“My goal is to help further the understanding of biology and disease through integration of large and heterogeneous datasets with a strong emphasis on human interpretability of the computationally inferred models. In order to bridge the gap between computational and biological complexity on the one hand and human interpretation on the other, my research focuses on: logic models, multiscale analysis, interpretation of ensemble classifiers, statistical hypothesis testing and visualization.” – Theo
Theo Knijnenburg was born in Leidschendam, The Netherlands, on August 21, 1980. In 1998 he started his study Electrical Engineering at the Delft University of Technology. During a three-month internship in 2002 he worked in British Telecom’s Future Content Group in Ipswich, UK, on object detection in video for portable devices. In 2004 he obtained his M.Sc. degree in Electrical Engineering after doing his graduation work in the Information and Communication Theory Group at the Delft University of Technology on feature selection in gene expression based tumor classification problems. In 2004 he started his Ph.D. study in the Information and Communication Theory Group on unraveling the transcriptional program of the yeast Saccharomyces cerevisiae. His Ph.D. project was performed in collaboration with the Industrial Microbiology Group at the Delft University of Technology and was part of the Kluyver Centre for Industrial Fermentation. From October 2008 until December 2010 he worked as a postdoctoral researcher at the Institute for Systems Biology, Seattle, US. His research topics included applications of statistical testing in computational biology and the development of novel analysis methods for genomics data, such as flow cytometry, ChIP-seq and selected reaction monitoring mass spectrometry. From January 2011 until January 2013 he worked as a postdoctoral researcher at the Netherlands Cancer Institute, Amsterdam, The Netherlands. His research focused on systems-level modeling of cancer onset and progression using genome-wide heterogeneous data. Specifically, he developed computational models that are both predictive and interpretable, thereby facilitating hypothesis formation and further experimentation by cancer biologists. In February 2013 he returned to the Institute for Systems Biology, Seattle, US, to work as a research scientist on the statistical analysis of genome-wide heterogeneous data sets. His research focuses on derivation of predictive and interpretable models of complex diseases by integrating molecular data with formalized knowledge from functional annotation databases and literature. He continues to actively collaborate with the Bioinformatics Lab at the Delft University of Technology and the Bioinformatics and Statistics Group at theNetherlands Cancer Institute.
Digital Signal Processing
PhD, Delft University of Technology