Tackling these problems usually involve a combination of learning, optimization and sampling. Learning involves fitting predictive models to understand designs. Optimization allows us to develop the next best design to test, while sampling acknowledges the inherent uncertainty in biological systems. I am fond of methods that one can be implement in a few lines of code (my PhD was about kernel methods). Most of the fun is discovering a new application or modification for an existing algorithm to find a wholly new use.
As part of the faculty of bioscience engineering, I work together with various people on applications in life science. My main interest is in community ecology, microbiomes and synthetic biology, though I always keep my eyes open for food-related ideas for hobby projects. Currently, I guide five PhD students. Two are working on methodological techniques; three are working on applications: designer phages, biofilm detection and microbiome engineering.
In research, personal or when working with others, I value learning and trying new things, creativity and collaboration as key principles.