جست‌وجوی استاد و پژوهشگر

Gábor Rappaiمشاهده پروفایل

استاد

• Econometrics
• Financial Time Series Modeling
• Causal Analysis
• +۳ مورد دیگر

Professor Gábor Rappai is a faculty member at the Department of Economics and Econometrics, University of Pécs. He serves as Head of the Doctoral School of Business Administration since 2019 and has held significant administrative roles including Vice Dean (1998-2005), Dean (2005-2011), and Senate member (14 years). PhD in Economics (Hungarian Academy of Sciences, 2003) Habilitation in Economics (2003) Candidate of Economics (1997) Graduated in Commodity Trading (Janus Pannonius University, 1987) His research focuses on econometric methodologies applied to financial time series analysis, causal inference in economic systems, and stochastic modeling in sports economics. He has developed frameworks for strategic policy evaluation and integrated quality control with production-marketing interfaces in JIT environments. Publications demonstrate expertise in sports economics (UEFA Champions League dynamics), policy evaluation (Europe 2020 strategic goals), and operations management (JIT production systems). Key methodologies include econometric testing, stochastic modeling, and applied mathematical optimization. Active in professional communities, he contributes to the Hungarian Academy of Sciences' Scientific Committee of Statistics and Prognostics, Hungarian Statistical Society, and Economic Modelling Society.

• Developmental Psychology
• Cognitive Science
• Information Search Strategies
• +۳ مورد دیگر

Azzurra Ruggeri is an Associate Professor at the Central European University (CEU) and Technical University of Munich (TUM), holding positions in the Department of Cognitive Science (CEU) and School of Education (TUM). Her research focuses on how individuals actively search for information during decision-making, causal inference, and categorization tasks, emphasizing adaptability and task sensitivity. Education: Ph.D. in Psychology, Humboldt University of Berlin Ph.D. in Cognitive Science, University of Siena M.A. in Philosophy, Decision Making and Philosophy of Science, University of Pisa Research Interests: Cognitive development of information search strategies Adaptive decision-making in children and adults Role of active learning in cognitive development Her work bridges developmental psychology and cognitive science, with contributions to understanding how search strategies adapt to task structures and enhance learning outcomes. Awards: Volkswagen Foundation Grant (€1.5M, 2020) DFG Focus Program Grant (€350K, 2018) Marie Curie Fellowship ($282K, 2013–2017) Labs/Teams: Leads the iSearch research group, exploring Information Search, Ecological, and Active Learning in children.

Márton Karsaiمشاهده پروفایل

دانشیار

• Network Science
• Computational Social Science
• Data Science
• +۵ مورد دیگر

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.

Laszlo Matyasمشاهده پروفایل

استاد

• Econometrics
• Panel Data Analysis
• Policy Analysis (Central and Eastern Europe)
• +۳ مورد دیگر

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.

Jozsef Fiserمشاهده پروفایل

استاد

• Cognitive Science
• Visual Statistical Learning
• Perceptual Decision-Making
• +۵ مورد دیگر

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.

• Statistical Literacy
• Financial Literacy
• Statistics Education
• +۵ مورد دیگر

Dr. Péter Kovács is an Associate Professor and Chair of the Department of Statistics and Demography at the University of Szeged, serving as Dean of the Faculty of Economics and Business Administration since 2017. Previously, he was Vice Dean (2014–2017). He holds a PhD and habilitation in Economics and an MA in Mathematics. His research focuses on statistical and financial literacy, multivariate statistical methods, and education technology. He leads the Financial Literacy Research Group and participates in initiatives like ProCivicStat. He has extensive editorial roles, including membership in the editorial boards of Pakistan Journal of Statistics and Operation Research , Journal of Applied Economics and Business , and others. He chairs the Hungarian Academy of Science's Statistics Scientific Subcommittee and holds leadership roles in international organizations like the International Association for Statistical Education (IASE) and the International Statistical Literacy Project (ISLP). His research interests bridge statistical methodology with societal issues, including the acceptance of autonomous vehicles, maternal mortality in sub-Saharan Africa, and the integration of visualization tools in education. He has been awarded the Péter György Award (2006) for teaching and recognized as Ambassador of Financial Literacy (2021). Teaching responsibilities include courses on statistics, econometrics, and business software. He emphasizes modern pedagogy, including technology-driven methods and collaborative learning approaches. His work addresses both theoretical advancements and practical applications in education and policy.

Ildikó Neumanné Virágمشاهده پروفایل

دانشیار

• European Integration
• International Economics
• International Trade
• +۴ مورد دیگر

Ildikó Neumanné Virág is an Associate Professor and Head of the Department of International Economics at the Institute of Economics, University of Pannonia. She specializes in European integration, EU enlargement impacts on Central and Eastern Europe, international trade, and sustainability economics. Current roles: Department Head, Faculty Council member, President of the Hungarian Economic Society of Veszprém County Research focus: Trade policy instruments in EU integration, circular economy, climate change, and statistical modeling using gravity models Key projects: ROP 3.3 "HEC for Development of Private Enterprises," EFOP-3.6.2 Sustainable Regional Models, RRF-2.3.1 National Climate Change Laboratory Memberships: INFER (International Network for Economic Research), Faculty Council since 2011

Edith Alice Kovácsمشاهده پروفایل

دانشیار

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +۸ مورد دیگر

Edith Alice Kovács is an Associate Professor at the Department of Differential Equations, Budapest University of Technology and Economics (BME). She holds a PhD from the West University of Timisoara (2005) and has taught at multiple institutions, including the General College of Entrepreneurship (ÁVF) and universities in Arad. Her research focuses on multivariate statistics, probability graph models, information theory, AI/ML, network reliability, and mathematical modeling in finance/medicine. She has received the BME Innovation Award 2020 for anomaly detection work. Kovács has presented at conferences worldwide, including the European Conference on Operational Research and the IEEE SACI conference. Her teaching spans subjects like economic modeling, operations research, and data science across BME and ÁVF programs. Education: BSc and MSc in mathematics from the West University of Timisoara (1994), PhD in Mathematics (2005). Professional roles include full-time employment at BME since 2015 and prior experience at Aurel Vlaicu and Vasile Goldis Universities (1994–2000). Research interests include decision-making under uncertainty, metaheuristic algorithms, neural halos, and social network modeling. Recent work explores copula-based anomaly detection and vine copula structures. Kovács has contributed to projects like stochastic inventory modeling for TESCO and VNF fingerprinting for Nokia.

Anand Murugesanمشاهده پروفایل

دانشیار

• Economics
• Causal Inference
• Development Economics
• +۳ مورد دیگر

Anand Murugesan is an Associate Professor of Economics at the Department of Public Policy, Central European University (CEU), and a Senior Researcher (part-time) at the University of Vienna. He specializes in combining economic insights with causal inference methods to study social issues, including conflicts in India, the legacy of Habsburg imperial history on contemporary behaviors, and democratic participation norms. Education: PhD in Economics, University of Maryland, College Park MA in Economics, Jawaharlal Nehru University, New Delhi BCom, St. Joseph's College (Bangalore University) Research Interests: Murugesan's work bridges economics and interdisciplinary methods, focusing on development, public policy, political behavior, and experimental economics. He collaborates on projects like the Horizon Europe-funded ENCODE project on democratic resilience and explores topics such as sanitation externalities, air pollution trade-offs, and electoral clientelism. Key Articles: His recent publications highlight causal inference applications in public policy, experimental evidence on environmental participation, and health implications of air pollution in Goa. His work reflects a commitment to methodological rigor and policy relevance. Affiliations: Social Mind Center at CEU CEU Democracy Institute Institute of Advanced Studies, Vienna

• statistics
• performance evaluation
• adaptive measurement technologies
• +۵ مورد دیگر

Dr. Szabolcs Takacs is an Associate Professor at Gáspár Károli Reformed University (KRE), where he serves as Deputy Head of the Institute of Psychology within the Faculty of Humanities and Social Sciences. Additionally, he holds the position of Deputy Dean of Education. His academic career combines expertise in mathematics, statistics, and psychology, with a focus on applying quantitative methods to psychological and social research questions. Dr. Takacs has established himself as a prominent figure in statistical methodology within the Hungarian academic community. Dr. Takacs earned his educational qualifications at Eötvös Loránd University (ELTE): PhD in Applied Mathematics (2014) - Dissertation: "Solving some problems of hierarchical money distribution systems using random vectors and multidimensional scaling" MA in Applied Mathematics (2004) Dr. Takacs' research spans multiple domains at the intersection of statistics and psychology. His primary focus areas include statistical methodology development, performance evaluation systems, and adaptive measurement technologies. He applies operations research techniques such as linear programming and stochastic optimization to psychological measurement problems. His work often addresses practical applications in workforce analysis, educational assessment, and risk management. Dr. Takacs has made significant contributions to cluster analysis within person-oriented research frameworks and has developed innovative validation procedures for statistical models. Analysis of Dr. Takacs' 15 most recent publications reveals a strong emphasis on statistical methodology development with applications primarily in psychology and social sciences. His work demonstrates expertise in multidimensional scaling, classification methods, and cluster analysis. A notable trend is the application of mathematical optimization techniques to psychological measurement problems. Many publications address workforce and employment issues, reflecting his interest in practical applications of statistical methods. His research shows increasing sophistication in handling complex data structures while maintaining focus on real-world applicability. Dr. Takacs has received prestigious recognition from KRE: Publication of the Year (2014) - awarded in 2015 Teacher of the Year (2014) - awarded in 2015 Dr. Takacs actively mentors the next generation of researchers, currently supervising three doctoral students across institutions (Tilburg University and KRE). His supervisory approach emphasizes methodological rigor in psychological research. He has served as an opponent in multiple doctoral defenses, most recently in 2024 for research on mathematics-related beliefs and academic performance. His leadership extends to the Competency Assessment Research Group, which he leads, focusing on developing and validating assessment tools for various professional contexts. As Deputy Dean of Education and Deputy Head of Institute, Dr. Takacs plays a significant role in shaping educational policy and research direction within the Faculty of Humanities and Social Sciences. His work bridges theoretical statistical methodology with practical applications in psychology and social sciences, contributing to the university's research profile and educational mission.

Attila Imre KATONAمشاهده پروفایل

عضو هیئت علمی

• Statistical Process Control
• Measurement Uncertainty
• Quality Management
• +۵ مورد دیگر

Attila Imre KATONA is a Senior Research Fellow and Data Scientist at IBM, with a concurrent affiliation at the University of Pannonia in the Department of Quantitative Methods under the Doctoral School of Management and Organizational Sciences. His work bridges statistical process control, machine learning, and European R&D program analysis, focusing on measurement uncertainty and risk-based methodologies. Education : PhD in Management Sciences (University of Pannonia, 2019), Graduate logistics manager (2012), Graduate technical manager (2008). Research Interests include: Addressing measurement uncertainty in statistical process control Analysis of student preferences using gravity and logit models Risk-based optimization of industrial control systems Email : katona.attila@gtk.uni-pannon.hu | Consultation Hours : Tuesday 10:30-11:30 (email pre-negotiation required).

Martina Contiscianiمشاهده پروفایل

پژوهشگر ارشد

• Network Science
• Statistical Inference
• Complex Systems
• +۳ مورد دیگر

Martina Contisciani is a Research Fellow at the Department of Network and Data Science, Central European University (CEU), Vienna. Her work focuses on developing statistical methods and algorithms for analyzing complex systems, with expertise in network inference and multiscale modeling. Her academic background includes: Ph.D. in Computer Science from Max Planck Institute for Intelligent Systems and University of Tübingen (2023), supervised by Dr. Caterina De Bacco M.Sc. in Data Science from University of Padua, Italy B.Sc. in Statistics for Business and Economics from University of Padua, Italy Contisciani's research centers on network science, particularly probabilistic generative models for complex systems. She has pioneered work in community detection for multilayer and hypergraph networks, developed the Hypergraphx library for higher-order network analysis, and currently leads migration flow modeling in Austria through the MOMA project. Her methodological innovations bridge theoretical network science with real-world demographic applications. Analysis of her 15 most recent publications (2017-2025) reveals an evolution from foundational work on community detection and network modeling toward impactful applications in migration studies. Her recent output demonstrates increasing integration of statistical inference with spatial and demographic data, particularly through multiscale network approaches applied to Austrian migration patterns. Her scientific recognition includes: Cyber Valley PhD Fellowship IMPRS-IS Fellowship Contisciani actively contributes to the MOMA project under Prof. Tiago P. Peixoto and Prof. Márton Karsai, developing inferential network models for migration analysis. While no formal student advising is documented, her role involves collaborative research within CEU's Network and Data Science department. Her recent presentations at IC2S2’25 and NetSci 2025 highlight ongoing contributions to network science methodology and applications. As a core member of CEU's Network and Data Science research team, she collaborates with leading network scientists on projects combining theoretical innovation with societal challenges. Her current work on Austria's migration flows represents a strategic shift toward data-driven policy-relevant research while maintaining strong methodological rigor.

Max Falkenbergمشاهده پروفایل

پژوهشگر ارشد

• Computational Social Science
• Network Science
• Climate Communication
• +۳ مورد دیگر

Max Falkenberg is a Research Fellow in the Department of Network and Data Science at Central European University (CEU), Vienna, Austria, and holds Visiting Fellow positions at the Institute for Sustainable Resources at University College London (UCL) and the Inverse Complexity Group at IT:U in Linz, Austria. He earned his PhD in Physics from Imperial College London (2022) and completed a postdoc in Mathematics at City, University of London (2024). His research applies network science and computational methods to study political polarization, climate communication, climate finance, and structural dynamics of social systems. Dr. Falkenberg’s work bridges computational social science with policy analysis. Notable contributions include datasets on Indian microblogging platform Koo, reports on climate tipping points for COP28, and analyses of fossil fuel finance in banking sectors. His findings have been featured in media outlets like The Guardian, Bloomberg, and BBC World Service. He has also advised policy forums such as the UK Parliament’s EFRA Select Committee and presented at institutions like the Bank of England and Spotify. Education: PhD Physics (Imperial College London), MSci Physics (Imperial College London) Key Projects: Climate finance modeling, Koo dataset analysis, COP28 climate policy report Media Contributions: Interviews with The Times, AFP Paris, and TVP World His policy-oriented research includes op-eds in The Banker (Financial Times) and collaborations with think-tanks. Falkenberg’s interdisciplinary approach integrates network science, statistical inference, and AI to address global challenges in sustainability and governance.

• Cognitive Psychology
• Linguistics
• Neuroscience
• +۷ مورد دیگر

Bálint Forgács is an Habil. Associate Professor at the Department of Cognitive Psychology, Eötvös Loránd University, and Head of the Language and Brain Research Group. His research focuses on semantic processing, metaphor comprehension, theory of mind in infants, and electrophysiological correlates of cognitive functions. He explores how language and communication develop in early childhood and how abstract concepts are mentally represented. His work intersects cognitive psychology, neuroscience, and linguistics, emphasizing social and developmental aspects of cognition. Research interests include the pragmatics of metaphorical language, the neurobiological basis of semantic systems, and the communicative functions of language from birth. He investigates how infants track others' understanding and how metaphors shape discourse in fields like climate science. His studies employ electrophysiological methods (e.g., ERP) and behavioral experiments to uncover mechanisms underlying meaning construction and social cognition. Forgács' recent work highlights newborns' sensitivity to communicative dimensions of language and the role of mentalization in semantic processing. He critiques metaphorical language in climate discourse, advocating for clearer scientific communication. His research has implications for education, linguistics, and interdisciplinary studies in cognitive science.

Robert Lieliمشاهده پروفایل

استاد

• Econometrics
• Forecasting
• Treatment Effects
• +۳ مورد دیگر

Robert Lieli is a Professor and Head of the Department of Economics and Business at Central European University (CEU) in Vienna. He holds a Ph.D. in Economics from the University of California, San Diego (2004). Prior to joining CEU, he worked at the University of Texas at Austin and the National Bank of Hungary. His research focuses on econometrics with an emphasis on forecasting (especially binary prediction) and treatment effects, contributing to top journals like the Journal of Econometrics and the Journal of Business and Economic Statistics. Dr. Lieli’s academic career includes roles as a member of CEU’s Economics Doctoral School and an Associate Professor in Undergraduate Studies. His research explores advanced econometric methods applied to policy evaluation, causal inference, and decision-making processes. Recent work addresses endogenous treatment effects, ROC curve analysis, and machine learning applications in treatment effect estimation. He actively participates in academic events such as the Brownbag Seminar series, focusing on topics like forecasting with feedback and treatment effect analysis. His expertise bridges theoretical econometrics with practical policy analysis, reflecting his contributions to both academic discourse and institutional leadership at CEU.