Genomics Data Analysis | Machine Learning | Statistical Inference | Bayesian Methods | Clinical Prediction Modeling

• Analysis of high-dimensional data, mostly genomics
• Machine learning with small sample size
• Statistical inference (testing, confidence intervals, etc)
• Application and development of Bayesian methods for medical data

Data drives most of my statistical omics research: provide a generic, robust solution for a given study, and one likely solves similar problems for many studies. My research interests cover a wide spectrum, including high-dimensional data analysis (omics) and predictive modeling, incl. machine learning. My main fascination nowadays is omics-based clinical prediction and classification, by either statistical or machine learners. Here, I focus on developing methods to improve predictive performance and biomarker selection by structural use of complementary data (co-data), e.g. from external studies or data bases. Moreover, we develop tools to aid interpretation of ML, e.g. by providing inference for variable importance metrics. We directly apply and test such methods in a number of collaborative projects on cancer diagnostics and prognostics.

Activities

• Teaching: Biostatistics topics in several medical tracks (VU University) and High-dimensional data analysis in the Statistics and Data Science Master programme (Leiden University)
• Consult: Supporting Amsterdam UMC medical researchers, with a focus on analysis of omics data and machine learning