We develop theory, methods and software for highly scalable and intelligent digital experiments. Traditional approaches to computational study of natural systems fail when data becomes very big, fast or complex, and when models become high-dimensional, stochastic and when they involve processes at widely disparate temporal and spatial scales (multiscale problems). A key focus of our group is how to design intelligent and interactive systems to automate engineering tasks such as parameter inference, model selection and model exploration. Many of our projects are driven by applications in the life sciences, in particular systems biology.
From flexible e-infrastructure to intelligent applications
We have formed a scientific team whose collective expertise spans mathematical modeling, scientific computing, machine learning, cloud infrastructure, data engineering, parallel programming, systems security, life science and more. We do most of our work together with academic and industrial collaborators. A current example of this is HASTE – an SSF funded project aiming at developing smart data management pipelines to handle the explosion of scientific data from microscopy experiments .