National Physical Laboratory

Data science has become a topic of great interest lately and has triggered new widescale research activities around efficientl first order methods for optimisation and Bayesian sampling. The National Physical Laboratory is addressing some of these challenges with particular focus on  robustness and confidence in the solution.  In this talk, I will present some problems and recent results concerning i. robust learning in the presence of outliers based on the Median of Means (MoM) principle and ii. stability of the solution in super-resolution (joint work with A. Thompson and B. Toader).

The National Physical Laboratory is the national measurement institute. Researchers in the Data Science Division analyse various types of data using mathematical, statistical and machine learning based methods. The goal of the workshop is to describe a set of exciting mathematical problems that are of interest to NPL and more generally to the Data Science community. In particular, I will describe the problem of clustering using minimum spanning trees (MST-Clustering), Non-Negative Matrix Factorisation (NMF), adaptive Compressed Sensing (CS) for tomography, and sparse polynomial chaos expansion (PCE) for parametrised PDE’s.