Computer Science & AI
Data science
The majority of scientists, from archaeologists through to zoologists, collect enormous volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics and computer science, we can develop algorithms for analyzing and identifying patterns in the big data from many specialist fields, and apply them to obtain novel insights.
Computational network science
dr. Frank Takes (head)
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.
More information about the CNS Lab
Explanatory data analysis
dr. Matthijs van Leeuwen (head)
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).
More information about the Explanatory Data Analysis group
Data mining & sports
dr. Arno Knobbe (head)
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 and data mining to make predictions from this data and to discover new underlying patterns that would otherwise have been unnoticed.
Data Science for Social Good (DSSG)
Prof.dr.ir. Wessel Kraaij (head)
DSSG is a group of data science researchers. The mission of the group is to investigate and develop methods and technology – in particular based on data science – to support initiatives that aim at social good. Recent economic and technological developments have shown the potential of using data-intensive methods for powerful digital services and platforms. These technologies have been welcomed by private equity as an effective means to increase shareholder value. However, in many cases focused attention on increased shareholder value leads to increased social inequality and decreased solidarity/trust, forming a real threat to our democratic society.
DSSG believes that data science methods can contribute to social good, that is to public value for citizens and citizen organisations. Data science, machine learning and digital methods have the potential to help e.g. citizen initiatives, or locally operating social impact driven companies that are motivated to contribute to the large societal transitions we are facing.
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Health Data Science
Prof.dr.ir. Wessel Kraaij (head)
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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