Ildikó Papp is an Associate Professor at the Faculty of Informatics, University of Debrecen , specializing in the Department of Data Science and Visualization . Her academic focus spans geometric modeling , 3D technologies , and experience-oriented teaching methods , bridging theoretical geometry with practical digital applications. Research Interests: Computer modeling of curves and surfaces Constructive and representational geometry 3D printing and visualization Cognitive infocommunication in education Publication Trends: Focus on QR code integration with 3D surfaces (2021-2022) Biomedical applications of FDM printing (2020) Educational impact of 3D technologies (2016-2018) Advancements in Bézier curve isoptics (2012-2013) Optimization techniques in geometric modeling (2012) Contact: Email: papp.ildiko@inf.unideb.hu
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.
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.
Márton Pósfai is an Assistant Professor at the Central European University (CEU), specializing in network science and statistical physics. His research focuses on the structural and dynamic properties of complex networks, particularly physical networks, their controllability, and interdependencies. He holds a PhD and MSc in Physics from Eötvös Loránd University, Budapest. His work explores interdisciplinary topics including the impact of physical constraints on network topology, resilience under damage, and social behavior in non-human primates. Notable projects include the DYNASNET initiative to understand and utilize network structures. Pósfai’s publications span theoretical models of network dismantling, multiplex centrality metrics, and algorithmic instabilities in ranking systems. Key research trends in his articles include analyzing physical network structures (e.g., 3D shape, bundling effects), controllability strategies (input node placement, longest control chains), and social dynamics influenced by resource access. His studies often bridge abstract network theory with empirical systems like primate societies and Bitcoin transaction networks. Advising and grants are not explicitly detailed in the provided materials. Pósfai maintains a lab or team focused on network science applications, though specific lab names are unspecified.
Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at Central European University (CEU). He also serves as a Research Professor at the Rényi Institute of Mathematics (Hungary) and Editor-in-Chief of Advances in Complex Systems . His work focuses on computational social science, human dynamics, and data-driven modeling of socioeconomic systems. Karsai holds advanced degrees including a DSc from the Hungarian Academy of Sciences and an HDR (Habilitation) in Computer Science from École Normale Supérieure de Lyon. His research integrates temporal networks, human mobility, and social contagion phenomena, often using large-scale datasets from digital platforms and wearable sensors. Notable projects include studies on evacuation behavior during disasters, vaccination hesitancy, and urban socioeconomic stratification. Karsai leads interdisciplinary initiatives like the DyLNet project, which examines social interactions and language development in preschool environments through sensor technology. Recent publications highlight innovations in network clustering algorithms (PASCO), epidemic modeling with generalized contact matrices, and the application of machine learning to infer socioeconomic status from satellite imagery. His work bridges computational methods with real-world challenges in public health, urban planning, and humanitarian development.
Dr. Zsuzsanna Hauck serves as an Associate Professor at the University of Pécs, currently on permanent absence status while maintaining academic engagement through email correspondence. Her career spans doctoral recognition (2015), habilitation (2021), and active research contributions through 2024. Her academic credentials include: Graduation in 2010 with dual degrees in Financial Instruments and Institutions and English-Hungarian Translation from the University of Pécs Doctoral dissertation (2015) awarded summa cum laude and international recognition Habilitation completed in 2021 Research focuses on inventory and production management systems with emphasis on quality control optimization, supply chain dynamics, and learning curve modeling. Her methodology bridges theoretical frameworks with real-world corporate challenges through industry partnerships, particularly examining defective item management and semiconductor industry applications. Publication trends (2015-2024) reveal consistent advancement in lot sizing models with defective items, evolving toward integrated quality-pricing decisions and semiconductor supply chain innovations. Works appear in premier journals like Omega and International Journal of Production Research, demonstrating rigorous quantitative approaches to production economics. Scientific recognition includes: Summa cum laude distinction for doctoral dissertation International award from the International Conference on Industrial Engineering Dr. Hauck teaches production management courses in Hungarian and English at undergraduate and graduate levels, emphasizing practical problem-solving through corporate collaborations. As board member of the Economic Modeling Society since 2016, she contributes to international research networks while developing pedagogical approaches that connect classroom theory with industrial applications.
LAKY Dóra is an Associate Professor at the Department of Sanitary and Environmental Engineering, Budapest University of Technology and Economics. She specializes in water and wastewater treatment technologies, focusing on contaminant removal, chemical processes, and water quality optimization. Drinking Water and Wastewater Treatment - BMEEOVKA-H1 Water and Wastewater Treatment Plants - BMEEOVKMV61 Water Chemistry and Hydrobiology - BMEEOVKAI43 Her research emphasizes arsenic removal, coagulation processes, and disinfection methods. Publications highlight laboratory experiments, pilot studies, and predictive modeling for sustainable water treatment solutions. Her recent work explores trends in contaminant removal, coagulation dynamics, and the interplay of chemical parameters in water purification. Articles from 2002–2014 demonstrate her long-term focus on drinking water safety and environmental engineering.
Dr. KNOLMÁR Marcell is an Assistant Professor at the Department of Sanitary and Environmental Engineering within the Faculty of Civil Engineering at Budapest University of Technology and Economics (BME) . He teaches courses including Infrastructural CAD, Legal Aspects of Water and Environment, and Public Works I, focusing on practical and regulatory dimensions of infrastructure design. His research spans urban drainage systems , climate change resilience , computational fluid dynamics , and GIS applications in wastewater management. Recent work emphasizes green stormwater infrastructure (GSI) , sewer network vulnerability assessments , and integrated hydraulic modeling for climate adaptation. Scientific awards include the Magyar Felvételi Scholarship (#építő250) . His publications reflect collaboration with the EU 5 Framework Program (CARE-S project) and long-term contributions to wastewater management , stormwater modeling , and environmental engineering in Hungary.
Marcell Beregi-Kovács is a Lecturer at the Department of Data Science and Visualization within the Faculty of Informatics at the University of Debrecen. His work focuses on machine learning, deep learning, optimization, and anomaly detection , aligning with the department's broader research in data science and visualization. The Department of Data Science and Visualization conducts research in areas including data processing, geospatial information systems, and computer graphics. Marcell contributes to educational and research activities within these domains. For direct inquiries, Marcell can be contacted at beregi.kovacs.marcell@inf.unideb.hu . His office is located in room I120 of the Faculty of Informatics building at 4028 Debrecen, Kassai Street 26.
Dr. Carolin Hannusch is a Senior Lecturer at the Department of Computer Science, Faculty of Informatics, University of Debrecen, Hungary. She holds a B.Sc. (2009) and M.Sc. (2011) in Mathematics from the University of Debrecen, and completed her Ph.D. there in 2015. Her office is located in Room I129 of the Faculty of Informatics building. Her primary research focuses on algebraic coding theory and cryptography , with expertise in error-correcting codes, combinatorial designs, discrete mathematics, and applications in cybersecurity. She also publishes in mathematics education and digital text analysis. Her recent publications demonstrate strong interdisciplinary work across coding theory (e.g., binary self-dual codes, Goppa codes), cryptographic systems (e.g., hash functions, symmetric cryptosystems), and educational technology (e.g., digital text management, online trigonometry instruction). Awards include: Special Price at XXX. Students' Conference Hungary (2011) Universitas Prize for Young Researchers (2015) University of Debrecen's "Thesis of the Year" Prize (2018) She maintains collaborations with researchers across Europe and has presented work at international conferences including FedCSIS, CITDS, and Central European Conference on Cryptology.
Dr. Attila Kuki is an Associate Professor at the University of Debrecen , affiliated with the Faculty of Informatics , Department of Informatics Systems and Networks . His primary research areas include Queueing Theory , Modeling Infocommunication Systems , Reliability Theory , and Modeling Stochastic Systems . Email: kuki.attila@inf.unideb.hu Research Interests: Queueing Theory Infocommunication Systems Reliability Theory Stochastic Systems Recent Publications focus on retrial queues, two-way communication systems, server reliability, collision handling, and stochastic modeling. These works often integrate simulation and numerical analysis to evaluate system performance under catastrophic breakdowns and impatience scenarios. Departmental Affiliation: The Department of Informatics Systems and Networks explores topics such as real-time communication, sensor networks, and distributed systems, aligning with Dr. Kuki's expertise.
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 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.
Dr. Attila Barta serves as an Assistant Professor in the Department of Applied Mathematics and Probability at the Faculty of Informatics, University of Debrecen. His office is located in room I210 on the 2nd floor of the Faculty of Informatics building at 4028 Debrecen, Kassai Street 26, with contact email barta.attila@inf.unideb.hu. His research aligns with the department's core focus areas: Probability Theory (limit value theorems, stochastic processes) Mathematical Statistics (asymptotics of estimates and tests) Statistics of Stochastic Processes Optimization and Numerical Methods applications Network Modeling Financial Mathematics No scientific awards, student advisement records, or publication details were specified in the source materials. His work contributes to the department's broader research initiatives in theoretical and applied mathematics.
János Török is an Associate Professor at the Department of Theoretical Physics, Budapest University of Technology and Economics, affiliated with the Morphodynamics group. His research spans granular materials, social network modeling, and morphodynamics of pebbles. Granular materials: Quasi-static shearing, shear band formation, particle shape effects, hopper flow Social science: Conflicts on Wikipedia, consensus modeling, social network dynamics Morphodynamics: Collective abrasion, fragmentation of pebbles His recent publications focus on computational modeling of granular physics and social dynamics, with applications in machine learning for malaria detection. Articles highlight interdisciplinary approaches to phase transitions, network analysis, and material deformation. Shear zones in granular materials Deep learning for social network parameters Malaria detection software He is involved in open-source projects (WWM, Mozi) and teaches courses in mathematical methods, mechanics, and scientific programming.
