Giovanni Montana @ Imperial College London

	Giovanni Montana Senior Lecturer in Statistics
	Department of Mathematics Statistics Section 180 Queen's Gate London SW7 2AZ email | twitter

Research

• Applied statistics, machine learning, large and high-dimensional data mining
• Applications mostly in statistical genetics, genomics, medical imaging

Teaching 2012-2013

• Machine Learning - MSc in Statistics
• Statistical Learning - MSc in Bioinformatics and Theoretical Systems Biology
• Statistical Pattern Recognition - London Taught Course Centre (PhD students)

Events I am organizing

• Workshop on Genomic Data Integration - 1 March 2013- Imperial College, London
• Industrial statistics event, Big Data theme - 25 March 2013- Newton Institute, Cambdridge
• Workshop on Big Data Mining - 14-15 May 2013- Imperial College, London
• Invited session on Big Data, ERCIM Conference - 13-16 December 2013 - University of London

Research group

• Current:
  • Zhana Kuncheva - PhD Student
  • Dimosthenis Tsagkrasoulis - PhD Student
  • Ricardo Monti - PhD Student (jointly with Christoforos Anagnostopoulos)
  • Zi Wang - PhD Student
  • Ryan Ruan - PhD Student (jointly with Alistair Young)
  • Chris Minas - Research Associate
  • Peter Nash - Research Associate
• Past:
  • Anand Pandit - PhD Student (jointly with Serena Counsell)
  • Chris Minas - PhD Student
  • Matt Silver - PhD Student
  • Orlando Dohering - MPhil Student
  • Yue Wang - Visiting PhD Student (jointly with Limsoon Wong, 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

Recently funded projects

• BHF (2012-14) Effect of rare variants in Endonuclease G and Phospholamban on left ventricular function assessed with 3D cardiac MRI (CoI)
• EPSRC (2012) Development and Application of Statistical Methods for Integrated Analysis of Cancer Datasets (PI)
• EPSRC (2012) Enabling the statistical analysis of massive data sets on Hadoop (PI)
• Amazon (2011-2012) - Amazon Web Services Education Grant
• EPSRC (2011-2013) Enabling high-performance statistical computing in R on hybrid GPU and multicore architectures (PI)
• NIHR (2010-2012) Risk prediction of adverse outcomes from hospital administrative data using machine learning (CoI)
• BRC (2010) Genomic imaging of preterm infants using novel statistical approaches (CoI)
• Wellcome Trust (2009-2012). Advanced MR image analysis to discover susceptibility genes for adverse brain development in preterm infants (PI)
• EPSRC (2008-2010). iHealth-information driven health (CoI)
• EPSRC and GlaxoSmithKline (2007-2010). Novel statistical methods for whole genome association studies with neuroimaging data (PI)
• Wellcome Trust (2007-2010). Statistical Methods for replicated, high dimensional biological time series (PI)

Preprints and selected publications

• 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. (2013) Brown and White Adipose Tissues. Intrinsic differences in gene expression and response to cold exposure. Preprint
• 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. To appear
• Minas C., Curry E., and Montana G. (2013) A distance-based test of association between paired heterogeneous genomic data. [arXiv]
• 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]
• 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. To appear
• Wang Y., Goh W, Wong L. and Montana G. (2013) Random forests on Hadoop for genome-wide studies of multivariate neuroimaging phenotypes. Preprint
• Sim A. and Montana G. (2013) Random forest regression on distance matrices for imaging genomics studies. Preprint
• 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]
• Minas C. and Montana G. (2013) Distance-based analysis of variance: approximate inference [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) Subspace clustering of high-dimensional data: a predictive approach. Data Mining and Knowledge Discovery. To appear [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]
• Cozzini A, Jasra A. and Montana G. (2012) A Bayesian Mixture of Lasso Regressions with t-Errors. [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, to appear [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, to appear
• 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, to appear
• 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

Software

• 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

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
• Session organiser, Statistical Methods for the Analysis of Large Data Sets 2009
• Member of the Program Committe
  • IDA (Intelligent Data Analysis) 2011, 2012, 2013
  • ICPRAM (International Conference on Pattern Recognition Applications) 2012, 2013, 2014
  • MASAMB (Mathematical and Statistical Aspects of Molecular Biology) 2013
• Chair, CompBio 2011
• Chartered Statistician (since 2006)
• Fellow of the Royal Statistical Society
• Royal Statistical Society Committee Member
  • Statistical Computing Section
  • Business & Industry Section
• Member of the Computing and Research Committees, IC Dept of Mathematics
• Visiting Senior Research Fellow, NUS School of Computing 2011
• Other IC affiliations: Centre for Bioinformatics, Centre for Biostatistics

For fun

• Alternative Olympic 2012 medal table - The Guardian
• Descent of Pop - charting the evolution of popular music