Kathryn finished her studies in theoretical physics in 2016 at the University of Sussex, and joined NPL soon after completing her PhD in cold atom trapping. Kathryn originally joined NPL in the time and frequency department but is now a member of the data science department working on autonomous vehicle metrology.
Kathryn’s expertise is in mathematical modelling and data analysis. She uses traditional and data driven modelling to realistically model and understand uncertainties to support developments in autonomous vehicles of all types (cars, vans, drones, boats, etc…).
She is also involved with work modelling how earthquakes affect optical fibre cables, has a passion for knowledge transfer working collaboratively with the NPL Training team to develop training resources and is a member of the Institute of Physics.
Areas of interest
- Assured autonomy – traceability and trustworthiness for autonomous vehicle sensors and systems
- Mathematical modelling of applied physical systems and fundamental physics experiments
- Machine learning – particularly in quantifying uncertainty and principled use of AI algorithms
- Atomic physics, particularly in uncertainty calibrations for atomic clocks
- Learning pathways for knowledge transfer and upskilling of scientists in academia and industry
Key publications
Measuring atom positions in a microwave cavity to evaluate distributed cavity phase shifts,
K. Burrows, R. J. Hendricks, K. Szymaniec, K. Gibble, S. Beattie and B. Jian
Metrologia 57, 065003 (2020).
The role of unit systems in expressing and testing the laws of nature.
P. Quincey and K. Burrows
Metrologia 56, 6, 065001 (2019).
Nonadiabatic losses from radio-frequency-dressed cold-atom traps: beyond the Landau-Zener model.
K. Burrows, H. Perrin and B. M. Garraway,
Physical Review A, 96, 2 (2017).
Email Kathryn Khatry
ORCID