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

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

Research interests

Events

Research group

  • Current Research Associates:
    • Savelie Cornegruta (KCL)
    • Ai Chung (KCL)
    • Rudra Poudel (KCL)
    • Zi Wang (KCL)
  • Current PhD students:
    • Michelle Krishnan (KCL)
    • Ruchi Upmanyu (KCL)
    • Petros-Pavlos Ypsilantis (KCL)
    • Nicolo Savioli (KCL)
    • Zhana Kuncheva (ICL)
    • Dimosthenis Tsagkrasoulis (ICL)
    • Ricardo Monti (ICL)
  • Previous Research Associates at ICL:
    • Chris Minas - BHF
    • Mansour Sharabiani - NIHR
    • Rene Gausoin - NIHR
    • Peter Nash - EPSRC
    • Becky Inkster - WT, VIP award
  • Previous PhD Students at ICL:
    • Zi Wang - BRC
    • Ryan Ruan
    • Chris Minas - EPSRC
    • Anand Pandit
    • Matt Silver - Wellcome Trust
    • Maria Vounou - EPSRC and GSK Clinical Imaging Center
    • Maurice Berk - Wellcome Trust
    • Brian McWilliams - EPSRC
    • Alberto Cozzini - AHL / Man Group
    • Theo Tsagaris - Bluecrest Capital
    • Orlando Dohering (MPhil)
  • Previous Academic Visitors at ICL:
    • Yue Wang - PhD Student visiting from NUS
    • Eva Jasounova - Leonardo da Vinci award
    • Francesco Parrella - Leonardo da Vinci award

    Preprints and selected publications

    • Pio Monti R., Anagnostopoulos C. and Montana G. (2015) Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection. [arXiv]
    • Tsagkrasoulis D., Hysi P., Spector T., and Montana G. (2015) Heritability maps of the human face shape. Preprint. See also heritabilitymaps.info
    • Lorenz R., Pio Monti R., Violante I., Faisal A., Anagnostopoulos C., Leech R., and Montana G. (2015) Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization. 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner. To appear. [arXiv]
    • Pio Monti R., Lorenz R., Leech R., Anagnostopoulos C., and Montana G. (2015) Streaming regularization parameter selection via stochastic gradient descent. [arXiv]
    • Krishnan M., Zhang Z., Silver M., Boardman J., Ball G., Counsell S., Walley A., and Edwards D. and Montana G. (2015) Identification of genes in lipid metabolism associated with white matter integrity in preterm infants using the graph-guided group lasso. Preprint
    • Krishnan M., Zhang Z., Silver M., Boardman J., Ball G., Counsell S., Walley A., Montana G., and Edwards D. (2015) Lipid pathways mediate genetic susceptibility to brain injury in preterm infants. Preprint
    • Ruan D., Young A., and Montana G. (2015). Differential analysis of biological networks. BMC Bioinformatics. [arXiv]
    • Lorenz R., Pio Monti R., Violante I.R., Anagnostopoulos C., Faisal A.A., Montana G. and Leech G. (2015) The automatic neuroscientist: automated experimental design with real-time fMRI [arXiv]
    • Kuncheva Z. and Montana G. (2015). Community detection in multiplex networks using locally adaptive random walks. In Proceedings on the First International Workshop on Multiplex and Attributed Network Mining. Paris, 25 August 2015 [arXiv]
    • Pio Monti R., Lorenz R., Anagnostopoulos C., Leech R., and Montana G. (2015) Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data. [arXiv] In Proceedings of PRNI 2015, to appear - Best paper award
    • Janousova E., Schwarz D., Montana G., and Kasparek T. (2015) Brain image classification based on automated morphometry and penalised linear discriminant analysis with resampling. In Proceedings of the 5th International Workshop on Artificial Intelligence in Medical Applications.
    • De Brébisson A. and Montana G. (2015) Deep neural networks for anatomical brain segmentation. In Proceedings of CVPR 2015, Bioimage Computing Workshop- Best paper award [arXiv]
    • Pio Monti R., Lorenz R., Anagnostopoulos C., Leech R., and Montana G. (2015) Measuring the functional connectome on-the-fly: towards a new control signal for fMRI-based brain-computer interfaces. [arXiv]
    • Wang Z. and Montana g. (2015) Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons. Bioinformatics. [arXiv]
    • Ypsilantis P., Siddique M., Sohn H., Davies A., Cook G., Goh V., and Montana G. (2015) Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PloS One.
    • Ayaru L, Ypsilantis P, Nanapragasam A, Chang-Ho Choi R, Thillanathan A, Min-Ho L, and Montana G (2015) Prediction of outcome in acute lower gastrointestinal bleeding using gradient boosting. PloS One.
    • Payan A. and Montana G. (2015) Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural network. In Proceedings of ICPRAM 2015. [arXiv]
    • Janousova E. et al (2015) 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
    • Wang Z., Curry E., and Montana G. (2014). Network-guided regression for detecting associations between DNA methylation and gene expression. Bioinformatics.
    • Pio Monti R., Hellyer P., Sharp D., Leech R., Anagnostopoulos C., Montana G. (2014) Estimating dynamic brain connectivity networks from functional MRI time series. Neuroimage. [arXiv]
    • 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.
    • Cozzini A, Jasra A., Montana G. and Persing A. (2014) A Bayesian mixture of lasso regressions with t-errors. Computational Statistics and Data Analysis. [arXiv]
    • Minas C. and Montana G. (2014) Distance-based analysis of variance: approximate inference. Statistical Analysis and Data Mining. [arXiv]
    • McWilliams B. and Montana G. (2014) Subspace clustering of high-dimensional data: a predictive approach. Data Mining and Knowledge Discovery. Volume 28, Issue 3, pp 736-772 [arXiv]
    • 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 [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. PloS Genetics. [arXiv]
    • 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. Bioinformatics. [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.
    • 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

    Code

    • sMVMF: Python code for Sparse Multi-View Matrix Factorisation
    • 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
      • Workshop on Statistically Sound Data Mining @ ECML/PKDD 2014
      • ERCIM (Computational and Methodological Statistics) 2014-15
      • CISIS (Complex, Intelligent, and Software Intensive Systems) 2014
      • IDA (Intelligent Data Analysis) 2011-2014
      • ICPRAM (International Conference on Pattern Recognition Applications) 2012-2016
      • 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

  •