Professor Nick Heard

Chair in Statistics

NAH
Research Interests:
  • Meta-analysis
  • Statistical methods for cyber-security
  • Computational Bayesian inference
  • Modelling large dynamic networks
  • Statistical approaches to clustering and classification
Contact Details:
  • 543 Huxley Building
  • Department of Mathematics
  • South Kensington Campus
  • Imperial College London, SW7 2AZ
    +44 (0)20 7594 1490
Some publications:

Meta-Analysis

  • Heard, N. A. and Rubin-Delanchy, P. T. G. (2018) Choosing Between Methods of Combining p-values. Biometrika, 105, 1, 239-246. (Online pdf)
  • Rubin-Delanchy, P. T. G., Heard, N. A. and Lawson, D. J (2018) Meta-analysis of mid-p-values: some new results based on the convex order. Journal of the American Statistical Association, to appear. (Online pdf)

Dynamic Networks and Cyber-Security

  • Sanna Passino, F. and Heard, N. A. (2019) Modelling dynamic network evolution as a Pitman-Yor process. Foundations of Data Science, to appear.
  • Metelli, S. and Heard, N. A. (2019) On Bayesian New Edge Prediction and Anomaly Detection in Computer Networks. Annals of Applied Statistics, to appear.
  • Price-Williams, M. and Heard, N. A. (2019) Nonparametric Self-exciting Models for Computer Network Traffic. Statistics and Computing, to appear. (Online pdf)
  • Price-Williams, M., Heard, N. A. and Rubin-Delanchy, P. (2019) Detecting weak dependence in computer network traffic patterns using higher criticism. Journal of the Royal Statistical Society, Series C, 68, 3, 641-655. (Online pdf)
  • Price-Williams, M., Turcotte, M. J. M. and Heard, N. A. (2018) Time of Day Anomaly Detection. In proceedings of IEEE European Intelligence and Security Informatics Conference (EISIC2018). (Online pdf)
  • Bolton, A. and Heard, N. A. (2018) Malware Family Discovery Using Reversible Jump MCMC Sampling of Regimes. Journal of the American Statistical Association, 113, 524, 1490-1502. (Online pdf)
  • Price-Williams, M., Heard, N. A. and Turcotte, M. J. M. (2017) Detecting periodic subsequences in cyber security data. In proceedings of IEEE European Intelligence and Security Informatics Conference (EISIC2017). (Online pdf)
  • Heard, N. A., Palla, K. and Skoularidou, M. (2016) Topic modelling of authentication events in an enterprise computer network. In proceedings of IEEE Intelligence and Security Informatics Conference (ISI2016), Cybersecurity and Big Data. (Online pdf)
  • Heard, N. A. and Rubin-Delanchy, P. T. G. (2016) Network-wide anomaly detection via the Dirichlet process. In proceedings of IEEE Big Data Analytics for Cybersecurity Computing (BDAC2016). (Online pdf)
  • Metelli, S and Heard, N. A. (2016) Model-Based Clustering and New Edge Modelling in Large Computer Networks. In proceedings of IEEE Intelligence and Security Informatics Conference (ISI2016), Cybersecurity and Big Data.(Online pdf)
  • Turcotte, M. J. M., Moore, J., Heard, N. A and McPhall, A. (2016) Poisson Factorization for Peer-Based Anomaly Detection. In proceedings of IEEE Intelligence and Security Informatics Conference (ISI2016), Cybersecurity and Big Data. (Online pdf)
  • Rubin-Delanchy, P. T. G., Adams N. M., and Heard, N. A. (2016) Disassortativity of computer networks. In proceedings of IEEE Big Data Analytics for Cybersecurity Computing (BDAC2016). (Online pdf)
  • Turcotte, M. J. M. and Heard, N. A. and Neil, J. (2014) Detecting Localised Anomalous Behaviour in a Computer Network. In Advances in Intelligent Data Analysis XIII, 321–332. (Online pdf)
  • Rubin-Delanchy, P. T. G. and Heard, N. A. (2014) A test for dependence between two point processes on the real line. Arxiv preprint. (Online pdf) Supplementary material.
  • Heard, N. A. and Rubin-Delanchy, P. T. G. and Lawson, D. J. (2014) Filtering automated polling traffic in computer network flow data. In proceedings of IEEE Joint Intelligence and Security Informatics Conference 2014. (Online pdf)
  • Metelli, S. and Heard, N. A. (2014) Modelling new edge formation in a computer network through Bayesian Variable Selection. In proceedings of IEEE Joint Intelligence and Security Informatics Conference 2014. (Online pdf)
  • Bolton, A. and Heard, N. A. (2014) Application of a linear time method for change point detection to the classification of software. In proceedings of IEEE Joint Intelligence and Security Informatics Conference 2014. (Online pdf)
  • Rubin-Delanchy, P. T. G., Lawson, D. J., Turcotte M. J. T., Adams, N. M. and Heard, N. A. (2014) Three statistical approaches to sessionizing network flow data. In proceedings of IEEE Joint Intelligence and Security Informatics Conference 2014. (Online pdf)
  • Lawson, D. J., Rubin-Delanchy, P. T. G. and Adams, N. M. and Heard, N. A. (2014) Statistical frameworks for detecting tunnelling in cyber defence using big data. In proceedings of IEEE Joint Intelligence and Security Informatics Conference 2014. (Online pdf)
  • Heard, N. A. and Turcotte, M. J. M. (2013). Monitoring a device in a communication network. In Data Analysis for Cyber-Security, eds. Adams, N. M. and Heard, N. A., Imperial College Press. (Online abstract)
  • Heard, N. A., Weston, D. J., Platanioti, K. and Hand, D. J. (2010) Bayesian Anomaly Detection Methods for Social Networks. Annals of Applied Statistics, 4, 2, 645-662. (Online pdf)

Bayesian Computation (see also Clustering)

  • Heard, N. A. and Turcotte, M. J. M. (2017) Adaptive sequential Monte Carlo for multiple changepoint analysis. The Journal of Computational and Graphical Statistics, 26, 2, 414-423. (Online pdf)
  • Heard, N. A. and Turcotte, M. J. M. (2016) Convergence of Monte Carlo distribution estimates from rival samplers. Statistics and Computing, 26, 6, 1147-1161. (Online pdf)
  • Holmes, C. C. and Heard, N. A. (2003) Generalized monotonic regression using random change points. Statistics in Medicine, 22, 4, 623-638. (Online abstract)

Clustering

  • Rubin-Delanchy, P., Burn, G. L., Griffie, J., Williamson, D. J., Heard, N. A., Cope, A. C. and Owen, D. M. (2015) Bayesian cluster identification in single-molecule localization microscopy data. Nature Methods, 12, 1072-1076. (Online pdf)
  • Fowler, A., Menon, A. and Heard, N. A. (2013). Dynamic Bayesian Clustering. Journal of Bioinformatics and Computational Biology, 11, 5, 1342001:1–15. (Online pdf)
  • Fowler, A. and Heard, N. A. (2013). Dynamic Bayesian Clustering of Gene Expression Data. In Proceedings of the 5th International Conference on Bioinformatics and Computational Biology.
  • Fowler, A. and Heard, N. A. (2012). On Two-way Bayesian Agglomerative Clustering of Gene Expression Data. Statistical Analysis and Data Mining, 5, 5, 463–476. (Online abstract)
  • Heard, N. A. (2011). Iterative Reclassification in Agglomerative Clustering. The Journal of Computational and Graphical Statistics, 20, 4, 920-936. (Online pdf)
  • Heard, N. A., Holmes, C. C. and Stephens, D. A. (2006) A quantitative study of gene regulation involved in the immune response of Anopheline mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves. Journal of the American Statistical Association, 101, 473, 18-29. (Online abstract)
  • Heard, N. A., Holmes, C. C., Stephens, D. A., Hand, D. J. and Dimopoulos, G. (2005) Bayesian Co-clustering of Anopheles Gene Expression Time Series: A Study of Immune Defense Response To Multiple Experimental Challenges. Proceedings of the National Academy of Science USA, 102, 47, 16939-16944(Online abstract)
  • Hand, D. J. and Heard, N. A. (2005) Finding groups in gene expression data. Journal of Biomedicine and Biotechnology, 2, 215-225. (Online pdf)
Software
C++ code