Since my graduation, I have been with the Philips Research Laboratories in Eindhoven. From May 1990 until February 1998 I have been a member of the Phideo project in the group Digital VLSI, of which I have been project leader from January 1996 until February 1998. In this project, I have been working on high-level synthesis of DSP systems for video applications, with the emphasis on scheduling problems and techniques. Based on my work in the Phideo project, I received a Ph.D. degree in 1995 from the Eindhoven University of Technology, The Netherlands. Furthermore, I received the best CAD paper award of the Electronic Design & Test Conference 1997 for the paper entitled Multidimensional Periodic Scheduling: A Solution Approach.
From February 1998 till November 2000, I have been a member of the group New Media Systems & Applications in which I was leader of the cluster Quality of Service. The aim of this cluster was to analyze and optimize the quality of service in multimedia systems and networks by means of mathematical modelling and optimization techniques and algorithms.
From November 2000 till December 2005, I have been a member of the group Media Interaction, which is basically a merger of the groups New Media Systems & Applications (NMSA) and User System Interaction Technology (USIT). In this group I have been heading the cluster Adaptive Algorithms, where on one hand we worked on machine learning for e.g. content filtering, and on the other hand work on on-line optimization for application-level resource management. This latter work resulted in another best paper award, for the paper entitled QoS Control Strategies for High-Quality Video Processing. After that, I have been a member of the cluster Intelligent Algorithms.
Since January 2006, I am a member of the group Molecular Diagnostics, where I am exploring the field of bioinformatics. More specifically, the topic that I am working on is analysis of biomolecular data sets, such as gene expression data or proteomics data, in order to find patterns that are predictive for e.g. disease progression. At first, we did this in a more data-driven way, but since a few years, we do this much more in a biology-driven way. This means, for instance, that we start from what is known about oncogenic signaling pathways, and build models to interpret genomic data based on this, so we can predict which kind of targeted cancer therapy may work best for an individual patient.
My other activities include: