On February 19, 2020, the RSS West Midlands local group held an event at the University of Warwick titled 'Data Science at the National Physical Laboratory'. The speaker was Kavya Jagan from the National Physical Laboratory.
The West Midlands Local Group is this year celebrating its 75th anniversary. Its first meeting in 1945 enjoyed a talk from the National Physical Laboratory, and so it was with particular pleasure that the group welcomed back NPL to help celebrate its 75th year in the form of their senior data scientist Kavya Jagan.
NPL are the custodians of looking after calibrating how we measure the seven fundamental units of the natural world, so ensuring that different facilities around the country get the same answer when measuring the same thing, in part to maintain a regulatory system to control trade based on defined quantities, and developing new measurement standards and techniques.
These seven fundamental quantities (with units) are mass (kilogram - kg), length (metre - m), time(seconds - s), temperature (Kelvin - K), amount of substance (mole - mol), luminous intensity (candela -cd), electric current (Ampere - A).
Every derived quantity is a power function of these fundamental units (for example the volt has units kgm2s-3A-1). Previous employees of NPL include Alan Turing where he was instrumental in developing the world’s first Automatic Computing Engine in 1946, and radar was co-developed at NPL in 1935.
There is much more to NPL than just measuring stuff; it works with a number of outside organisations (including industry), in areas such as advanced manufacturing, energy & environment, and life sciences & health, where the work is focussed on measurement traceability and uncertainty analysis.
Kavya illustrated this extracurricular activity via a number of case studies including:
- Decision making on the degree of severity of myocardial blood flow abnormalities, utilizing techniques in image analysis and machine learning, for better diagnosing heart disease
- Clustering satellite data of ground movement, whereby the aim is to preserve important features in the data in a lower dimensional space, with the aim of monitoring air quality
- Noise monitoring using data from acoustic sensor networks using Guassian Process modelling to model acoustic noise in urban environments.
Event summary by Tim Davis (timdavis consulting ltd.)