Visiting Professor of Statistics
Department of Mathematics
Imperial College London
g.montana [at] imperial. ac. uk

Professor of Biostatistics and Bioinformatics
Department of Biomedical Engineering
King's College London
giovanni.montana [at] kings. ac. uk


Recent events

Research group

  • PhD students:
    • Michelle Krishnan (KCL)
    • Alexandre de Brebisson (ICL)
    • Petros-Pavlos Ypsilantis (KCL)
    • Zhana Kuncheva (ICL)
    • Dimosthenis Tsagkrasoulis (ICL)
    • Ricardo Monti (ICL)
    • Zi Wang (ICL)
    • Ryan Ruan ICL)
  • Research associates:
    • Ai Chung (KCL)
    • Chris Minas (IC)
    • Rene Gausoin (IC)
  • Alumni:
    • Peter Nash - Research Associate
    • Anand Pandit - PhD Student
    • Chris Minas - PhD Student
    • Matt Silver - PhD Student
    • Orlando Dohering - MPhil Student
    • Yue Wang - Visiting PhD Student (NUS)
    • Maria Vounou - PhD Student, EPSRC and GSK Clinical Imaging Center
    • Maurice Berk - PhD Student, Wellcome Trust
    • Brian McWilliams - PhD Student, EPSRC
    • Alberto Cozzini - PhD Student, AHL / Man Group
    • Theo Tsagaris - PhD Student, Bluecrest Capital
    • Mansour Sharabiani - Research Associate, NIHR
    • Becky Inkster - Research Associate, VIP award
    • Eva Jasounova - Academic Visitor, Leonardo da Vinci award
    • Francesco Parrella - Academic Visitor, Leonardo da Vinci award

    Preprints and selected publications

    • Janousova E. et al (2014). Mapping of cognitive processes on subcortical volumes, cortical thickness and area patterns shows no significant associations. Preprint
    • Wang Z. and Montana G. (2014) The graph-guided group lasso for genome-wide association studies. In "Regularization, Optimization, Kernels, and Support Vector Machines", Johan A.K. Suykens et al (Editors). In press
    • Ruan D., Young A., and Montana G. (2014). Differential analysis of biological networks. Preprint
    • Wang Z., Curry E., and Montana G. (2014). Network-guided regression for detecting associations between DNA methylation and gene expression. Preprint
    • Pio Monti R., Hellyer P., Sharp D., Leech R., Anagnostopoulos C., Montana G. (2014) Estimating dynamic brain connectivity networks from functional MRI time series. Preprint
    • Minas, C. and Montana, G. (2014) Hypothesis testing in distance-based regression. Preprint.
    • Gaudoin R., Montana G., Jones S., Aylin P. and Bottle A. (2014) Classifier calibration using splined empirical probabilities in clinical risk prediction. Health Care Management Science, to appear.
    • Cozzini A, Jasra A., Montana G. and Persing A. (2014) A Bayesian mixture of lasso regressions with t-errors. [arXiv] Computational Statistics and Data Analysis. In press
    • Minas C. and Montana G. (2014) Distance-based analysis of variance: approximate inference [arXiv] Statistical Analysis and Data Mining. In press
    • McWilliams B. and Montana G. (2014) Subspace clustering of high-dimensional data: a predictive approach. [arXiv] Data Mining and Knowledge Discovery. Volume 28, Issue 3, pp 736-772
    • de Marvao A., Dawes T., Shi W., Minas C., Keenan N., Diamond T., Durighel G., Montana G. , Rueckert D., Cook S. and O'Regan D. (2014) Automated cardiac phenotyping using 3D high spatial resolution MR imaging. Journal of Cardiovascular MR, 16:16
    • Kiskinis E., Chatzeli L., Curry E., Kaforou M., Frontini A., Cinti S., Montana G., Parker M. and Christian M. (2014) RIP140 represses the BRITE adipocyte program including a futile cycle of TAG breakdown and synthesis. Molecular Endocrinology, Vol 28, Issue 3.
    • Rosell M., Kaforou M., Frontini A., Okolo A., Nikolopolou E., Millership S., Fenech ME, MacIntyre D, Turner JO, Blackburn E., Gullick W., Cinti S., Montana G., Parker MG, Christian M. (2014) Brown and white adipose tissues. Intrinsic differences in gene expression and response to cold exposure. Am J Physiol Endocrinol Metab.
    • Sim, A., Tsagkrasoulis, D. and Montana, G. (2013) Random forests on distance matrices for imaging genetics studies. Statistical Applications in Genetics and Molecular Biology. Volume 12, Issue 6, Pages 757-786
    • Silver M., Chen P., Ruoying L., Cheng CY, Wong TY, Tai E., Teo YY, and Montana G. (2013) Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts. [arXiv] PloS Genetics
    • Herberg J., Kaforou M., Gormley S., Sumner E.D., Patel S., Jones KDJ, Paulus S., Fink C., Martinon-Torres F., Montana G., Wright VJ, Levin M. (2013) Transcriptomic profiling in childhood H1N1/09 influenza reveals reduced expression of protein synthesis genes. The Journal of Infectious Disease 15;208(10):1664-8.
    • Minas C., Curry E., and Montana G. (2013) A distance-based test of association between paired heterogeneous genomic data. [arXiv] Bioinformatics.
    • Pandit AS, Robinson E., Aljabar P., Ball G., Gousias IS, Wang Z., Hajnal JV, Rueckert D., Counsell SJ, Montana G., Edwards AD (2013) Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth. Cerebral Cortex.
    • Wang Y., Goh W, Wong L. and Montana G. (2013) Random forests on Hadoop for genome-wide studies of multivariate neuroimaging phenotypes. BMC Bioinformatics.
    • Cozzini A, Jasra A. and Montana G. (2013) Model-based clustering with gene ranking using penalised mixtures of heavy-tailed distributions. Journal of Bioinformatics and Computational Biology. [arXiv]
    • Gendrel AV, Apedaile A, Coker H, Termanis A, Zvetkova I, Godwin J, Tang YA, Huntley D, Montana G., Taylor S, Giannoulatou E, Heard E, Stancheva I, Brockdorff N (2012) Smchd1-dependent and -independent pathways determine developmental dynamics of CpG island methylation on the inactive X chromosome. Developmental Cell, to appear.
    • Silver M., Janousova E., Hue X., Thompson P. and Montana G. (2012) Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. Neuroimage, 63(3), Pages 1681-1694 [arXiv]
    • McWilliams B. and Montana G. (2012) Multi-view predictive partitioning in high dimensions. Statistical Analysis and Data Mining,5(4): 304-321 [arXiv]
    • Silver M. and Montana G. (2012) Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps. Statistical Applications in Genetics and Molecular Biology, vol. 11, issue 1, article 7 [arXiv]
    • Janousova E., Vounou M, Wolz R., Gray K. R., Rueckert D. and Montana G. (2012) Biomarker discovery for sparse classification of brain images in Alzheimer's disease. Annals of the British Machine Vision Association (2), 1-11
    • Berk M. and Montana G. (2012) A skew-t-normal multi-level reduced-rank functional PCA model with applications to replicated `omics time series data sets. In Proceedings of the IDA Symposium 2012 [arXiv]
    • Inkster B, Strijbis E, Vounou M, Bendtfeld K, Radue EW, Matthews PM, Barkhof F, Polman CH, Montana G*, Geurts JJG*. (2012) Histone deacetylase gene variants predict brain volume changes in multiple sclerosis. Neurobiology of Aging.
    • Strijbis E, Inkster B, Vounou M, Kappos L, Radue EW, Matthews PM, Uitdehaag B, Barkhof G, Polman CH, Montana G*, Geurts JJG* (2012) Glutamate gene polymorphisms predict brain volume changes in multiple sclerosis. Multiple Sclerosis Journal.
    • Vounou M, Janousova E., Wolz R., Stein J. Thompson P., Rueckert D. and Montana G. (2011) Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease. NeuroImage, 60(1):700-716
    • McWilliams B. and Montana G. (2011) Predictive Subspace Clustering. In Procedings of the Tenth IEEE International Conference on Machine Learning and Applications, Vol. 1, pp.247-252.
    • Minas C, Waddell S. and Montana G. (2011) Distance-based differential analysis of gene curves. Bioinformatics, 27 (22): 3135-3141.
    • Pathan N., Burmester M., Adamovic T., Berk M., Ng K., Betts H., Macrae M., Waddell S., Paul-Clark M., Levin M., Montana G., Mitchell J. (2011) Intestinal injury and endotoxemia in children undergoing surgery for congenital heart disease. American Journal of Respiratory and Critical Care Medicine, Vol 184, Pages:1261-1269
    • Silver M. and Montana G. (2011) Pathway selection for GWAS using the group lasso with overlaps. In IEEE International Proceedings of Chemical, Biological & Environmental Engineering, Singapore.
    • Janousova E., Vounou M., Wolz R. Ruecket D., and Montana G. (2011) Fast brain-wide search of highly discriminative regions in medical images: an application to Alzheimer's disease. In Proceedings of MIUA (Medical Image Understanding and Analysis), London, UK.
    • Berk M., Ebbels T, and Montana G. (2011) A statistical framework for metabolic profiling using longitudinal data. Bioinformatics, 27(14), pp. 1979-1985.
    • Berk M., Hemingway C., Levin M. and Montana G. (2011). Longitudinal analysis of gene expression profiles using functional mixed-effects models. In 'Studies in Theoretical and Applied Statistics' pp 57-67. Springer. [arXiv]
    • Triantafyllopoulos, K. and Montana, G. (2011) Dynamic modeling of mean-reverting spreads for statistical arbitrage. Computational Management Science. Vol 8, Issue 1, pp. 23-49 [arXiv]
    • Pathan N, Burmester M, Adamovic T, Berk M, Montana G, Levin M, Mitchell J (2010) Gut barrier dysfunction and activation of endoxin signal pathways in children undergoing for congenital heart disease. In proceedings of the 40th Critical Care Congress. Lippincot Williams & Wilkins.
    • Spanu et al. (2010) Genome expansion and gene loss in powdery mildew fungi reveal functional tradeoffs in extreme parasitism. Science 10, Dec 2010: Vol. 330 no. 6010 pp. 1543-1546
    • McWilliams B. and Montana G. (2010) A PRESS statistic for two-block partial least squares regression. In Proceedings of the 10th Conference on Computational Intelligence UK, Colchester [arXiv]
    • Vounou M. Nichols T., and Montana G. (2010) Detecting genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach. NeuroImage, 5;53(3), pp. 1147-59.
    • Silver M., Montana G., and Nichols T. (2010). False positives in neuroimaging genetics using voxel based morphometry data. NeuroImage, 15;54(2), pp. 992-1000
    • Tang Y. A., Huntley, D., Montana G., Cerase A., Nesteroa, T. B. and Brockdorff N. (2010) Efficiency of Xist-mediated silencing on autosomes is linked to chromosomal domain organisation. Epigenetics and Chromatin. 7;3(1):10.
    • Montana G., Berk M. and Ebbels T. (2010) Modelling short time series in metabolomics: a functional data analysis approach. In 'Software Tools and Algorithms for Biological Systems', Advances in Experimental Medicine and Biology, 2011, Volume 696, Part 4, 307-315, Springer.
    • McWilliams B. and Montana G. (2010) Sparse partial least squares for on-line variable selection in multivariate data streams. Statistical Analysis and Data Mining. 3: 170-193. [arXiv]
    • McWilliams B. and Montana G. (2009) Dynamic asset allocation for bivariate enhanced index tracking using sparse partial least squares. International Workshop on Advances in Machine Learning for Computational Finance, 20-21 July, London. [Video]
    • Berk M. and Montana G. (2009). Functional modelling of microarray time series with covariate curves. Statistica, 2-3, pp. 153-177 [arXiv]
    • Montana G., Triantafyllopoulos K. and Tsagaris T. (2009) Flexible least squares for temporal data mining and statistical arbitrage. Expert Systems with Applications 36(2), pp. 2819-2830. [arXiv]
    • Montana G. and Parrella F. (2009) Data mining for algorithmic asset management. In 'Data Mining for Business Applications',Springer US.
    • Montana G. and Parrella F. (2008) Learning to trade with incremental support vector regression experts. Lecture Notes in Artificial Intelligence Vol. 5271, pp. 591-598. Springer-Verlag
    • Montana G., Triantafyllopoulos K. and Tsagaris, T. (2008) Data stream mining for market-neutral algorithmic trading. In Proceedings of ACM Symposium on Applied Computing, pp. 966-970.
    • Triantafyllopoulos K. and Montana G. (2007) Fast estimation of multivariate stochastic volatility. [arXiv]
    • Montana G. and Hoggart C. (2007) Statistical software for gene mapping by admixture linkage disequilibrium, Briefings in Bioinformatics 8, pp. 393-395
    • Adams N.M., Hand D.J., Montana G. and Weston D. (2006). Fraud Detection in consumer credit. Expert Update, 9(1), pp. 21-27. (Special Issue on the 2nd UK KDD Workshop)
    • Montana G. (2006) Statistical methods in genetics. Briefings in Bioinformatics 7(3), pp. 297-308
    • Montana G. (2005) HapSim: A simulation tool for generating haplotype data with pre-specified allele frequencies and LD patterns. Bioinformatics 21(23), pp. 4309-4311
    • Triantafyllopoulos K. and Montana G. (2004) Forecasting the London metal exchange with a dynamic model. In Proceedings of the 16th Symposium in Computational Statistics, pp. 1885-1892
    • Montana G. and Pritchard J. K. (2004) Statistical tests for admixture mapping with case-control and case-only data. American Journal of Human Genetics 75, pp. 771-789
    • Kendall W.S. and Montana G. (2002) Small sets and Markov transition kernels. Stochastic Processes and Their Applications 99(2), pp. 177-19


    • NsRRR: R code for Network-guided sparse Reduced-Rank Regression
    • SINGLE: R package implementing the Smooth Incremental Graphical Lasso Estimation algorithm
    • GRV: R code for the generalised RV test of association between distance matrices (with data)
    • HiPLAR: R packages for High Performance (GPU and multi-core) Linear Algebra in R
    • PaRFR: Java implementation of parallel random forest regression for hadoop
    • PsRRR: Python code for pathways-sparse reduced-rank regression (with data)
    • PSC: Matlab code for the PSC (predictive subspace clustering) algorithm (with data)
    • ISPLS: Matlab code the ISPL (incremental sparse partial least squares) algorithm (with data)
    • MVPP: Matlab code for the MVPP (multi-view predictive partitioning) algorithm
    • PTM: R code for the PTM (penalised finite mixtures of t distributions) model
    • DBF: R code for the DBF (distance-based F) test statistic and artificial data simulation
    • SME: R code for the SME (smoothing splines mixed effects) model for functional data
    • MALDsoft: C code for admixture mapping using hidden Markov models
    • HapSim: R package for realistic haplotype data simulation
    • Online SVR: C++ code for on-line support vector regression
    • DLM: C++ code for fitting dynamic linear models
    • I maintain the CRAN Task View on Statistical Genetics

    Recent teaching (2012-2013)

    Previous positions

    • Research Biostatistician - Statistical Genetics and Biomarkers Group, Bristol-Myers Squibb Company. Pharmaceutical Research Institute. Princeton, USA
    • Research Associate - Department of Human Genetics. University of Chicago. Chicago, USA
    • PhD in Statistics - Department of Statistics. University of Warwick. Coventry, UK

    Other activities

    • Guest editor, Computational Statistics & Data Analysis, special issue on Advances in Data Mining and Robust Statistics, 2013-14
    • Member of the Program Committe
      • IDA (Intelligent Data Analysis) 2011-2014
      • ICPRAM (International Conference on Pattern Recognition Applications) 2012-2014
      • MASAMB (Mathematical and Statistical Aspects of Molecular Biology) 2013
    • Chair, CompBio 2011
    • Chartered Statistician (since 2006) and fellow of the Royal Statistical Society
    • Committee Member of the Business & Industry Section, Royal Statistical Society (2010-)
    • Vice Chair of the Statistical Computing Section, Royal Statistical Society (2010-)
    • Member of the Computing and Research Committees, IC Dept of Mathematics, 2010-2013
    • Visiting Senior Research Fellow, NUS School of Computing, 2011

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