Professor of Biostatistics and Bioinformatics
Visiting Professor of Statistics

Research interests
 Machine learning and statistical pattern recognition
 Highdimensional data modelling
 Realtime data analytics
 Bioinformatics and medical imaging
Workshop organisation
 Workshop on Computational Intelligence Approaches for MultiView Data Analytics  IEEE World Congress on Computational Intelligence  Paper submission: February 05, 2015
 Workshop on Deep Learning for Computer Vision  13 November 2015, Edinburgh.
 Workshop on Computational Methods for Massive/Complex Data  1920 June 2014 IC, London
 Workshop on Big Data Mining  1415 May 2013 IC, London
 Industrial statistics event, Big Data theme  25 March 2013 Newton Institute, Cambdridge
 Workshop on Genomic Data Integration  1 March 2013 IC, London
Research team
 Current Research Associates:
 Mauro Ammarumma (KCL)
 Emanuele Pesce (KCL)
 Savelie Cornegruta (KCL)
 Rudra Poudel (KCL)
 Current PhD students:
 Ksenia Grozdova (KCL), Philips Healthcare
 Michelle Krishnan (KCL), Wellcome Trust
 Ruchi Upmanyu (KCL), Roche
 PetrosPavlos Ypsilantis (KCL), Wellcome Trust
 Nicolo Savioli (KCL), Siemens Healthcare
 Zhana Kuncheva (ICL), EPSRC
 Dimosthenis Tsagkrasoulis (ICL), EPSRC
 Ricardo Monti (ICL), EPSRC
 Previous Research Associates:
 Zi Wang (KCL), BRC
 Ai Chung (KCL), BRC
 Chris Minas (ICL), BHF
 Mansour Sharabiani (ICL), NIHR
 Rene Gausoin (ICL), NIHR
 Peter Nash (ICL), EPSRC
 Becky Inkster (ICL), WT / VIP Award
 Previous PhD Students:
 Zi Wang (ICL), BRC
 Ryan Ruan (ICL)
 Chris Minas (ICL), EPSRC
 Anand Pandit (ICL)
 Matt Silver (ICL), Wellcome Trust
 Maria Vounou (ICL), EPSRC and GlaxoSmithKline
 Maurice Berk (ICL), Wellcome Trust
 Brian McWilliams (ICL), EPSRC
 Alberto Cozzini (ICL), AHL / Man Group
 Theo Tsagaris (ICL), Bluecrest Capital
 Orlando Dohering (ICL)
 Previous Academic Visitors:
 Yue Wang (ICL), PhD Student visiting from NUS
 Eva Jasounova (ICL), Leonardo da Vinci Award
 Francesco Parrella (ICL), Leonardo da Vinci Award
Preprints and selected publications
 Cole J., Poudel R., Tsagkrasoulis D., Caan M., Steves C., Spector T., and Montana G. (2016) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Preprint [arxiv] See also: MIT Technology Review
 Tsagkrasoulis D., and Montana G. (2016) Random forest regression for manifoldvalued responses. Preprint
 Monti R., Lorenz R., Leech R., Anagnostopoulos C., and Montana G. (2016) Decoding timevarying functional connectivity networks via linear graph embedding methods. Preprint
 Monti R., Anagnostopoulos C., and Montana G. (2016) A framework for adaptive regularization in streaming Lasso models. Preprint
 Kuncheva Z., Krishnan M., and Montana G. (2016). Exploring brain transcriptomic patterns: a topological analysis using spatial expression networks. Pacific Symposium on Biocomputing.
 Ypsilantis P. and Montana G. (2016). Recurrent Convolutional Neural Networks for Pulmonary Nodule Detection in CT Imaging. Preprint [arxiv]
 Cornegruta S., Bakewell R., Withey S., and Montana G. (2016) Modelling Radiological Language with Bidirectional Long ShortTerm Memory Networks. 7th International Workshop on Health Text Mining and Information Analysis [arxiv]
 Poudel R., Lamata P. and Montana G. (2016) Recurrent Fully Convolutional Neural Networks for Multislice MRI Cardiac Segmentation. MICCAI Workshop [arXiv]
 Monti R., Lorenz R., Anagnostopoulos C., Leech R., and Montana G. (2016) Realtime estimation of dynamic functional connectivity networks. Human Brain Mapping, in press [arXiv]
 Lorenz R., Monti R., Violante I.R., Anagnostopoulos C., Faisal A.A., Montana G. and Leech G. (2016) The automatic neuroscientist: automated experimental design with realtime fMRI. Neuroimage Vol. 129, Pages 320334 [arXiv]
 R. Monti, R. Lorenz, R. Leech, C. Anagnostopoulos, and G. Montana (2016) Textmining the NeuroSynth corpus using Deep Boltzmann Machines. 6th International Workshop on Pattern Recognition in Neuroimaging, to appear. [arXiv]
 R. Lorenz, R. Monti, A. Hampshire, Y. Koush, C. Anagnostopoulos, A. A. Faisal, D. Sharp, G. Montana, R. Leech, and I. R. Violante (2016) Towards tailoring noninvasive brain stimulation using realtime fMRI and Bayesian optimization. 6th International Workshop on Pattern Recognition in Neuroimaging, to appear. [arXiv]
 A. W. Chung, E. Pesce, R. Monti, and G. Montana (2016) Classifying HCP TaskfMRI Networks Using Heat Kernels. 6th International Workshop on Pattern Recognition in Neuroimaging, to appear. [arXiv]
 Z. Wang, V. Karolis, C. Nosarti, and G. Montana (2016) Studying the brain from adolescence to adulthood through sparse multiview matrix factorisations. 6th International Workshop on Pattern Recognition in Neuroimaging, to appear. [arXiv]
 Chung A., Schirmer M., Krishnan M., Ball G., Aljabar P., Edwards D., and Montana G. (2016) Characterising brain network topologies: a dynamic analysis approach using heat kernels. Neuroimage, to appear. [arXiv]
 Monti R., Anagnostopoulos C. and Montana G. (2016) Learning population and subjectspecific brain connectivity networks via mixed neighborhood selection. Preprint [arXiv]
 Tsagkrasoulis D., Hysi P., Spector T., and Montana G. (2016) Heritability maps of human face morphology through largescale automated threedimensional phenotyping. See also heritabilitymaps.info. Preprint [arXiv]
 Krishnan M., Zhang Z., Silver M., Boardman J., Ball G., Counsell S., Walley A., Montana G., and Edwards D. (2016) Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants. Brain and Behaviour.
 Janousova E., Montana G., Kasparek T., Schwarz D. (2016) Supervised, Multivariate, Wholebrain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research. Frontiers in Neuroscience.
 Lorenz R., Monti R., Violante I., Faisal A., Anagnostopoulos C., Leech R., and Montana G. (2015) Stopping criteria for boosting automatic experimental design using realtime fMRI with Bayesian optimization. 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner. To appear. [arXiv]
 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 graphguided group lasso. Preprint
 Ruan D., Young A., and Montana G. (2015). Differential analysis of biological networks. BMC Bioinformatics. [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]
 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] 5th International Workshop on Pattern Recognition in Neuroimaging  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]
 Wang Z. and Montana g. (2015) Sparse multiview 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, ChangHo Choi R, Thillanathan A, MinHo 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]
 Wang Z. and Montana G. (2014) The graphguided group lasso for genomewide 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). Networkguided regression for detecting associations between DNA methylation and gene expression. Bioinformatics.
 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 distancebased 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 terrors. Computational Statistics and Data Analysis. [arXiv]
 Minas C. and Montana G. (2014) Distancebased analysis of variance: approximate inference. Statistical Analysis and Data Mining. [arXiv]
 McWilliams B. and Montana G. (2014) Subspace clustering of highdimensional data: a predictive approach. Data Mining and Knowledge Discovery. Volume 28, Issue 3, pp 736772 [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 757786 [arXiv]
 Silver M., Chen P., Ruoying L., Cheng CY, Wong TY, Tai E., Teo YY, and Montana G. (2013) Pathwaysdriven sparse regression identifies pathways and genes associated with highdensity 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., MartinonTorres 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):16648.
 Minas C., Curry E., and Montana G. (2013) A distancebased 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) Wholebrain 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 genomewide studies of multivariate neuroimaging phenotypes. BMC Bioinformatics.
 Cozzini A, Jasra A. and Montana G. (2013) Modelbased clustering with gene ranking using penalised mixtures of heavytailed 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) Smchd1dependent 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 16811694 [arXiv]
 McWilliams B. and Montana G. (2012) Multiview predictive partitioning in high dimensions. Statistical Analysis and Data Mining,5(4): 304321 [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), 111
 Berk M. and Montana G. (2012) A skewtnormal multilevel reducedrank 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 reducedrank regression detects genetic associations with voxelwise longitudinal phenotypes in Alzheimer's disease. NeuroImage, 60(1):700716
 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.247252.
 Minas C, Waddell S. and Montana G. (2011) Distancebased differential analysis of gene curves. Bioinformatics, 27 (22): 31353141.
 Pathan N., Burmester M., Adamovic T., Berk M., Ng K., Betts H., Macrae M., Waddell S., PaulClark 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:12611269
 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 brainwide 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. 19791985.
 Berk M., Hemingway C., Levin M. and Montana G. (2011). Longitudinal analysis of gene expression profiles using functional mixedeffects models. In 'Studies in Theoretical and Applied Statistics' pp 5767. Springer. [arXiv]
 Triantafyllopoulos, K. and Montana, G. (2011) Dynamic modeling of meanreverting spreads for statistical arbitrage. Computational Management Science. Vol 8, Issue 1, pp. 2349 [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. 15431546
 McWilliams B. and Montana G. (2010) A PRESS statistic for twoblock 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 highdimensional neuroimaging phenotypes: a sparse reducedrank regression approach. NeuroImage, 5;53(3), pp. 114759.
 Silver M., Montana G., and Nichols T. (2010). False positives in neuroimaging genetics using voxel based morphometry data. NeuroImage, 15;54(2), pp. 9921000
 Tang Y. A., Huntley, D., Montana G., Cerase A., Nesteroa, T. B. and Brockdorff N. (2010) Efficiency of Xistmediated 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, 307315, Springer.
 McWilliams B. and Montana G. (2010) Sparse partial least squares for online variable selection in multivariate data streams. Statistical Analysis and Data Mining. 3: 170193. [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, 2021 July, London. [Video]
 Berk M. and Montana G. (2009). Functional modelling of microarray time series with covariate curves. Statistica, 23, pp. 153177 [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. 28192830. [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. 591598. SpringerVerlag
 Montana G., Triantafyllopoulos K. and Tsagaris, T. (2008) Data stream mining for marketneutral algorithmic trading. In Proceedings of ACM Symposium on Applied Computing, pp. 966970.
 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. 393395
 Adams N.M., Hand D.J., Montana G. and Weston D. (2006). Fraud Detection in consumer credit. Expert Update, 9(1), pp. 2127. (Special Issue on the 2nd UK KDD Workshop)
 Montana G. (2006) Statistical methods in genetics. Briefings in Bioinformatics 7(3), pp. 297308
 Montana G. (2005) HapSim: A simulation tool for generating haplotype data with prespecified allele frequencies and LD patterns. Bioinformatics 21(23), pp. 43094311
 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. 18851892
 Montana G. and Pritchard J. K. (2004) Statistical tests for admixture mapping with casecontrol and caseonly data. American Journal of Human Genetics 75, pp. 771789
 Kendall W.S. and Montana G. (2002) Small sets and Markov transition kernels. Stochastic Processes and Their Applications 99(2), pp. 17719
Code
 sMVMF: Python code for Sparse MultiView Matrix Factorisation
 NsRRR: R code for Networkguided sparse ReducedRank 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 multicore) Linear Algebra in R
 PaRFR: Java implementation of parallel random forest regression for hadoop
 PsRRR: Python code for pathwayssparse reducedrank 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 (multiview predictive partitioning) algorithm
 PTM: R code for the PTM (penalised finite mixtures of t distributions) model
 DBF: R code for the DBF (distancebased 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 online support vector regression
 DLM: C++ code for fitting dynamic linear models
 I maintain the CRAN Task View on Statistical Genetics
Courses
 Current courses
 Machine Learning (KCL), postgraduate
 Research Skills (KCL), postgraduate
 Statistical Pattern Recognition (London Taught Course Centre), postgraduate
 Previous courses
 Machine Learning (ICL), MSc in Statistics
 Statistical Learning (ICL), MSc in Bioinformatics and Theoretical Systems Biology
 Probability and Statistics for Mechanical Engineers (ICL), undergraduate
 Probability and Statistics for Electrical Engineers (ICL), undergraduate
Previous positions
 Research Biostatistician  Statistical Genetics and Biomarkers Group, BristolMyers 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, 201314
 Member of the Program Committe
 Workshop on Statistically Sound Data Mining @ ECML/PKDD 2014
 ERCIM (Computational and Methodological Statistics) 201415
 CISIS (Complex, Intelligent, and Software Intensive Systems) 2014
 IDA (Intelligent Data Analysis) 20112014
 ICPRAM (International Conference on Pattern Recognition Applications) 20122016
 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, 20102013
 Visiting Senior Research Fellow, NUS School of Computing, 2011