The majority of scientists, from archaeologists through to zoologists, collect huge volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics, we can develop algorithms for analysing and identifying patterns in the big data from many specialist fields, and apply them to obtain novel insights.
Computational network science
The Leiden Computational Network Science Lab (CNS Lab) researches methods for knowledge discovery from real-world network data. Using a combination of graph algorithms and machine learning techniques, we strive to unveil patterns in dynamic complex networks from a range of application domains. Examples include social networks, communication networks, scientific networks, infrastructure networks and corporate/economic/financial networks.
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Data mining & sports
Collecting data in sports increased in importance the last few years. Camera systems can track the position of players, sensors are implemented in clothing and many applications have been designed to monitor, for example, the health of athletes. The Data Mining and Sports group uses artificial intelligence, machine learning, data mining, semantic web technology, image processing and high performance computing to make predictions from this data and to discover new underlying patterns that would otherwise have been unnoticed.
Explanatory data analysis
The Explainatory Data Analysis group develops algorithms and theory that enable domain experts to explain data by finding interpretable patterns and models. Their main focus is on exploratory data analysis, often in the form of discovering novel and unexpected patterns that may give useful insights. They aim for algorithms that are accurate, provide interpretable results, and can be guided by the analyst. Their research builds on the state of the art in information theoretic data mining, statistical pattern mining, and interactive data exploration and analytics. More broadly speaking, their research can be situated in the fields of data mining, machine learning, data science, and artificial intelligence (AI).
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Text mining and retrieval
Text Mining and Retrieval Leiden (TMRL) focusses on text mining and retrieval problems in complex domains. The methods they develop build on state-of-the-art Natural Language Processing methods. Current projects implement and evaluate methods in the legal, the archaeological, the policy-making, and the health domain. The textual data used is diverse. Examples include grey literature reports, scientific and legal publications, EU law texts, health records, user-generated content in online patient communities (discussion forums), and news posts on social media.
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Health Data Science
Health research, medical practice and consequently the whole population is increasingly affected by digitization, data science and AI. The possibilities for improving health outcomes on the individual, group and population level are vast, since more data becomes available and is increasingly being combined for improved risk detection, diagnosis, treatment and etiological research. Our group is concerned with analysing structured and unstructured data sources (real world data, routine care data, environmental data) for extracting new knowledge or prediction of health outcomes, by e.g. designing digital biomarkers and update /calibrate published models (the evidence base).
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