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Data Science Methodology

Data Science Methodology

Pushing the boundaries of data science

The data science field is rapidly developing with new methods and models. Our academic experts are at the forefront of these methodological developments. We work on advancing methodology without losing track of the applicability of the methods. With a large team of experts, we cover many sub-fields of data science from machine learning to text analytics and from robust statistics to optimization and high-dimensional Bayesian modelling.

Our research is motivated by questions from practice requiring new methodology, or by more fundamental methodological questions. We specifically consider a methodology that is useful for applications in business and economics. Examples of our research interests are:

  • Machine learning
  • Optimization
  • Privacy-preserving methods
  • Explainable AI, Interpretation of black-box models
  • Causal machine learning
  • Anomaly detection and robust methods
  • Approximate inference
  • High-dimensional methods
  • Dealing with missing data
  • Data/text mining
  • Knowledge representation
  • Visualization of datasets and results

Research Projects


Dr. Dennis Fok

Professor of Econometrics and Data Science at ESE & Expert Practice Director of Data Science Methodology

Dr. Kristiaan Glorie

Executive Director at Erasmus Q-Intelligence


Academic directors

  • Dennis Fok // Professor and Director // Data Science, Econometrics, Bayesian statistics
  • Dr. Kristiaan Glorie // Executive Director at Erasmus Q-Intelligence // Econometrics, Data science, Machine learning, Operations research.

Other experts

  • Andreas Alfons // Associate Professor // Anomaly detection, Robust methods, Regularization, Missing data
  • Carlo Cavicchia // Assistant Professor // Unsupervised classification, Latent variable models
  • Flavius Frasincar // Assistant Professor // Text analysis, Semantic Web, Data mining, Informatics
  • Maria Grith // Assistant Professor // Functional analysis, Finance
  • Patrick Groenen // Professor // Data Science, Computational Statistics, Regularization, Visualisation, Classification, Multidimensional scaling
  • Kathrin Gruber // Assistant Professor // Probabilistic graphical models, Bayesian and approximate computation
  • Alex Koning // Assistant Professor // Mathematical statistics
  • Andrea Naghi // Assistant Professor // Causal machine learning
  • Eoghan O’Neill // Assistant Professor // Tree-based inference, Bayesian statistics, Energy economics
  • Anastasija Tetereva // Assistant Professor // Text analysis, Finance
  • Michel van de Velden // Associate Professor // Visualization, Multivariate statistics
  • Phyllis Wan // Assistant Professor // Extreme values, Network analysis
  • Mikhail Zhelonkin // Assistant Professor // Robust Statistics, Causal inference
  • Chen Zhou // Professor // Mathematical Statistics, Risk Management

Current PhD students

  • Jiawei Fu
  • Utku Karaca
  • Jens Klooster
  • Luuk van Maasakkers
  • Daniël Touw
  • Max Welz
  • Terri van der Zwan