Máté Szabó is an Assistant Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Information Technology . His email contact is szabo.mate@inf.unideb.hu . He works in areas such as Machine Learning , Smart Cities , and Mobile Computing . His research spans topics like microservice architecture for ensemble models, Markov modeling of traffic flows, and distributed machine learning on mobile platforms. He has explored neural models for conversational AI and gamification in programming education through Minecraft-based challenges. His work also addresses edge computing and data parallelism in mobile environments. His publications (2016–2024) reflect trends in machine learning deployment on Android platforms smart city traffic analytics gamified educational tools cognitive modeling of numerical understanding microservice-based model integration .
Dr. Gábor Pintér holds dual academic appointments as Assistant Professor at Károli Gáspár Reformed University's Faculty of Humanities and Social Sciences (Department of Japanology) and as Associate Professor at Kobe University's School of Languages and Communication. Born in Hungary in 1977, he earned his MA (2005) and PhD (2008) in Linguistics from Kobe University, Japan, with a dissertation on asymmetrical segment distributions in Japanese. His research focuses on three interconnected domains: phonological theory (especially Japanese phonology), experimental phonetics, and applications of automatic speech recognition in language education. Specific interests include vowel devoicing patterns, prosodic perception in L2 learners, phonotactic constraints, and diachronic sound changes in Japanese. Pintér actively contributes to international research collaborations, including the JSPS-funded project 'Stochastic & theoretical phonological research' and serves as chair of the Phonology Association in Kansai (PAIK). His 15 most recent publications demonstrate methodological diversity, combining theoretical linguistics with computational approaches and experimental studies, primarily focused on Japanese phonetics and speech technology applications. Professional memberships include board positions in The Phonological Society of Japan and memberships in the International Speech Communication Association and Association for Laboratory Phonology.
János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.
Andrea Kő is a researcher at the Corvinus University of Budapest's Institute of Data Analysis and Informatics, with a focus on artificial intelligence, fintech, and big data applications. She has held positions in the Department of Information Systems until 2022 before transitioning to her current role. Her work emphasizes investment recommenders, Industry 4.0 readiness, and e-government solutions. Her research spans financial technologies, manufacturing optimization, and organizational resilience in SMEs. Notable contributions include hybrid AI models for production systems and frameworks for digital transformation assessment. She actively contributes to international conferences like EGOVIS and BiDEDE, editing proceedings and presenting on topics such as robo-advisors and smart manufacturing. Key projects include the CCMS2.0e maturity model for Industry 4.0 adoption and studies on pandemic impacts on SMEs. Her work integrates machine learning (ANFIS, MMNN) with domain-specific challenges, addressing both theoretical and practical aspects of digital innovation across sectors.
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 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.
Anikó Csébfalvi is a full professor at the Department of Civil Engineering, Faculty of Engineering and Information Technology, University of Pécs. She specializes in structural optimization, heuristic methods, and stability analysis of elastic structures, with a focus on discrete and continuous optimization of space trusses. Her research integrates hybrid metaheuristic algorithms, such as ANGEL, for engineering applications in structural design and project scheduling. Education: MSc (1978), PhD (1996), CSc (1996), Habilitation (2011) from institutions including Budapest University of Technology and Economics and the University of Pécs. Research: Structural optimization (sizing-shaping-topology), heuristic modeling, stability analysis, resource-constrained project scheduling, and elastic-plastic collapse constraints. Her 15 most recent articles (2004–2012) demonstrate expertise in hybrid metaheuristics, discrete-continuous truss optimization, and financial engineering. She serves as a thesis supervisor in the Marcell Breuer Doctoral School and collaborates internationally on structural mechanics topics. Scientific contributions include memberships in CEACM, ISSMO, and editorial roles for journals like Pollack Periodica and Structural and Multidisciplinary Optimization .
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.
Professor András Baranyai is a Doctor of Science at Eötvös Loránd University's Institute of Chemistry, where he leads research in physical chemistry and molecular modeling. His work focuses on developing advanced computational methods to understand liquid structure and ion interactions. Research interests explore: Molecular dynamics of aqueous systems Statistical mechanics foundations Polarizable force fields Water's anomalous properties Interfacial phenomena Astrochemical processes His publications demonstrate consistent focus on water structure, ionic solutions, and methodological advances in molecular simulation techniques. Laboratory activities include development of novel algorithms for charge distribution calculations and validation of molecular models against experimental data.
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.
Péter Stumpf is an Associate Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics. His research focuses on advanced control systems, power electronics, and machine learning applications in electrical drives. Contact Information: • Office: Building Q.B114, 1117 Budapest, Magyar tudósok krt. 2. Hungary • Phone: +36 (1) 463-2870 • Email: Stumpf.Peter@aut.bme.hu His recent work explores predictive control methods, including Model Predictive Control (MPC) and Reinforcement Learning (RL), applied to permanent magnet synchronous motors, grid-side converters, and high-speed drives. He has developed novel algorithms for optimal current computation, weighting factor assignment, and harmonics compensation. Key research trends include: Integration of machine learning in control systems Optimization of power electronics for renewable energy Advanced modulation techniques in motor drives Compensation of nonlinear effects in high-speed systems
Dr. Brigitta Krisztina Tóth is an Associate Professor at the Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). She serves on the Faculty Council and its Education Committee, and holds the communication and marketing officer role within her department. Her research bridges structural mechanics and biomedical applications, with a focus on computational modeling and mechanical testing. Academic Affiliation: Budapest University of Technology and Economics (BME), Faculty of Civil Engineering, Department of Structural Mechanics Leadership Roles: Member of Faculty Council and Education Committee; Communication and Marketing Officer Research Trends: Her publications span biomechanics (brain aneurysms, collagen fibrils), hyperelastic material modeling, fire safety engineering, and interdisciplinary topics like hand hygiene protocols. Recent work emphasizes energetically stable computational methods and statistical evaluation of biological tissues. Key Research Areas: Biomechanics, Structural Mechanics, Computational Modeling, Hyperelastic Materials, Cerebral Aneurysm Analysis Scientific Awards: #építő250 scholarship Teaching: Courses include Material Models in Mechanics , Nonlinear Mechanics , and foundational topics in structural analysis.
Tibor Dessewffy is a habilitated Associate Professor at the Department of Social Psychology within Eötvös Loránd University . His academic roles include positions as a lecturer at the Institute of Social Relations and the Department of Social Psychology . Research Interests: Spanning digital sociology , political sociology , and cultural studies , his work explores intersections between digital culture , political discourse , and societal transformation . Key themes include social media activism , democratic crises , and information society . Email: dessewffy.tibor@tatk.elte.hu
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.
Laszlo Matyas is a University Professor at the Department of Economics and Business at Central European University (CEU). He has held leadership roles, including Head of the Hungarian Accredited PhD Program in Economics and former Provost of CEU. His work focuses on econometrics , panel data analysis , and policy analysis in Central and Eastern Europe . He has co-authored/co-edited influential works, such as the Springer book series on econometrics. Education: Ph.D. (C.Sc, D.Sc) in Economics/Econometrics from the Hungarian Academy of Sciences. Research Interests: His research emphasizes panel data methodologies, fixed effects models, gravity models in trade analysis, and the impact of institutional changes on economic behavior. Recent work explores inflation dynamics, post-pandemic economic recovery, and machine learning applications in econometrics. Articles Trends: His recent publications address panel data techniques, policy implications of trade flows, and the economic effects of geopolitical events like the Ukraine war. He also investigates methodological challenges in econometrics, such as discretized variables and pairwise observations. Grants & Labs: While specific grants are not listed, his editorial roles and co-edited volumes (e.g., Seven Decades of Econometrics ) highlight his contributions to advancing econometric theory and practice.
