Dr Zeike Taylor
- Position: Associate Professor
- Areas of expertise: medical simulation, computer-assisted interventions, medical image computing, surgical simulation, computational biomechanics
- Email: Z.Taylor@leeds.ac.uk
- Phone: +44(0)113 343 0767
- Location: 6.06a EC Stoner / 2.40d Mechanical Engineering
- Website: My Personal Site | LinkedIn | Googlescholar
I joined the School of Mechanical Engineering as an Associate Professor in 2018. Prior to this, I was Senior Lecturer in the Dept of Mechanical Engineering at the University of Sheffield. I obtained a first class degree in Mechanical Engineering and a PhD in Biomechanical Engineering from the University of Western Australia in 2002 and 2006, respectively. For the latter I was supported by a competitive Australian Postgraduate Award scholarship and an industrial scholarship through Optiscan Imaging Ltd. I subsequently held postdoctoral positions with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia, University College London, and the University of Queensland. Within the University, I am Deputy Director of the CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine (www.cistib.org), and a member of the Institute of Medical and Biological Engineering.
- Deputy Director, CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine
My core expertise is in the computational modelling of biological tissues and structures, and in use of such models for simulating therapeutic interventions. The applications of these technologies have been wide ranging. E.g. they have formed the basis of interactive (VR) simulators for surgeon training or for rehearsing complex interventions, wherein the fidelity of the simulation critically depends on accurate, yet fast (real-time) prediction of tissue deformation. During my time at CSIRO and UCL, I led development of one of the first GPU-based finite element libraries, which enabled real-time solution of fully non-linear FE models.
During real interventions, the same technologies have been used to predict and compensate for organ deformations, and thereby to improve the accuracy of treatment delivery (so-called computer-assisted interventions). This is especially important for focal therapies, which aim for very localised effects and minimal disruption of surrounding healthy tissues, and for navigation systems for surgical robots.
A third, more recent stream of research concerns development of simulation tools for so-called in silico clinical trials of medical devices. These aim to augment and partially replace traditional trials, which are hugely expensive and time-consuming, with simulated tests on large cohorts of virtual patients. While substantially cheaper and faster, in silico approaches aim also to allow more thorough ‘testing’ of devices under wider ranges of physiological conditions. My group has begun in particular to explore new modelling techniques for this purpose, which integrate ‘traditional’ mechanistic (finite element) approaches with advanced machine learning tools.
I lead the Intervention Planning and Real-Time Computational Update Theme within the EPSRC UK Image-Guided Therapues Network+: www.image-guided-therapies.ac.uk.
Check out our GPU-based FE package NiftySim, here: http://sourceforge.net/projects/niftysim.<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>
- BEng (Western Australia)
- PhD (Western Australia)
- PGCert (Sheffield)
- Senior Member, IEEE
- Fellow, Higher Education Academy
- Member, MICCAI Society
I am involved in teaching of finite element methods in undergraduate and masters programmes. I also supervise undergraduate and masters student projects.
Research groups and institutes
- Institute of Medical and Biological Engineering
- Computational Medicine
Current postgraduate researchers
<li><a href="//phd.leeds.ac.uk/project/1062-domain-aware-deep-learning-models-for-fast-and-accurate-biomechanical-simulation">Domain-aware deep learning models for fast and accurate biomechanical simulation</a></li>