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Department of Applied Mathematics and Theoretical Physics

Career

  • 2018-: PhD, CCIMI/DAMTP, University of Cambridge
  • 2017-2018: Research Assistant, MRC Biostatistics Unit
  • 2016-2017: MPhil in Computational Biology, University of Cambridge
  • 2013-2016: BA in Mathematics, University of Cambridge

Research

Tom is a member of the Department of Applied Mathematics and Theoretical Physics Computational Biology research group, supervised by Dr Stephen Eglen and Dr Ari Ercole. His current research interests are in intensive care unit big data.

Publications

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation
T Edinburgh, SJ Eglen, P Thoral, P Elbers, A Ercole
– Gigabyte
(2022)
2022,
1
Causality indices for bivariate time series data: a comparative review of performance
T Edinburgh, SJ Eglen, A Ercole
– Chaos: an interdisciplinary journal of nonlinear science
(2021)
31,
083111
Causality indices for bivariate time series data: A comparative review of performance.
T Edinburgh, SJ Eglen, A Ercole
– Chaos (Woodbury, N.Y.)
(2021)
31,
083111
DeepClean: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning
T Edinburgh, M Czosnyka, P Smielewski, M Cabeleira, S Eglen, A Ercole
– Acta Neurochirurgica: Supplementum
(2021)
131,
235
DeepClean - self-supervised artefact rejection for intensive care waveform data using generative deep learning.
T Edinburgh, P Smielewski, M Czosnyka, M Cabeleira, SJ Eglen, A Ercole
(2019)
meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.
S Gelfman, Q Wang, Y-F Lu, D Hall, CD Bostick, R Dhindsa, M Halvorsen, KM McSweeney, E Cotterill, T Edinburgh, MA Beaumont, WN Frankel, S Petrovski, AS Allen, MJ Boland, DB Goldstein, SJ Eglen
– PLoS Comput Biol
(2018)
14,
e1006506

Research Groups

Cantab Capital Institute for the Mathematics of Information
Computational Biology

Room

FL.04

Telephone

01223 763139