Michel van de Velden

Michel van de Velden is an Associate Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. His work bridges statistical methodology and data science, with a focus on exploratory techniques and machine learning.

His research interests lie primarily in exploratory data analysis, with a focus on dimension reduction, cluster analysis, and data visualization. He also actively contributes to supervised machine learning, particularly using tree-based methods such as random forests. His methodological work often addresses challenges in categorical data and mixed-type variables.

The recent articles highlight a strong trend in developing exact and unbiased statistical methods for clustering and distance computation. His work spans correspondence analysis, hierarchical clustering algorithms, and software tools for mixed data, reflecting a blend of theoretical rigor and practical implementation.

He has served on the editorial boards of Computational Statistics and Statistical Papers, contributing to academic peer review and publication quality.

Michel van de Velden has supervised academic work and contributed to open datasets and software, indicating active mentorship and collaboration. His research is supported by ongoing publications and editorial activities, suggesting sustained engagement in the academic community.

He is involved in developing software such as manystat and datasets related to categorical data visualization, indicating leadership in reproducible and open research practices.