Opportunities

Generally, funded opportunities will be announced here as and when
they arise. However, some studentship opportunities are only
available after the student is identified so prospective students
are encouraged to get in touch as early as possible.
Please also do feel free to get in touch if you are a prospective
visiting researcher, industrial partner, or would like to
collaborate on a project.
Recent grants

EPSRC/Innovate UK RA project on "Computational statistics on
power networks" (2015)
 JAIST/UCL joint PhD studentship on "Statistical
regularisation" (2015)
 Dstl RA project on "Graphbased, informationtheoretic
approaches to minimal sensing" (2014)
 Dstl Impact PhD studentship "Regularity and
information filtering approaches to uncertainty management"
(2014)
 EPSRC/D2U RA project on "Semisupervised learning and
smart sensor network monitoring" (2014)
 ASTAR/UCL joint PhD studentship "Statistical analysis,
semantic modelling, and learning for highly structured data"
(2014)
 ASTAR/UCL joint PhD studentship "Multiresolution
hypergraphs for histopathological classification" (2013)
 Dstl Impact PhD studentship "Inhomogeneous multiresolution
random fields" (2013)
 EPSRC/TSB Technology Inspired Innovation RA project on
"Smart sensor network for powerline monitoring using machine
learning" (2013)
 Dstl fully funded 4year PhD studentship "Statistical
signal processing and machine learning for network traffic
anomaly detection" (2012)
 Dstl/Atlas Elektronik UK RA project on semisupervised
mine countermeasures (2012)
 EPSRC RA project "Multiresolution Markov models for detecting radial patterns of spicules in mammograms" (2012)
 Dstl RA project "Image modelling for assured detection and classification of objects across different operating environments" with Nick Kingsbury, University of Cambridge (2015)
 Renishaw/Impact/CoMPLEX studentship "Raman spectralspatial imaging for cancer diagnostics" with Prof. Geraint Thomas, UCL (2015)

EPSRC RA project "Development of locally invariant signal
processing to discriminate between key manmade and natural
features" (2009)
 MoD Counter Terrorism Centre project "Anomaly detection in video imagery" (2009)
Principal Investigator
CoInvestigator
Researcher CoInvestigator
Publications
[1]  J. D. B. Nelson, C. Nafornita, and A. Isar. Semilocal scaling exponent estimation with boxpenalty constraints and totalvariation regularization. IEEE Transactions on Image Processing, 25(7), 2016. [ bib  http ] 
[2]  P. Chen, J. D. B. Nelson, and J.Y. Tourneret. Towards a sparse Bayesian Markov random field approach to hyperspectral unmixing and classification. IEEE Transactions on Image Processing (accepted), 2016. [ bib ] 
[3]  A. J. Gibberd and J. D. B. Nelson. Estimating dynamic graphical models from multivariate timeseries data: Recent methods and results. Lecture Notes on Artificial Intelligence (in press), 2016. [ bib ] 
[4]  A. J. Gibberd and J. D. B. Nelson. Regularized estimation of piecewise constant Gaussian graphical models: The groupfused graphical Lasso. ArXiv eprint arXiv:1512.06171, 2016. [ bib  http ] 
[5]  R. Gaifulina, A. Maher, C. Kendall, J. D. B. Nelson, M. RodriguezJusto, K. Lau, and G. Thomas. Labelfree Raman spectroscopic imaging to extract morphological and chemical information from a formalinfixed, paraffinembedded rat colon tissue section. International Journal of Experimental Pathology, 2016. [ bib ] 
[6]  J. D. B. Nelson and A. J. Gibberd. Introducing the locally stationary dualtree complex wavelet model. IEEE International Conference on Image Processing, 2016. [ bib ] 
[7]  H. AkhondiAsl and J. D. B. Nelson. Mestimate robust PCA for seismic noise attenuation. IEEE International Conference on Image Processing, 2016. [ bib ] 
[8]  J.B. Regli and J. D. B. Nelson. Scattering convolutional hidden Markov trees. IEEE International Conference on Image Processing, 2016. [ bib ] 
[9]  H. AkhondiAsl and J. D. B. Nelson. Multiscale sparse coding with anomaly detection and classification. IEEE Statistical Signal Processing Workshop, 2016. [ bib ] 
[10]  A. J. Gibberd and J. D. B. Nelson. Regularised estimation of 2dlocally stationary wavelet processes. IEEE Statistical Signal Processing Workshop, 2016. [ bib ] 
[11]  J. D. B. Nelson. Enhanced Bwavelets via mixed, composite packets. IEEE Transactions on Signal Processing, 63(12):31913203, 2015. [ bib  http  code ] 
[12]  D. R. Tomassi, D. H. Milone, and J. D. B. Nelson. Wavelet shrinkage using adaptive structured sparsity constraints Bwavelets via mixed, composite packets. Signal Processing, 106:7387, 2015. [ bib  http ] 
[13]  A. J. Gibberd and J. D. B. Nelson. Estimating multiresolution dependency graphs within the stationary wavelet framework. IEEE Global Conference on Signal and Information Processing, 2015. [ bib ] 
[14]  J. D. B. Nelson, C. Nafornita, and I. Isar. Generalised MLasso for robust, spatially regularised Hurst estimation. IEEE Global Conference on Signal and Information Processing, 2015. [ bib ] 
[15]  N. Tsipinakis and J. D. B. Nelson. Sparse temporal difference learning via alternating direction method of multipliers. IEEE International Conference on Machine Learning and Applications, 2015. [ bib ] 
[16]  C. Guo and J. D. B. Nelson. Compressive imaging with complex wavelet transform and turbo AMP reconstruction. Proceedings of the European Signal Processing Conference, 2015. [ bib ] 
[17]  J.B. Regli and J. D. B. Nelson. Piecewise parameterised Markov random fields for semilocal Hurst estimation. Proceedings of the European Signal Processing Conference, 2015. [ bib ] 
[18]  A. J. Gibberd and J. D. B. Nelson. Sparsity in the multivariate wavelet framework: A comparative study using epileptic electroencephalography data. IET Intelligent Signal Processing Conference, 2015. [ bib ] 
[19]  C. Guo and J. D. B. Nelson. Direction of arrival estimation via approximate message passing. IET Intelligent Signal Processing Conference, 2015. [ bib ] 
[20]  A. J. Gibberd and J. D. B. Nelson. Estimating dynamic graphical models from multivariate timeseries data. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases: Workshop on Advanced Analytics and Learning on Temporal Data (ECML/PKDD:AALTD), 2015. [ bib ] 
[21]  V. Krylov and J. D. B. Nelson. Line extraction via phase congruency with a novel adaptive scale selection for poisson noisy medical images. Proceedings of the V ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, 2015. [ bib ] 
[22]  J. D. B. Nelson. On the equivalence between a minimal codomain cardinality Riesz basis, a system of HadamardSylvester operators, and a class of sparse, binary optimisation problems. IEEE Transactions on Signal Processing, 62(20):52705281, 2014. [ bib  http ] 
[23]  V. Krylov and J. D. B. Nelson. Stochastic extraction of elongated curvilinear structures with applications. IEEE Transactions on Image Processing, 23(12):53605373, 2014. [ bib  http  code ] 
[24]  A. J. Gibberd and J. D. B. Nelson. High dimensional changepoint detection with a dynamic graphical lasso. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014. [ bib ] 
[25]  C. Nafornita, A. Isar, and J. D. B. Nelson. Regularised, semilocal Hurst estimation via generalised lasso and dualtree complex wavelets. IEEE International Conference on Image Processing, pages 26892693, 2014. [ bib ] 
[26]  B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson. Improving hyperspectral band selection by constructing an estimated reference map. Journal of Applied Remote Sensing, 8(1), 2014. [ bib ] 
[27]  J. D. B. Nelson and V. Krylov. Textural lacunarity for semisupervised learning in sonar imagery. IET Radar, Sonar and Navigation, 8(6):616621, 2014. [ bib ] 
[28]  A. J. Gibberd and J. D. B. Nelson. Groupfused graphical lasso for changepoint estimation in multivariate timeseries. IMA International Conference on Mathematics in Signal Processing, 2014. [ bib ] 
[29]  D. KnightGaynor, S. Lu, and J. D. B. Nelson. A sparse regionaware kernel approach to superpixel matching for histopathological data. IMA International Conference on Mathematics in Signal Processing, 2014. [ bib ] 
[30]  A. Kalaitzis and J. D. B. Nelson. Online joint classification and anomaly detection via sparse coding. IEEE Machine Learning for Signal Processing, 2014. [ bib ] 
[31]  J. D. B. Nelson. Fused Lasso and rotation invariant autoregressive models for texture classification. Pattern Recognition Letters, 34(16):21662172, 2013. [ bib  code ] 
[32]  V. Krylov and J. D. B. Nelson. Fast road network extraction from remotely sensed images. Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, (8192):227237, 2013. [ bib ] 
[33]  V. Krylov, S. Taylor, and J. D. B. Nelson. Stochastic extraction of elongated curvilinear structures in mammographic images. International Conference on Image Analysis and Recognition, 2013. [ bib  code ] 
[34]  A. J. Gibberd and J. D. B. Nelson. Sparsity for change point detection in network traffic. Data Analysis for CyberSecurity, 2013. [ bib ] 
[35]  J. D. B. Nelson and N. G. Kingsbury. Fractal dimension, wavelet shrinkage, and anomaly detection for mine hunting. IET Signal Processing Journal, 2012. [ bib  .pdf ] 
[36]  J. D. B. Nelson and N. G. Kingsbury. Multiresolution Markov random field wavelet shrinkage for ripple suppression in sonar imagery. IMA International Conference on Mathematics in Signal Processing, 2012. [ bib  slides  .pdf ] 
[37]  S. Julier, R. De Nardi, and J. D. B. Nelson. Multirate estimation of coloured noise models in graphbased estimation algorithms. International Conference on Information Fusion, 2012. [ bib ] 
[38]  J. D. B. Nelson and N. G. Kingsbury. Enhanced shift and scale tolerance for dualtree complex wavelet rotation invariant matching. IEEE Transactions on Image Processing, 20(3), 2011. [ bib  .pdf ] 
[39]  J. D. B. Nelson and N. G. Kingsbury. Fractal dimension based sand ripple suppression for mine hunting with sidescan sonar. International Conference on Synthetic Aperture Sonar and Synthetic Aperture Radar, 2010. [ bib  .pdf ] 
[40]  J. D. B. Nelson and N. G. Kingsbury. Dualtree wavelets for locally varying and anisotropic fractal dimension estimation. IEEE International Conference on Image Processing, 2010. [ bib  .pdf ] 
[41]  J. D. B. Nelson, S. R. Gunn, R. I. Damper, and B. Guo. A signal theory approach to support vector classification: the sinc kernel. Neural Networks, 22(1):4957, 2009. [ bib  .pdf ] 
[42]  S. K. Pang, J. D. B. Nelson, S. J. Godsill, and N. G. Kingsbury. Video tracking using dualtree wavelet polar matching and raoblackwellised particle filter. Journal on Advances in Signal Processing, 2009. [ bib  .pdf ] 
[43]  J. D. B. Nelson, S. R. Gunn, R. I. Damper, and B. Guo. Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels. Neurocomputing, 72(13):1522, 2009. [ bib  .pdf ] 
[44]  B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson. A fast separabilitybased feature selection method for highdimensional remotelysensed image classification. Pattern Recognition, 41(8), 2008. [ bib  .pdf ] 
[45]  B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson. Customizing kernel functions for SVMbased hyperspectral image classification. IEEE Transactions on Image Processing, 17(4):622629, 2008. [ bib  .pdf ] 
[46]  J. D. B. Nelson, S. K. Pang, S. J. Godsill, and N. G. Kingsbury. Tracking ground based targets in aerial video with dualtree complex wavelet polar matching and particle filtering. International Conference on Information Fusion, 2008. [ bib  .pdf ] 
[47]  S. K. Pang, J. D. B. Nelson, S. J. Godsill, and N. G. Kingsbury. Video tracking using dualtree wavelet polar matching and particle filtering. The Institution of Engineering and Technology Seminar on Target Tracking and Data Fusion, 2009. [ bib  .pdf ] 
[48]  A. Mahmood, P. M. Tudor, W. Oxford, R. Hansford, J. D. B. Nelson, N. G. Kingsbury, A. Katartzis, M. Petrou, N. Mitianoudis, T. Stathaki, A. Achim, D. Bull, N. Canagarajah, S. Nikolov, A. Loza, and N. Cvejic. Applied multidimensional fusion. The Computer Journal, 50(6), 2007. [ bib  .pdf ] 
[49]  B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson. Band selection for hyperspectral image classification using mutual information. IEEE Geoscience and Remote Sensing Letters, 2006. [ bib  .pdf ] 
[50]  J. D. B. Nelson, S. R. Gunn, R. I. Damper, and B. Guo. Signal theory for SVM kernel parameter estimation. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, 2006. [ bib  .pdf ] 
[51]  J. D. B. Nelson. A wavelet filter enhancement scheme with a fast integral Bwavelet transform and pyramidal multiB wavelet algorithm. Applied and Computational Harmonic Analysis, 18(3):234251, 2005. [ bib  .pdf ] 
[52]  B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson. Adaptive band selection for hyperspectral image fusion using mutual information. International Conference on Information Fusion, 2005. [ bib  .pdf ] 
[53]  B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson. Hyperspectral image fusion using spectrally weighted kernels. International Conference on Information Fusion, 2005. [ bib  .pdf ] 
[54]  J. D. B. Nelson. Applications of enhanced vision to the automotive industry. SMi Conference on Enhanced Vision, 2004. [ bib ] 
[55]  J. D. B. Nelson. On the coefficient quantization of the Fourier basis. IEEE Transactions on Signal Processing, 51(7):18381846, 2003. [ bib  .pdf ] 
[56]  J. D. B. Nelson. A multichannel, multisampling rate theorem. Sampling Theory in Signal and Image Processing: An International Journal, 2(1):8396, 2003. [ bib  .pdf ] 
[57]  J. D. B. Nelson. The construction of some Riesz basis families and their application to coefficient quantization, sampling theory, and wavelet analysis. PhD thesis, Anglia Polytechnic University, 2001. [ bib  .pdf ] 
Teaching
 2010  2011, 2013  present: Lecturer of
"LTCC course on Stochastic Processes" (London Taught Course
Centre: an EPSRC supported graduate training programme for PhD
students in the mathematical sciences)
 2011  present: MSc "Statistical Computing"
 2011  present: Tutor for "Introduction to Applied Probability"
 2010  2011: Tutor for MSc "Skills Development course in
graphics"
 2011: Lecturer for the MSc Foundation course on "Poisson
Processes & Further Applied Probability"
 2010  2011: Lecturer of "Forecasting"
 2010  2011: Tutor for "Probability and Inference"
 2010: Tutor for "Introduction to probability and
statistics"
Admin
 Natural
Sciences Mathematics and Statistics stream representative
 Centre for Security Technology steering committee member
 Statistical
Science Departmental Teaching Committee member
Code

Mixed, composite Bwavelet functions
cf.: Nelson, J. D. B. (2015) "Enhanced Bwavelets via mixed,
composite packets" IEEE Transactions on Signal
Processing,

Construction of minimal codomain cardinality Riesz bases
cf.: Nelson, J. D. B. (2014) "On the equivalence between a
minimal codomain cardinality Riesz basis, a system of
HadamardSylvester operators, and a class of sparse, binary
optimisation problems" IEEE Transactions on Signal Processing

MCMC extraction of curvilinear structures in mammogram data
cf.: Krylov, V., Taylor, S., and Nelson, J. D. B. (2013)
"Stochastic extraction of elongated curvilinear structures in
mammographic images". International
Conference on Image Analysis and Recognition