Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Imperial College Research Fellow



james.avery Website




036Paterson WingSt Mary's Campus





James is a Research Associate in the Hamlyn Centre, Institute of Global Health Innovation and the Department of Surgery and Cancer, St Mary's Hospital. He received an MEng in Acoustical Engineering at the ISVR at the University of Southampton and completed his PhD in Biomedical Engineering at University College London in 2015. There he continued his work as an EPSRC Doctoral Research Fellow, developing Electrical Impedance Tomography methods for brain imaging as part of Prof. David Holder’s Neurophysiology lab.

Clinical studies during this time brought into sharp focus the benefits that good, open and reproducible engineering can have for patients and strengthened his desire to translate his work into clinical practice. Since 2018 he has worked as a postdoctoral researcher at the NIHR Imperial Biomedical Research Centre, seeking to develop new sensor technologies for surgery.

His research interests include:

  • Electrical Impedance Tomography
  • Surgical Robotics
  • Soft sensors
  • Implantable and wearable sensors
  • Open source biomedical devices and methods
  • Fixing things, more than likely after having broken them in the first place. Particularly 3D printers. 



Hannan S, Faulkner M, Aristovich K, et al., 2020, Optimised induction of on-demand focal hippocampal and neocortical seizures by electrical stimulation, Journal of Neuroscience Methods, Vol:346, ISSN:0165-0270, Pages:108911-108911

Avery J, Tactile sensor for minimally invasive surgery using Electrical Impedance Tomography, Ieee Transactions on Medical Robotics and Bionics

Dowrick T, Avery J, Faulkner M, et al., 2020, EIT-MESHER – Segmented FEM mesh generation and refinement, Journal of Open Research Software, Vol:8, ISSN:2049-9647, Pages:1-4

McDermott BJ, Elahi A, Santorelli A, et al., 2020, Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis, Physiological Measurement, Vol:41, ISSN:0967-3334, Pages:1-17


Avery J, Tom D, Aristovich K, et al., 2020, EIT Mesher, v.v1.0

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