David Whittaker

Dr David Whittaker has a physical sciences background, with experience of radiation physics at postgraduate level and of scientific programming and computational hydrodynamic and atomic physics modelling for doctoral and postdoctoral work involving high-power lasers and plasmas. He is a Higher Research Scientist in the Digital Manufacturing team of the Data Science group, having joined NPL at the end of 2018. More recently, his interests have broadened to include process optimisation and the application of machine learning to industrial processes.

Current Interests

• Power quality assessment
• Numerical methods and Monte Carlo simulation
• Uncertainty quantification in the context of machine learning
• Synthetic data generation

Recent Publication

P. M. Duncan and D. S. Whittaker, “Distribution identification and information loss in a measurement uncertainty network”, Metrologia 58 034003 (2021), Distribution identification and information loss in a measurement uncertainty network - IOPscience