Paul Docherty

ProfessorPaul Docherty

Director of Studies 2nd Year Engineering
Civil Mechanical E510
I'm keen on finding unique ways that complex engineering methodologies and approaches can be used to help people in society live better lives.


Research Interests

I have been lucky enough to work with some amazing people across a variety of research fields. This has led me to engage across many applications and develop a very diverse research skill set. I work predominantly with numerical methods in bioengineering. This concentrates in the forward problem, the inverse problem, and identifiability - typically in lumped parameter systems. However, I have also worked extensively in PIV analysis of biological flows, head injury mechanics and qualitative research in education. The full scope that I have worked in includes agricultural design, cellular modelling, aerospace design, and medical device design.

Recent Publications

  • Abdulbaki Alshirbaji T., Jalal NA., Docherty PD., Neumuth T. and Möller K. (2022) Robustness of Convolutional Neural Networks for Surgical Tool Classification in Laparoscopic Videos from Multiple Sources and of Multiple Types: A Systematic Evaluation. Electronics (Switzerland) 11(18)
  • Cao F., Docherty PD. and Chen XQ. (2022) Contact force estimation for serial manipulator based on weighted moving average with variable span and standard Kalman filter with automatic tuning. International Journal of Advanced Manufacturing Technology 118(9-10): 3443-3456.
  • Docherty PD., Zaka PA. and Fox-Turnbull W. (2022) A quantitative analysis of the short-term and mid-term benefit of a flipped classroom for foundational engineering dynamics. Research Papers in Education 37(6): 860-874.
  • Emanuel RHK., Roberts J., Docherty PD., Lunt H., Campbell RE. and Möller K. (2022) A review of the hormones involved in the endocrine dysfunctions of polycystic ovary syndrome and their interactions. Frontiers in Endocrinology 13
  • Jalal NA., Arabian H., Alshirbaji TA., Docherty PD., Neumuth T. and Moeller K. (2022) Analysing attention convolutional neural network for surgical tool localisation: a feasibility study. Current Directions in Biomedical Engineering 8(2): 548-551.