Wouter van Loon
Wouter van Loon is a PhD candidate at the department of Methodology and Statistics and the Leiden Centre of Data Science (LCDS). He obtained his Master’s degree in Statistical Science (cum laude) at Leiden University. His research primarily concerns supervised learning algorithms for combining information from different types of high-dimensional data.
The aim of his PhD project is to develop accurate but interpretable ensemble learning methods for high-dimensional multi-domain data. Nowadays, researchers are confronted with multi-domain data more and more often. In health research, for example, multi-domain data can occur when data are collected from multiple sources (e.g. medical imaging, genomics, questionnaires), or when different feature sets are derived from a single source (e.g. different MRI modalities). Combining data from multiple domains can potentially lead to improved early diagnosis of disease. Furthermore, identification of important domains can lead to simpler, more interpretable diagnostic models.
This project is part of the Data Science Research Programme.
Wouter is a tutorial instructor for the second-year bachelor course on Multivariate Data Analysis.
- Van Loon W., Fokkema M., Szabo B. & De Rooij M. (2020), Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning, Information Fusion 61: 113-123.
No relevant ancillary activities