What we aim ?
GeomScale is a research and development project that delivers open source code for state-of-the-art algorithms at the intersection of data science, optimization, geometric, and statistical computing.
The current focus of GeomScale is scalable algorithms for sampling from high-dimensional distributions, integration, convex optimization, and their applications. One of our ambitions is to fill the gap between theory and practice by turning state-of-the-art theoretical tools in geometry and optimization to state-of-the-art implementations.
We believe that towards this goal, we will deliver various innovative solutions in a variety of application fields, like finance, computational biology, and statistics that will extend the limits of contemporary computational tools.
GeomScale aims in serving as a building block for an international, interdisciplinary, and open community in high dimensional geometrical and statistical computing.
Scientific background, problems and applications
The main computational problem is sampling from high-dimensional distributions. Sampling is a fundamental operation that plays a crucial role across sciences including modern machine learning and data science. We work on the development of practical algorithms based on sampling for a set of fundamental computational problems such as convex optimization, integration and volume computation.
Regarding applications GeomScale project provides efficient geometric algorithms for estimating high-dimensional copulas that are useful in computational finance, in particular financial crisis prediction. Moreover, GeomScale hosts a software framework for analysis of metabolic networks that given a metabolic model generates high-dimensional random sampling of metabolic fluxes that in turn provide an unbiased description of the capabilities of the metabolic network.