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.
Federico Battiston is an Associate Professor of Network Science and Director of the PhD Program in Network Science at Central European University (CEU), the first such program in Europe. He holds a PhD in Applied Mathematics from Queen Mary University of London and degrees in statistical physics from Sapienza University of Rome. His research focuses on network science, complex systems, and computational social science, with contributions in leading journals like Nature Physics , Physical Review Letters , and Science Advances . He coordinates the software project Hypergraphx and was Chair of NetSci2023, the largest Network Science conference. He has received awards including the Complex Systems Society's Junior Award (2022) and the European Physical Society's Early Career Prize (2021). Education: PhD in Applied Mathematics, Queen Mary University of London MSc in Theoretical Physics, Sapienza University of Rome BSc in Physics, Sapienza University of Rome Research Interests: Battiston explores generalized network structures (e.g., multilayer and higher-order networks), dynamics on networks (epidemics, social/cultural dynamics, synchronization), and their applications in social systems, neuroscience, and ecology. He emphasizes how network topology influences collective behavior and emergent phenomena. Key Contributions: His work includes hypergraph modeling, collaboration in escape rooms, and the role of higher-order interactions in brain networks. He co-authored the book Higher-order systems and guest-edited a Focus Collection on higher-order networks in Communications Physics . Awards & Roles: Junior Award of the Complex Systems Society (2022) Early Career Prize, European Physical Society (2021) Elected Member, Complex Systems Society Council Editor, Communications Physics Advising & Grants: Advised PhD students such as Milan Janosov, Luis Natera, and Rebeka Szabo. Two students received CEU Advanced Awards. His projects include Mapping the Higher-Order Dynamics of Neurodegeneration and DYNASNET . Labs/Teams: Leads the Hypergraphx team and collaborates on interdisciplinary projects in network science, including ecological networks and urban mobility analysis.
Balazs Vedres is a **Professor** at the **Central European University (CEU)**, with a joint appointment in the **Department of Network and Data Science**. His research integrates network science, data science, and social theory to explore creativity, innovation, and historical processes in collaborative networks. He holds a **PhD in Sociology from Columbia University** and an **MA in Economics from Corvinus University**. **Research Interests**: Focuses on structural dynamics in creative fields (e.g., jazz, video games, open-source software), gender inequality in collaborative platforms, historical network evolution, and the impact of social bots on human collaboration. He analyzes how network configurations like *structural folds* and *forbidden triads* drive innovation and creativity. **Awards**: Elected **Member of the European Academy of Sociology (2017)**, recognizing his contributions to sociological research. His work bridges computational methods with sociological theory, as seen in his publications in *American Journal of Sociology* and interdisciplinary journals. **Projects**: Leads initiatives like *Gendered Creative Teams: From Marginality to Success* and *Ceunet/Indra Mapping European Network Science*. His research often involves empirical studies of transnational activism, entrepreneurial networks, and digital platforms. **Labs/Teams**: Active in CEU’s **Data and Network Dynamics Studies (DNDS)** group, fostering interdisciplinary collaboration in computational social science. His work emphasizes the interplay between global economic integration and local developmental agency.
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.
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.
Edina Robin is a Habilitated Associate Professor at the Institute of Language Mediation , part of Eötvös Loránd University . She serves as the Director of the Institute and leads two departments: the Department of Translation and Interpreting and the Department of Hungarian as a Foreign Language . Her work bridges theoretical and applied translation studies, emphasizing modern challenges in language mediation. Role: Director of Institute of Language Mediation Role: Head of Department of Translation and Interpreting Role: Head of Department of Hungarian as a Foreign Language Research Interests Translation universals and revision practices Corpus-based translation analysis Human-in-the-loop machine translation systems Ethical implications of AI in translation Distance education methodologies in translation training Publication Trends reflect her interdisciplinary focus on machine translation (33%), translation teaching (25%), corpus linguistics (20%), and crisis communication (15%). Recent works explore the intersection of artificial intelligence, human agency, and ethical frameworks in language mediation. Academic Leadership includes institutional development for language mediation, curriculum design for Hungarian as a foreign language, and fostering collaboration between academic and industry translation practices.
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.
László Mérő is a Professor and lecturer in the Department of Affective Psychology at Eötvös Loránd University's Institute of Psychology. His work focuses on the interplay between psychology, game theory, and complex systems, particularly exploring how humans perceive and rationalize rare events, emotions in decision-making, and the boundaries of rational thought. He has also contributed to artificial intelligence research and the philosophical underpinnings of scientific inquiry. His publications often bridge theoretical and applied domains, addressing topics like monetary behavior, social coordination, and cognitive biases. Mérő’s research interests prominently feature the study of 'miracles' as probabilistic anomalies, the evolution of economic systems, and the limits of rationality in both human cognition and AI. He has written extensively on these themes, including books translated into English such as The Logic of Miracles and Moral Calculations . His work integrates empirical psychology with mathematical models to explain phenomena ranging from emotional impulses to collective social behaviors. No scientific awards are explicitly mentioned in the provided texts. While no advising or grant details are available, his publications suggest a focus on interdisciplinary research at the intersection of psychology, philosophy, and computational sciences. His institutional affiliation includes the Institute of Psychology, where he maintains an office at Izabella u. 46, Budapest, and can be reached via email and listed phone number.
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.
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.
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 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 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.
Krisztián Pomázi is a Lecturer 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 . His work bridges educational technology and cognitive science , utilizing machine learning and bioinformatics to enhance learning experiences. Department: Automation and Applied Informatics Email: Pomazi.Krisztian@aut.bme.hu Research interests include: Adaptive learning systems integrating biofeedback Machine learning applications in cognitive workload assessment Psychological profiling using computational models Usability evaluation via biomedical signal processing Intelligent exercise generation for cognitive assessment Educational game mechanics with physiological feedback Recent publication trends show a focus on blending machine learning with human-computer interaction to create personalized educational tools. His work incorporates cognitive science principles to dynamically adjust learning environments using biofeedback data, while also exploring psychometric and usability dimensions in digital education. Contact: Pomazi.Krisztian@aut.bme.hu
