Our research focuses on six main themes: network science, educational data science, health data analytics, intelligent monitoring and anomaly detection, media analytics, and science of science.
Network science
Our network science research covers both fundamental methodological research and applied research. Our main research areas include the structural characterization of complex networks, particularly fractality and robustness, data-driven analysis of fractal networks, calibrating network models, graph embedding methods.
Educational data science is an important branch of data science that aims to extract knowledge from various forms of massive educational data using statistical and machine learning methods. In cooperation with the Central Academic Office of BME, we aim to assist educational stakeholders by providing a better understanding of the big data stored in the educational administrative system. Our expertise includes identifying students at risk of dropping out, assessing the predictive validity of the admission system, identifying various factors of student success, quantifying the impact of interventions, explainable artificial intelligence.
We are committed to using the tools of network science and data science in medicine. We develop decision-support tools that assist the physicians by predictions based on data available at hospitalization.
Intelligent monitoring, state prediction, and anomaly detection
An important line of our research revolves around high-dimensional, high-frequency data. We do research about intelligent monitoring and predictive maintenance, particularly anomaly detection. We developed a coupla-based anomaly detection and localization algorithm that performs well on high-dimensional data and can cope with missing values as well.
Our media analytics research focuses on the statistical, network science, and machine learning analysis of large-scale data from traditional and social media. Our goal is to understand how media spaces function, along which trajectories information spreads across different topics, and what patterns characterize public communication. Our research topics include the joint examination of traditional and social media sources, the identification of information flows and narratives, the analysis of discourse structure, and the modeling of the temporal, network, and linguistic dynamics of topics.
The science of science examines the functioning of the scientific system through large-scale analysis of publication and citation data. Our goal is to develop new, mathematically grounded performance evaluation metrics, and to explore what determines scientific impact, what factors make publications interdisciplinary, and what consequences shocks to the scientific system entail. Our research areas include institutional change, university mergers, and the effect of the emergence of new research topics on researchers, journals, institutions, and scientific performance.