Prof. Zoltán Károly Lakner is a Professor at the Hungarian University of Agriculture and Life Sciences, affiliated with the Department of Agricultural and Food Economics. His research focuses on applying dynamic systems theory, econometrics, and artificial intelligence to analyze food supply networks, agricultural policy, and socio-economic systems in Africa and Central Asia. Key areas include big-data modeling of complex systems, game-theory applications, and open-source intelligence tools in innovation policy. He leads the SafeConsume H2020 project, developing strategies to mitigate foodborne illness risks through consumer education and prototype tools. His work spans food policy analysis, climate change impacts on agriculture, and ethical food consumption behaviors. Education details not explicitly provided in text. Research interests emphasize quantitative methods in agricultural economics, econometrics-driven policy analysis, and interdisciplinary approaches to global food challenges. Recent publications explore bioenergy expansion effects, water tax policy implications, and post-Paris Agreement green finance trends. No scientific awards explicitly stated in the text. Grants include the Horizon 2020 SafeConsume project. Active in advising and mentoring, though specific student names are not listed. Affiliated with the Szént István Campus in Gödöllő, contributing to institutional research and policy initiatives.
Roles: Full Professor at Budapest University of Technology and Economics (BME), leading the Laboratory of Cryptography and Systems Security (CrySyS Lab) . Specializes in cyber security, IoT security, and privacy technologies. Served as Associate Editor for IEEE Transactions on Mobile Computing and Elsevier Computer Communications. Education: M.Sc. in Computer Science, BME (1995) Ph.D. in Computer Science, Swiss Federal Institute of Technology Lausanne (EPFL, 2002) Habilitation at BME (2013) Doctor of Science, Hungarian Academy of Sciences (2021) Research Interests: Focuses on malware detection on embedded systems, security of industrial control systems, and privacy-preserving AI. Current projects include DOSS (IoT supply chain security), SECURED (health data security), and SPAM (AI and cybersecurity). Published over 150 papers and co-authored books on wireless network security and cryptographic obfuscation. Grants & Awards: Awarded Dennis Gabor Award (2024), Bolyai Fellowship (2008-2011), and led EU projects like SEVECOM and WSAN4CIP. Current grants include H2020 DOSS and OTKA-funded research on federated learning incentives. Advising: Supervised 13 PhD students, including current faculty members (e.g., András Gazdag, Dorottya Papp). Active in mentoring CTF teams like !SpamAndHex (DEFCON qualifier). Labs & Teams: Director of CrySyS Lab, leading research in embedded device security, vehicle cyber defense, and industrial IoT resilience. Active in EDIH cybersecurity consulting for SMEs.
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.
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.
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.
Dmitriy Dunaev is an Associate Professor at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics. His research focuses on hardware security, particularly through Physical Unclonable Functions (PUFs), software obfuscation, and FPGA-based security solutions. Current position: Associate Professor Institution: Budapest University of Technology and Economics Department: Automation and Applied Informatics Research spans cryptographic security enhancement via configurable hardware designs, machine learning vulnerability analysis, and optimization of security protocols in embedded systems. Recent publications investigate temperature sensitivity, bit configuration in PUFs, and FPGA implementation challenges. Contact: dunaev@aut.bme.hu
Husam Al-Magsoosi is an Assistant Research Fellow at the Budapest University of Technology and Economics , affiliated with the College of Engineering and the Department of Automation and Applied Informatics . His work focuses on hardware security, particularly in the development and analysis of Physical Unclonable Functions (PUFs) . Research Interests : Hardware Security, PUFs, FPGA Design, Machine Learning in Security, Embedded Systems, Wireless Sensor Networks. His recent publications highlight advancements in configurable ring oscillator PUFs, addressing security, performance optimization, and environmental factors like temperature sensitivity. He explores FPGA-based implementations and evaluates vulnerabilities to machine learning attacks. His work also extends to energy-efficient routing protocols for sensor networks and embedded system design. Scientific Awards : No specific awards mentioned in the provided data.
Ágnes Backhausz is a habilitated assistant professor at Eötvös Loránd University's Faculty of Science, Institute of Mathematics, Department of Probability Theory and Statistics, with a part-time research position at the Alfréd Rényi Institute of Mathematics. Her academic career spans theoretical probability and practical applications in network science. Dr. Backhausz's research focuses on probability theory with specializations in random graphs, matrices and their limits, spectral theory of random graphs, and factor of iid processes. Her work bridges pure mathematics with real-world applications, particularly in epidemiological modeling on complex networks. She has made significant contributions to understanding the eigenvectors of random regular graphs and the behavior of processes on infinite trees. Her recent publications demonstrate a growing emphasis on applying probabilistic methods to epidemic modeling on multilayer networks with overlapping communities. This research has important implications for public health policy and disease control strategies. The trend in her work shows increasing interdisciplinary collaboration, combining mathematical rigor with practical healthcare applications. Dr. Backhausz holds significant academic responsibilities including serving as Supervisor and training lead for the Beyond The Edge Marie Curie Doctoral Network (2024-2027) and as a researcher at the National Laboratory for Health Security, Hungary (2023-2026). She is an editor for Acta Mathematica Hungarica since March 2021 and has been organizing the Departmental seminar of the Department of Probability Theory and Statistics since 2010. She actively contributes to the mathematical community through program committee memberships for major conferences like Eurocomb and by organizing workshops on graph limits, groups, and stochastic processes. Her editorial work further demonstrates her standing in the mathematical research community. As an educator, Dr. Backhausz teaches mathematical statistics, probability theory, and related subjects across multiple programs at ELTE, including courses for mathematics students, earth science students, and informatics programs, showcasing the interdisciplinary nature of her expertise.
Ágnes Vathy-Fogarassy is Habilitated Associate Professor and Head of the Department of Computer Science and Systems Technology at the University of Pannonia's Faculty of Engineering and Informatics. She also serves as the Rector's Commissioner for Artificial Intelligence Education and Development and the Dean's Representative for Quality Assurance and Accreditation. Additionally, she leads the Data-intensive Artificial Intelligence Methods and Systems Research Laboratory and the Healthcare Analytics Research and Development Center. Her educational background includes: PhD in Information Science (2009) Studies at Eötvös Loránd University in Computer Science (1999-2007) Studies at University of Pannonia in Computer Science (1995-1998) Mathematics-Physics and Computer Science Teacher training at Berzsenyi Dániel Teacher Training College (1995) Ágnes Vathy-Fogarassy's research focuses on machine learning, artificial intelligence, data science, and their applications in healthcare . Her work spans predictive analytics, network analysis, and medical informatics, with a particular emphasis on developing AI methods for healthcare data analysis. She has pioneered approaches for N-glycomics-based biomarker discovery, cancer treatment prediction, and heart failure risk assessment using machine learning techniques. Her interdisciplinary research bridges computer science with medical applications, creating innovative solutions for healthcare challenges. Her recent publications demonstrate a strong trend toward applied AI in healthcare , with significant work on diabetes classification, chemotherapy effectiveness prediction, and cardiovascular risk assessment. She also maintains active research in automotive AI applications (vehicle dynamics prediction) and renewable energy optimization (solar power plant modeling). Her work consistently combines theoretical machine learning advancements with practical implementations across diverse domains. Her notable scientific achievements include: László Méray Award, University of Pannonia (2024) Tarján Memorial Medal, John Neumann Computer Science Society (2022) Pro Sciencia Award, University of Pannonia (2021) Veszprém Women's Roundtable Association Women's Empowerment Award (2019) Pro Universitate Pannonica silver medal (2017) PE-MIK Best Female Instructor (2017) As an academic advisor, Ágnes Vathy-Fogarassy has successfully guided multiple PhD students to completion, including Dániel Leitold (2020), Szabolcs Szekér (2024), and János Kontos (2025). She currently supervises several ongoing doctoral research projects with Attila Knolmajer, Tamás Miseta, Veronika Gombás, and Eszter Szakács. Her commitment to talent development is evident through her students' numerous Best Paper awards at international conferences and successful TDK papers. She has developed the curriculum for several data science subjects and established the Data Science master's program at the University of Pannonia in 2023. She leads two major research entities: the Data-intensive Artificial Intelligence Methods and Systems Research Laboratory (founded 2021) and the Healthcare Analytics Research and Development Center (founded 2017). These teams focus on cutting-edge AI research with particular emphasis on healthcare applications, bringing together interdisciplinary researchers to tackle complex data challenges in medical domains.
Anita Mondok is an External Lecturer at the Institute of Health Promotion and Sport Sciences, Eötvös Loránd University (Hungary), with a focus on hydrogeological research and environmental tracer applications. Her academic work bridges groundwater dynamics, karst systems, and sustainable water management. 2025 : Studies on hypogene karst transitions and groundwater aging 2024 : Investigations in soda lake budgets, uranium mobility, and AI-driven hydrology Her research leverages isotopic tracers and numerical models to address drinking water quality, climate change impacts, and aquifer vulnerability. She has also explored unconventional topics like pediatric medicine and thermal spring biofilms, suggesting interdisciplinary interests. Her publications highlight applications of mesh graph neural networks and reactive transport models to hydrological challenges. While no formal awards or advisees are documented in available sources, her work directly informs groundwater protection strategies in Hungary and Northern Italy.
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 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.
