Dr Catherine Higham

  • Research Associate (School of Computing Science)
  • Honorary Research Fellow (School of Mathematics & Statistics Administration)

telephone: 01413301626
email: Catherine.Higham@glasgow.ac.uk

Biography

My current position as a senior member of Prof. Rod Murray-Smith’s Inference, Dynamics and Interaction group involves applying state-of-the-art machine learning and statistical methods to inverse problems in image processing arising from recent advances in Quantum Optics. The research associate position is supported by the EPSRC UK Quantum Technology Programme under grant P/M01326X/1. 

My previous position was Research Associate on the FP7 funded TiMet project 2012-2015, which brought together leading centres for the emerging discipline of systems biology. My role focused on developing, testing and analysing new methods for parameter estimation and model selection using state of the art Markov Chain Monte Carlo sampling within a non-parametric Bayesian statistical framework. Overall, this work is motivated by the challenge of handling novel experimental data and developing quantitative answers to questions raised by experimentalist colleagues.

After a graduating with a mathematics degree from Oxford, I worked for Novaction, a brand/marketing consultancy based in Paris, France, who sponsored me through an business MBA at City University, London.  After moving to Scotland, I worked as a strategic analyst at Scottish Hydro-Electric.

Following a career break, I independently prepared and submitted a research proposal to the Daphne Jackson Trust, and obtained a two-year Fellowship 2006-2008. This was followed by a Lord Kelvin/Adam Smith PhD scholarship from the University of Glasgow, 2008-2012.

My PhD work took place in Professor Darren Monckton’s lab, which has accumulated one of the most comprehensive datasets concerned with unstable human DNA mutations. The remit for my PhD was to add value to this unique data resource by developing, calibrating and comparing new mathematical models describing the biological processes underlying the DNA dynamics of diseased individuals.

 

 

Research interests

My research interests are concerned with the development of machine learning algorithms that harness recent technological advances for data sensing and processing for scientific understanding and industrial/commercial application.

I have built up a range of skills in machine learning, deep learning, statistical inference, high performance computing mathematical modelling, and large-scale scientific computation. I have also gained a wide range of experience in interdisciplinary research alongside experimental scientists.

Publications

List by: Type | Date

Jump to: 2018 | 2015 | 2013 | 2012 | 2009
Number of items: 10.

2018

Caramazza, P., Boccolini, A., Buschek, D., Hullin, M., Higham, C. F., Henderson, R., Murray-Smith, R. and Faccio, D. (2018) Neural network identification of people hidden from view with a single-pixel, single-photon detector. Scientific Reports, 8, 11945. (doi:10.1038/s41598-018-30390-0) (PMID:30093701) (PMCID:PMC6085360)

Higham, C. F., Murray-Smith, R., Padgett, M. J. and Edgar, M. P. (2018) Deep learning for real-time single-pixel video. Scientific Reports, 8, 2369. (doi:10.1038/s41598-018-20521-y) (PMID:29403059) (PMCID:PMC5799195)

2015

Macdonald, B., Higham, C. and Husmeier, D. (2015) Controversy in mechanistic modelling with Gaussian processes. Journal of Machine Learning Research: Workshop and Conference Proceedings, 37, pp. 1539-1547.

Higham, C. F. and Husmeier, D. (2015) Inference of circadian regulatory pathways based on delay differential equations. In: Ortuno, F. and Ignacio, R. (eds.) Bioinformatics and Biomedical Engineering. Series: Lecture Notes in Computer Science, 9044 (9044). Springer, pp. 468-478. ISBN 9783319164793 (doi:10.1007/978-3-319-16480-9_46)

2013

Higham, C. and Monckton, D. (2013) Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease. Journal of the Royal Society: Interface, 10(88), p. 20130605. (doi:10.1098/​rsif.2013.0605)

Higham, C. and Husmeier, D. (2013) A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. BMC Bioinformatics, 14(Sup 10), S3. (doi:10.1186/1471-2105-14-S10-S3)

2012

Morales Montero, F. et al. (2012) Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity. Human Molecular Genetics, 21(16), pp. 3558-3567. (doi:10.1093/hmg/dds185)

Higham, C.F., Morales, F., Cobbold, C.A., Haydon, D.T. and Monckton, D.G. (2012) High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations. Human Molecular Genetics, 21(11), pp. 2450-2463. (doi:10.1093/hmg/dds059)

2009

Higham, C., Wilcox, D., Haydon, D. , Cobbold, C. and Monckton, D. (2009) Modelling dynamic DNA in mytonic dystrophy. In: Sixth International Workshop on Computational Systems Biology, WCSB 2009, Aarhus, Denmark, 10-12 Jun 2009, pp. 63-66.

Higham, C.F. (2009) Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop. BMC Systems Biology, 3(12), (doi:10.1186/1752-0509-3-12)

This list was generated on Tue Sep 18 04:07:49 2018 BST.
Number of items: 10.

Articles

Caramazza, P., Boccolini, A., Buschek, D., Hullin, M., Higham, C. F., Henderson, R., Murray-Smith, R. and Faccio, D. (2018) Neural network identification of people hidden from view with a single-pixel, single-photon detector. Scientific Reports, 8, 11945. (doi:10.1038/s41598-018-30390-0) (PMID:30093701) (PMCID:PMC6085360)

Higham, C. F., Murray-Smith, R., Padgett, M. J. and Edgar, M. P. (2018) Deep learning for real-time single-pixel video. Scientific Reports, 8, 2369. (doi:10.1038/s41598-018-20521-y) (PMID:29403059) (PMCID:PMC5799195)

Macdonald, B., Higham, C. and Husmeier, D. (2015) Controversy in mechanistic modelling with Gaussian processes. Journal of Machine Learning Research: Workshop and Conference Proceedings, 37, pp. 1539-1547.

Higham, C. and Monckton, D. (2013) Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease. Journal of the Royal Society: Interface, 10(88), p. 20130605. (doi:10.1098/​rsif.2013.0605)

Higham, C. and Husmeier, D. (2013) A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. BMC Bioinformatics, 14(Sup 10), S3. (doi:10.1186/1471-2105-14-S10-S3)

Morales Montero, F. et al. (2012) Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity. Human Molecular Genetics, 21(16), pp. 3558-3567. (doi:10.1093/hmg/dds185)

Higham, C.F., Morales, F., Cobbold, C.A., Haydon, D.T. and Monckton, D.G. (2012) High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations. Human Molecular Genetics, 21(11), pp. 2450-2463. (doi:10.1093/hmg/dds059)

Higham, C.F. (2009) Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop. BMC Systems Biology, 3(12), (doi:10.1186/1752-0509-3-12)

Book Sections

Higham, C. F. and Husmeier, D. (2015) Inference of circadian regulatory pathways based on delay differential equations. In: Ortuno, F. and Ignacio, R. (eds.) Bioinformatics and Biomedical Engineering. Series: Lecture Notes in Computer Science, 9044 (9044). Springer, pp. 468-478. ISBN 9783319164793 (doi:10.1007/978-3-319-16480-9_46)

Conference Proceedings

Higham, C., Wilcox, D., Haydon, D. , Cobbold, C. and Monckton, D. (2009) Modelling dynamic DNA in mytonic dystrophy. In: Sixth International Workshop on Computational Systems Biology, WCSB 2009, Aarhus, Denmark, 10-12 Jun 2009, pp. 63-66.

This list was generated on Tue Sep 18 04:07:49 2018 BST.