاهده پروفایل

استاد

• Cognitive Science
• Visual Statistical Learning
• Perceptual Decision-Making
• +۵ مورد دیگر

Jozsef Fiser is a Professor in the Department of Cognitive Science at Central European University (CEU), where he serves as Director of the Center for Cognitive Computation. His research bridges human psychophysical experiments, computational modeling, and neurophysiological studies to explore how visual statistical learning shapes internal representations for cognition and behavior. He holds advanced degrees in Psychology (D.Sc., Hungarian Academy of Sciences; Dr. habil., Janus Pannonius University), Computer Science (Ph.D., University of Southern California), and Electrical Engineering (Diploma, Technical University of Budapest). D.Sc. in Psychology, Hungarian Academy of Sciences (2007) Dr. habil. in Psychology, Janus Pannonius University (2004) Ph.D. in Computer Science, University of Southern California (1997) M.A. in Psychology, University of Southern California (1992) Diploma in Electrical Engineering, Technical University of Budapest (1986) His research focuses on hierarchical object representations, active learning, probabilistic computation in the brain, and the link between beliefs and knowledge. Key themes include temporal processing, cross-modal integration, attention, and neural variability during perceptual learning. His work often examines how low-level visual processes (e.g., orientation coding) relate to higher-level cognitive functions. Analysis of his recent publications reveals trends in visual statistical learning , probabilistic inference , and neural sampling as frameworks for understanding cognition. Many studies investigate how uncertainty, memory consolidation, and attention modulate learning across species (humans, honeybees, chicks) and contexts (naturalistic vision, sleep effects). As Director of CEU's Center for Cognitive Computation, Fiser leads interdisciplinary research linking computational models with empirical studies of perception, decision-making, and neural dynamics.