Dr James Nelson
+44 (0)20 7679 1875
|Themes||Stochastic Modelling and Time Series|
James Nelson joined the Department of Statistical Science as a Lecturer in 2010 and became Senior Lecturer in 2013. After a
PhD in applied harmonic analysis from the Mathematics Department at Anglia
Polytechnic University (1998-2001), he held post-doc positions in: the Applied
Mathematics and Computing Group at the University of Cranfield (2001-2004); the
Information: Signals, Images, and Systems Research Group at the University of
Southampton (2004-2006); and the Signal Processing and Communications Laboratory at
the University of Cambridge (2006-2010).
Multiresolution analysis; random fields; spatial models; statistical machine learning; statistical signal and image processing. Examples: Markov random fields; wavelet/Riesz basis and packet construction and pursuit/optimisation; sparsity and other regularisation with applications to detection and classification; weak self-similar random fields; support vector machine kernel construction and hyper-parameter estimation; generalised cardinal series and sampling theory; robust, regularised Hurst parameter estimation for texture and volatility models.
- Chunli Guo (sparse detection methods)
- Alfredo Kalaitzis (multivariate time series and sparse methods on networks)
Vladimir Krylov [now at University of Genoa]
- stochastic geometry for image analysis
- semi-supervised anomaly detection
- Diego Tomassi [funded visit from National University of the Littoral] (wavelet shrinkage using adaptive structured sparsity constraints)
- Alex Gibberd (sparsity on graphs and statistical machine learning on networks)
- Daniel Knight-Gaynor (statistical learning and graphlets for histopathological data)
- Maria Toomik (multiresolution analysis and regularisation approaches to highly structured data)
- Jean-Baptiste Regli (computational statistics and spatial models)
- Nikolaos Tsipinakis (regularity and information filtering approaches to uncertainty management)
- Christian Niedworok (automated cell detection algorithms in 2-photon microscopy of whole brain images; 1st supervisor Prof. Troy Margrie, MRC National Institute for Medical Research)
- Please feel free to get in touch if you are a prospective research student, visiting researcher, industrial partner, or would like to collaborate on a project.
- Nelson, J. D. B. (2015) "Enhanced B-wavelets via mixed, composite packets" IEEE Transactions on Signal Processing, 63(12):3191-3203
- Tomassi, D. R., Milone, D. H., and Nelson, J. D. B. (2015) "Wavelet shrinkage using adaptive structured sparsity constraints". Signal Processing, 106:73-87
- Nelson, J. D. B. (2014) "On the equivalence between a minimal codomain cardinality Riesz basis, a system of Hadamard-Sylvester operators, and a class of sparse, binary optimisation problems". IEEE Transactions on Signal Processing, 62(20):5270-5281
Gibberd, A. J. and Nelson, J. D. B. (2014) "High dimensional changepoint detection with a dynamic graphical lasso". IEEE International Conference on Acoustics, Speech, and Signal Processing
- Nelson, J. D. B. (2013) "Fused Lasso and rotation invariant autoregressive models for texture classification". Pattern Recognition Letters 34(16):2166-2172
- Nelson, J. D. B. and Kingsbury, N. G. (2012) "Fractal dimension, wavelet shrinkage, and anomaly detection for mine hunting". IET Signal Processing Journal.
- Nelson, J. D. B. and Kingsbury, N. G. (2011) "Enhanced shift and scale tolerance for rotation invariant polar matching with dual-tree wavelets". IEEE Transactions on Image Processing, 20(3): 814-821.
- Nelson, J. D. B. and Kingsbury, N. G. (2010) "Fractal dimension based sand ripple suppression for mine hunting with sidescan sonar". Institute of Acoustics International Conference on Synthetic Aperture Sonar and Synthetic Aperture Radar.
- Nelson, J. D. B., Damper, R. I., Gunn, S. R. and Guo, B. (2009) "A signal theory approach to support vector classification: the Sinc kernel". Neural Networks, 22 (1): 49-57.
Guo, B., Gunn, S. R., Damper, R. I. and Nelson, J. D. B. (2008) "A fast separability-based feature selection method for highdimensional remotely-sensed image classification". Pattern Recognition 41 (8): 1670-1679
More publications, with preprints, code, and opportunities, etc can be found here
See also my Google Scholar and Google Sites pages and the Centre for Computational Statistics and Machine Learning site.