The ongoing transformation of biology to a quantitative discipline raises as many opportunities as challenges. The many -omics projects (genomics, proteomics, transcriptomics, metabolomics, to only name a few) allow us to map and identify all components of a living cell at the molecular level, from both a physical and functional standpoint. New technologies such as high resolution time-lapse microscopy and micro-scale devices have vastly enhanced our abilities to study the mechanics of biomolecules, cells, and tissues, giving us hope that we will be able to unravel the fundamentals of life. In addition to these technological advances, computational methods are playing an ever growing role in biology. As physical models improve and greater computational power becomes available, simulation of complex biological processes will become increasingly tractable. The challenges however come in analyzing and interpreting the vast amount of data generated from these disciplines. We need new methods for extracting knowledge from data, as well as new simulation methods that allow us to implement this knowledge into holistic models that will enable understanding. This needs have been the major drive in my scientific career.

	New algorithms for clustering post-genomic data, derived from statistics (read more).
	Powerful geometric methods for processing image data and for comparisons of 3D-images in a high-throughput manner (read more).
	Efficient and robust solvers of partial differential equations to understand the solvation and thermodynamics of drug targets (read more).
	In addition, many ideas have been developed to understand the rich information contained in sequence data, making use of phylogenetic information, as well as of the geometric and structural properties of macromolecules (read more).
	Ethics in biological and biomedical research, with a special focus on the use of Artificial Intelligence for Healthcare