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Members of the laboratory:
  • Dr. Ágnes Vathy-Fogarassy, associate professor, head of the laboratory
  • Dr. Ágnes Stark-Werner, associate professor
  • Dr. Tibor Dulai, adjunct professor
  • Gyula Ábrahám, assistant professor
  • Zsuzsanna Nagy, PhD student
  • Szabolcs Szekér, PhD student
  • János Kontos, PhD student
  • Tamás Miseta, PhD student
  • Veronika Gombás, PhD student
  • Péter Scsibrán, BSc student

Former member of the laboratory: 

  • Dr. Dániel Leitold, adjunct professor
Activity of the research laboratory:
Our research activity focuses on the development of machine learning algorithms based on structured, semi-structured and free text recorded information. Our goal is to develop new data-driven machine learning algorithms that can effectively exploit and transform the hidden information inherent in exponentially growing data assets into valuable information. Our research area covers the fields of supervised, unsupervised and reinforced learning. In our research, the study and development of neural networks and deep learning methods represent a significant segment. Our results offer partly field-specific and partly general solutions to the investigated problems.
Related publications:

J. Kontos, B. Kránicz, Á. Vathy-Fogarassy (2023). Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network. Sensors, 23(12), 5670.
S. Szekér, G. Fogarassy, Á. Vathy-Fogarassy (2023). A general text mining method to extract echocardiography measurement results from echocardiography documents. Artificial Intelligence in Medicine, 143, 102584.
T. Miseta, A. Fodor, Á. Vathy-Fogarassy (2022). Energy trading strategy for storage-based renewable power plants. Energy, 250, 123788.
J. Kontos, B. Kránicz, Á. Vathy-Fogarassy (2022). Neural Network-Based Prediction for Lateral Acceleration of Vehicles. In 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) (pp. 153-158). IEEE.
Á. Vathy-Fogarassy, I. Vassányi, I. Kósa (2022). Multi-level process mining methodology for exploring disease-specific care processes. Journal of Biomedical Informatics, 125, 103979.
S. Szekér, Á. Vathy-Fogarassy (2021). Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection. Algorithms, 14(12), 356.
S. Szekér, Á. Vathy-Fogarassy (2020). Weighted nearest neighbours-based control group selection method for observational studies. PLOS ONE 15: 7 Paper: e0236531
Z. Nagy, A. Werner-Stark (2020). A Multi-perspective Online Conformance Checking Technique. In: 2020 6th International Conference on Information Management (ICIM), IEEE, 172-176
Á. Vathy-Fogarassy, S. Szekér, B. Szolár, G. Fogarassy (2020). The Efficiency of Different Distance Metrics for Keyword-Based Search in Medical Documents: A Short Case Study. Studies in Health Technology and Informatics 271, pp. 232-239
D. Leitold, Á. Vathy-Fogarassy, J. Abonyi (2020). Network-Based Analysis of Dynamical Systems. Springer Briefs in Computer Science, p.110. Springer International Publishing
G. Fogarassy, Á. Vathy-Fogarassy, I. Kenessey, M. Kásler, T. Forster (2019). Risk prediction model for long-term heart failure incidence after epirubicin chemotherapy for breast cancer – A real-world data-based, nationwide classification analysis. International Journal of Cardiology, vol. 285, 47-52, 6 p.
G. Ábrahám, P. Auer, G. Dósa, T. Dulai, Á. Werner-Stark (2019). A Reinforcement Learning Motivated Algorithm for Process Optimization. Periodica Polytechnica-Civil Engineering, 63:4, 961-970
T. Miseta, Á. Vathy-Fogarassy (2019). The Effect of the Different Data Aggregation Methods and their Detail Levels to the Prediction of Bitcoin's Exchange Rate. In: Levente, Kovács; Carlos, M. Travieso-González (szerk.) Proceedings of IEEE International Work Conference on Bioinspired Intelligence IWOBI 2019, IEEE, pp. 145-152
Z. Nagy, Á. Werner-Stark, T. Dulai (2019). Using Process Mining in Real-Time to Reduce the Number of Faulty Products. In: Kamišalić Latifić, Aida; Podgorelec, Vili; Eder, Johann; Welzer, Tatjana (eds.) Advances in Databases and Information Systems: 23rd European Conference, ADBIS 2019, Springer, 89-104
S. Szekér, G. Fogarassy, K. Machalik, Á. Vathy-Fogarassy (2019). Application of Named Entity Recognition Methods to Extract Information from Echocardiography Reports. Studies in Health Technology and Informatics, vol. 260, 41-48
S. Szekér, Á. Vathy-Fogarassy (2018). The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching. Studies in Health Technology and Informatics, vol. 248, 1-8
K. Tóth, K. Machalik, G. Fogarassy, Á. Vathy-Fogarassy (2017). Applicability of Process Mining in the Exploration of Healthcare Sequences. In: Szakál, Anikó (szerk.) IEEE 30th Jubilee Neumann Colloquium, 151-155
K. Tóth, I. Kósa, Á. Vathy-Fogarassy (2017). Frequent Treatment Sequence Mining from Medical Databases. Studies in Health Technology and Informatics, 2017, 236: 211-218

Introduction of the head of the laboratory:
  Ágnes Vathy-Fogarassy (associate professor, head of Department of Computer Science and Systems Technology). She obtained the PhD degree at the Eötvös Lóránd University in 2009 in the field of Computer Science. She wrote her PhD dissertation in the topic of unsupervised learning (graph-based clustering algorithms). She habilitated in Computer Science in 2023. Her research interests include data-intensive predictive analytical methods, network analysis, deep learning, and applications of machine learning methods in healthcare. So far, she has co-authored 2 monographs and more than 100 articles, mainly related to the topic of the research laboratory.