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. Tamás Koltai is a Professor and Dean at the Faculty of Economics and Social Sciences of Budapest University of Technology and Economics (BME). He leads the doctoral school's Specialization Group in Production Management. His roles include overseeing academic programs and research in production management, operations research, and efficiency analysis. Education: Doctor of the Hungarian Academy of Sciences (2016) Dr. habil. (2000), Budapest University of Technology and Economics Candidate of Technical Sciences (1987), Hungarian Academy of Sciences M.Sc. in Mechanical Engineering (1983), BME Faculty of Mechanical Engineering Research Interests: Dr. Koltai focuses on production management optimization, including the application of Data Envelopment Analysis (DEA), sensitivity analysis in mathematical models, and the integration of robotics in assembly lines. His work bridges theoretical models (e.g., MILP/CP optimization) with practical industrial challenges, particularly in healthcare efficiency and educational management. Notable Awards: IEOM Society Teaching Excellence Award (2021) BME GTK Faculty Memorial Medal (2016) János Susánszki Award (2013) Széchenyi Professorship Scholarship (1999–) Teaching & Leadership: He has held visiting roles at the University of Seville (1990–1992) and the University of Michigan (1988/89). His teaching excellence is recognized through awards and his contributions to business simulation education. His research often collaborates with industry partners to address real-world operational challenges. Labs/Teams: Leads the Production Management Specialization Group and contributes to interdisciplinary teams focusing on manufacturing efficiency and healthcare operations within BME.
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.
Giacomo De Giorgi is a Full Professor of Economics at the University of Geneva’s Geneva School of Economics and Management (GSEM), affiliated with the Institute of Economics and Econometrics. He previously held roles as Assistant Professor at Stanford University (2006–2013), Visiting Professor at UC Berkeley (2010–2011), Wesley Mitchell Visiting Professor at Columbia University (2012–2013), Research Professor at ICREA-MOVE Barcelona (2013–2014), Senior Economist at the New York Federal Reserve (2014–2016), and currently holds a Visiting Professorship at UC Irvine (since 2018). He is an Associate Editor of the Journal of the European Economic Association and a member of BREAD, CEPR, and IPA. Education: Ph.D., University College London. His research focuses on Economic Development, Labour Economics, Household Finance, and Entrepreneurship . Notable contributions include studies on cash transfer programs’ spillover effects, family networks’ impact on education, and credit market dynamics. He co-launched the Virtual Development Economics Seminar Series (VDEV/Channel) in 2020. Recent articles explore lifecycle inequality between racial groups, post-default economic dynamics, and SDG tracking in crises. His work appears in top journals like the American Economic Review, Review of Economic Studies, and Journal of Development Economics. Awards: Banamex Prize (2020). Teaching includes Development Economics, Labor Economics, and Advanced Econometrics. He has advised numerous institutions and co-authored influential papers on topics like informal firms’ formalization and consumption networks. He is actively involved in interdisciplinary collaborations, presenting globally on themes like refugees’ poverty, farming transitions, and financial crisis narratives.
Robert Szoke is a Lecturer at the Department of Analysis, Eötvös Loránd University (ELTE), Budapest, Hungary. He has taught advanced courses such as Complex Function Theory , Kaehler Manifolds , and Stein Manifolds for mathematics students at both undergraduate and graduate levels since at least 2017. Teaching Roles: Courses in complex analysis, univariate/multivariate analysis, and differential geometry for BSc, MSc, and doctoral programs. Research Interests: Focus on complex and differential geometry, particularly Kaehler and Stein manifolds, multivariable complex functions, and several complex variables. Publication: Authored Multivariable Complex Functions (Eötvös Publishing House, 2003), a textbook used at ELTE. His work intersects mathematical analysis and geometry, emphasizing complex structures.
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.
János Győri is a Professor at the Institute of Intercultural Psychology and Education at Eötvös Loránd University in Budapest. His roles include serving as a lecturer in the Relation between Shadow and Public Education Research Group and as an Alternate Member of the Credit Transfer Committee. He specializes in intercultural education systems, shadow education dynamics, and international comparative studies in teacher education and language learning motivation. His research spans multiple countries including Japan, Singapore, the Czech Republic, and Myanmar, with a focus on educational policies and student motivation across different cultural contexts. Dr. Győri’s work emphasizes cross-cultural pedagogical comparisons, particularly analyzing how informal educational structures (shadow education) influence formal schooling systems. He has conducted extensive studies on international students' language learning motivations, teacher identity formation, and gifted education programs across global contexts. His research bridges educational theory and practice, often employing both quantitative and qualitative methods to explore complex educational phenomena. Notable themes in his publications include examining motivational frameworks for language acquisition among international populations, analyzing systemic differences in teacher education between nations, and evaluating the societal impacts of supplementary educational systems. His recent work highlights emerging trends in global talent development strategies and the evolving role of non-formal educational interventions. While no specific awards are listed, his prolific publication record indicates sustained academic contribution to educational sciences. His advisory activities and committee roles reflect engagement with institutional educational reforms and cross-border educational collaborations.
Tamas Koltai is a Professor at the Budapest University of Technology and Economics, affiliated with the Department of Management and Corporate Economics within the Faculty of Economic and Social Sciences. He holds a PhD in Industrial Engineering and is actively contributing to research in operations management, production planning, and industrial engineering. His research interests include: Operations Management Industrial Engineering Production Planning Assembly Line Balancing Human-Robot Collaboration Data Envelopment Analysis (DEA) Supply Chain Management Learning Curves Flexible Manufacturing Systems Performance Evaluation His recent publications focus on applying mathematical programming models (MILP, CP), simulation, and DEA to optimize assembly lines, particularly under learning effects and human-robot collaboration. He explores workload distribution, cycle time optimization, and efficiency evaluation in both manufacturing and service sectors, including healthcare and business simulation games. His work bridges theoretical models with practical industrial applications, supporting managerial decision-making under uncertainty. While no specific scientific awards are listed, his extensive publication record (over 50 papers) and high citation count reflect significant academic impact. He frequently collaborates with researchers such as Imre Dimény, Noémi Kalló, Viola Gallina, and Rita Dénes. There is no public information on PhD students advised or grants received. His work does not mention specific labs or research teams, but his focus on applied operations research suggests strong industry collaboration potential.
Vajk István is a Professor at the Department of Automation and Applied Informatics within the Budapest University of Technology and Economics (BME) . His work focuses on control theory, autonomous vehicles, robotics, and optimization algorithms. Position: Professor University: Budapest University of Technology and Economics Department: Automation and Applied Informatics Research Interests : Vajk’s research spans robust control systems for autonomous vehicles, trajectory optimization, and multi-actuator control strategies. His recent work emphasizes parameter-free and minimal-tuning control frameworks, leveraging internal model control and predictive algorithms. Publications : He has contributed extensively to journals and conferences on robotics, control theory, and optimization. Key themes include autonomous vehicle dynamics, nonconvex trajectory planning, and harmonics compensation in power systems. Contact : Vajk.Istvan@aut.bme.hu
Dr. Szabolcs Kertész serves as Associate Professor at the Faculty of Engineering, University of Szeged, Hungary, where he also manages the Innovation, Knowledge and Technology Transfer Office (ITTTI). His academic career spans over 15 years with significant contributions to membrane technology research and environmental engineering education. His educational background includes: Ph.D. in Environmental Sciences (2011) from University of Szeged Environmentalist MSc (2006) from University of Szeged Faculty of Sciences Habilitation (2019) from Doctoral School of Environmental Sciences Dr. Kertész's research centers on membrane separation processes for environmental applications, with particular emphasis on wastewater treatment technologies. His work investigates innovative approaches to reduce membrane fouling through 3D printed turbulence promoters, vibration technologies, and combined treatment methods. He has made substantial contributions to dairy industry wastewater management , developing sustainable solutions that reduce environmental impact while maintaining process efficiency. His research bridges chemical engineering principles with practical environmental applications, emphasizing translation of laboratory findings to industrial implementation. His recent publications demonstrate a strong trend toward integrating advanced manufacturing techniques like 3D printing with traditional membrane technologies. The research spans from fundamental studies of membrane fouling mechanisms to applied work on dairy and food industry wastewater treatment, showing consistent growth in impact factor and interdisciplinary collaboration. Among his distinguished recognitions: Bolyai-plakett (2024) Excellent Talent Care Award (2024, 2018) János Bolyai Research Scholarship (multiple periods) New National Excellence Program Scholarship (2020-2022) Dr. Kertész has secured significant research funding as Principal Investigator for projects including 'Optimization of hydrodynamic conditions with 3D printed elements' (NKFIH/OTKA FK142414) and 'Development of a dairy-based beverage selectively enriched with milk fat globule membrane materials.' His research group maintains international collaborations with institutions in Serbia, Turkey, Finland, and Belgium. He leads a research team focused on membrane technology innovation, specifically investigating how 3D printed elements can enhance membrane filtration efficiency in wastewater treatment applications. His laboratory combines experimental membrane testing with computational modeling to optimize hydrodynamic conditions in filtration systems, with particular emphasis on dairy industry applications.
János Baumgartner serves as Assistant Professor at the Department of Computer Science, Faculty of Information Technology, University of Pannonia in Veszprém, Hungary. His office is located in Building I, room 905, and he actively contributes to the PIMS Academy postgraduate program. His research focuses on: Supply Chain Management in petroleum industry Logistics Engineering and optimization Mathematical modeling of value chains Operations research applications Petroleum refining optimization Dr. Baumgartner plays a key role in the PIMS Academy, a specialized program developed with MOL Group that trains specialists in oil industry supply chain management. The program teaches market-leading optimization tools for downstream petroleum operations, covering subjects including LP Modeling, Petroleum Refining, Short Term Scheduling, and Supply Chain Management Fundamentals. As part of Hungary's educational initiative in energy sector optimization, his work contributes to producing graduates with the Specialist of Supply Chain Optimisation in Petroleum Industry (SCOPI) credential through a two-semester English-language program with potential industry placement. His academic contributions bridge computer science theory with practical industrial applications, particularly in optimizing complex petroleum value chains across multiple production sites and operational domains.
Dr. Zoltán Siménfalvi serves as Dean of the Faculty of Mechanical Engineering and Informatics at the University of Miskolc, Hungary, holding the academic rank of Professor. His leadership encompasses oversight of academic programs, research initiatives, and administrative functions within the faculty, positioning him at the forefront of mechanical engineering education and innovation in Central Europe. His research spans explosion protection, biogas technology, combustion engineering, and sustainable energy systems. Key focus areas include computational fluid dynamics (CFD) simulations for hazardous area classification, flash point determination of flammable mixtures, hydrogen/methane dispersion modeling, and optimization of anaerobic digestion processes. His work bridges theoretical analysis with industrial applications in energy safety and renewable resource utilization. Analysis of his 15 most recent publications (2022-2024) reveals dominant themes in explosion hazard analysis (60% of works), particularly 2D/3D hazardous area modeling, gas detector performance in ammonia environments, and FLACS-CFD simulations for hydrogen-methane mixtures. Biogas technology constitutes 25% of output, emphasizing mixing efficiency in anaerobic digesters and process optimization. Remaining research addresses sustainable engineering through carbon capture strategies for V4 countries, coal gasification efficiency, and propane leakage dynamics. No scientific awards were documented in the available materials. Information regarding student advising, research grants, or laboratory supervision was not provided in source materials. His administrative role as Dean suggests strategic oversight of research funding and academic programs, though specific grant details remain unreported. No dedicated research laboratories or specialized teams were explicitly referenced, though his publications indicate collaboration with computational modeling groups and industrial safety partners for experimental validation of CFD simulations.
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.
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
Judit Mokos is a Research Fellow in the Department of Plant Systematics, Ecology and Theoretical Biology at Eötvös Loránd University's Faculty of Science since 2023. An evolutionary and theoretical biologist with strong data science skills, she focuses on human behavior, cooperation, and well-being. She also works as a consultant data scientist for small companies and research groups, leveraging her expertise in R programming and statistical analysis. Education: PhD in Evolutionary and Theoretical Biology, Eötvös Loránd University (2015-2024) MSc in Ecology, Evolutionary and Conservation Biology, Eötvös Loránd University (2013-2015) BSc in Biology-Literature Teacher, Eötvös Loránd University (2010-2013) Judit's research spans multiple interdisciplinary areas focusing on why humans are supercooperative in certain situations but not others, why we help strangers but struggle with large-scale problems like climate change, and how social relationships (including with pets) impact human well-being. Her work combines evolutionary biology, data science, and psychology to understand human cooperation, altruism, and responses to global challenges. She employs diverse methodological approaches including experimental designs, statistical modeling, and meta-analyses to investigate questions at the intersection of evolutionary theory and contemporary societal issues. Her recent publications demonstrate a strong focus on applying evolutionary principles to understand modern human behavior, particularly during the COVID-19 pandemic. The articles show consistent interdisciplinary work bridging evolutionary biology, psychology, and public health, with particular attention to altruism, cooperation, stress responses, and pandemic-related behaviors. Her research often employs rigorous statistical methods and frequently involves collaborative work across disciplines. Scientific Awards: Cited on Wikipedia for academic work Ambassador of Researchers' Night in Hungary (2018, 2019) LifeScience Elevator Speech Festival 1st Place (2016) FameLab Hungary Finalist (2017) Covid FAQ webpage recommended by Hungarian Academy of Sciences as trustworthy information source Judit actively supervises students on diverse projects related to human cooperation, diversity in academia, and science communication. Her current supervisees include M. V. (MSc, 2024) working on "Diversity and innovation in academic life," M. Gy. (MSc, 2024) researching "Cooperation and sociopolitics," and S. L. (MSc, in progress) studying "Social dilemmas and data communication." She has taught biostatistics, research methodology, and advanced R programming at Eötvös Loránd University since 2015 and is passionate about interdisciplinary collaboration and science communication. She is actively involved in several research projects including studies on pet ownership and well-being, corruption in experimental settings, climate cooperation games, and online communication behavior. Her work often incorporates pre-registered study designs to ensure methodological rigor. She also contributes to science communication through her role as a guide at the Budapest Botanical Garden and by developing gamification projects for teaching evolution.
