College: Lady Margaret Hall
Location: The Podium Institute, Old Road Campus Research Building, Oxford OX3 7DQ
My interest in research stems from a concussion I sustained from playing rugby union, which led to post-concussion syndrome after I was not taken off the field. After completing my MEng at the University of Oxford in 2022, I was excited to realise I could contribute to the understanding and prevention of SR-TBI through further research.
Research Project
Title: A coupled experimental-numerical framework for the prediction of sports-related traumatic brain injury (SR-TBI)
Summary: My research aims to use mechanics-informed machine learning to predict concussion from on-the-day assessments of athletes. A surrogate for brain finite element simulations is being developed, to allow real-time insight into the mechanics of injury. To improve injury identification, this insight will be combined with conventional injury assessment methods.
Supervisors: Prof. Antoine Jerusalem and Prof. Jeroen Bergmann
Performance of current tools used for on-the-day assessment and diagnosis of mild traumatic brain injury in sport: a systematic review, P. Haste, L. de Almeida e Bueno, A. Jérusalem, J. Bergmann, BMJ Open Sport & Exercise Medicine 2025;11:e001904.,
A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations. Y. Wei, J. Oldroyd, P. Haste, J. Jayamohan, M. Jones, N. Casey, J.M. Peña, S. Baylis, S. Gilmour, A. Jérusalem, Communications Engineering, In Press.
Feature Importance for Estimating Rating of Perceived Exertion from Cardiorespiratory Signals Using Machine Learning, R Cheng, P Haste, E Levens, J Bergmann, Frontiers in Sports and Active Living 6, 1448243