James Taylor
PROFILE
James Taylor is Professor of Control Engineering in the School of Engineering at Lancaster University. His interdisciplinary research on statistical modelling and control of uncertain systems in the natural sciences and engineering, involves applications spanning robotics, transport, energy, health, and the environment. He supervises projects in these areas for students across a spectrum of mechanical, electronic, nuclear, and chemical engineering disciplines.
FORMERLY
James first arrived at Lancaster University as an undergraduate student of Biological Sciences but graduated with BSc (Hons) (1992) and PhD (1996) degrees in Environmental Science. His PhD on control system design was supervised by Professor Peter Young and Dr Arun Chotai. He was research associate in Environmental Science for six years, before his appointment to an academic position in the Engineering Department, now School, in 2000.
Across his career, he has been investigator for a diverse portfolio of UK research council grants and industry funding, covering work such as responsive manufacturing, nuclear decommissioning robotics, wave-energy conversion, and adaptive medical treatment. Alongside such projects, he continues to use and to co-develop the CAPTAIN Toolbox.
Publications from 2022
Bandala, M., Chard, P., Cockbain, N., Dunphy, D., Eaves, D., Hutchinson, D., Lee, D., Ma, X., Marshall, S., Murray, P., Parker, A., Stirzaker, P., Taylor, C.J., Zabalza. J. and Joyce, M. J. (2025) A responsive integrated dry route for uranium hexafluoride conversion using machine intelligence, Energy Conversion and Management: X, 28, 101230.
Khan, I.U., Ali, A., Taylor, C.J. and Ma, X. (2025) Data-driven insights: boosting algorithms to uncover electricity theft patterns in AMI, IEEE Transactions on Instrumentation and Measurement, 74, 2524212.
Dunphy, R.D., Parker, A.J., Bandala, M., Bennet, S., Boxall, C., Chard, P., Cockbain, N., Eaves, D., Goddard, D., Ma, X., Taylor, C.J., Wilbraham, R., Zabalza, J., Murray, P. and Joyce, M.J. (2025) Hyperspectral imaging suggests potential for rapid quantification of fission products in spent nuclear fuel, Scientific Reports, 15, 5434.
Wu, Y., Sheng, W., Taylor, C.J., Aggidis, G., Ma, X. (2024) TALOS wave energy converter power output prediction analysis based on a machine learning approach, International Journal of Offshore and Polar Engineering, 34, pp. 306–313.
Bandala, M., Chard, P., Cockbain, N., Dunphy, D., Eaves, D., Hutchinson, D., Lee, D., Ma, X., Marshall, S., Murray, P., Parker, A., Stirzaker, P., Taylor, C.J., Zabalza, J. and Joyce, M.J. (2024) Digital twin challenges and opportunities for nuclear fuel manufacturing applications, Nuclear Engineering and Design, 420, 113013.
Tsitsimpelis, I., Alton, T., West, A., Taylor, C.J., Lennox, B., Livens, F.R. and Joyce, M.J. (2023) Localizing and identifying radionuclides via energy-resolved angular photon responses, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1057, 168771.
Khan, I.U., Javaid, N., Taylor, C.J. and Ma, X. (2023) Robust data driven analysis for electricity theft attack-resilient power grid, IEEE Transactions on Power Systems, 38, pp. 537–548.
Khan, I.U., Javaid, N., Taylor, C.J., Gamage, K.A.A. and Ma, X. (2022) A stacked machine and deep learning–based approach for analysing electricity theft in smart grids, IEEE Transactions on Smart Grid, 13, pp. 1633–1644.
Tsitsimpelis, I., Alton, T.L., West, A., Taylor, C.J., Livens, F.R., Lennox, B. and Joyce, M.J. (2022) Improved localization of radioactivity with a normalized sinc transform, Frontiers in Nuclear Engineering, 1, 989361.
Sheng, W., Tapoglou, E., Ma, X., Taylor C.J., Dorrell, R.M., Parsons, D.R. and Aggidis, G. (2022) Hydrodynamic studies of floating structures: comparison of wave–structure interaction modelling, Ocean Engineering, 249, 110878.
A full list of publications are available from Lancaster University at https://www.lancaster.ac.uk/engineering/about/people/james-taylor
