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Backhausz ÁgnesView profile

Assistant Professor

• Probability Theory
• Random Graphs
• Matrices and their Limits
• +3 more

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.

Edith Alice KovácsView profile

Associate Professor

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +8 more

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.

Csaba NoszályView profile

Assistant Professor

• Probability Theory
• Stochastic Processes
• Mathematical Statistics
• +5 more

Csaba Noszály is an Assistant Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Applied Mathematics and Probability Theory . His work focuses on probability theory, stochastic processes, network modeling, and random graphs, with applications in numerical methods and statistical analysis. Research Areas : Probability theory, random graphs, network evolution models, stochastic optimization, and mathematical statistics. His publications explore continuous-time network evolution, triangle-based models, Taylor’s power law in networks, and algorithms for global minimization of conic functions. Publications : Csaba has contributed to studies on 3-interaction networks, random graph cliques, distance metrics in 2D samples, and strong laws of large numbers for random fields. Academic Role : He is a full-time faculty member with a primary focus on teaching and research in applied mathematics and probability.

Sándor PecsoraView profile

Assistant Professor

• Network modeling
• Random graphs
• Probability theory
• +1 more

Dr. Sándor Pecsora serves as an Assistant Professor at the Department of Applied Mathematics and Probability within the Faculty of Informatics, University of Debrecen. He maintains an active teaching role for core undergraduate courses including Discrete Mathematics, Probability Theory and Statistics, and Mathematics for Business and Economics, with office location in the Faculty of Informatics building (Kassai út 26, room E210/I210). His research specialization focuses on: Network modeling Random graphs These interests directly contribute to the department's broader research initiatives in probability theory, stochastic processes, and mathematical statistics. Educational materials for his courses are publicly accessible through his institutional profile. No scientific awards, grants, or student advisees are documented in the available institutional records. Dr. Pecsora remains a current, full-time faculty member actively engaged in the department's academic mission without indications of retirement or former status.