Mark Girolami

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Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Simon RogersSenior Lecturer, Computing Science, University of GlasgowVerified email at glasgow.ac.uk
Ben CalderheadImperial College LondonVerified email at imperial.ac.uk
Theodoros (Theo) DamoulasProfessor, CS+Stats, University of Warwick - UKRI Turing AI FellowVerified email at warwick.ac.uk
François-Xavier BriolAssociate Professor in Statistical Science, UCLVerified email at ucl.ac.uk
Ata KabanProfessor, University of Birmingham, School of Computer ScienceVerified email at bham.ac.uk
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoVerified email at salk.edu
Te-Won LeeVerified email at ucsd.edu
Alessandro BarpUniversity College London, The Alan Turing InstituteVerified email at ucl.ac.uk
Maurizio FilipponeAssociate Professor - Statistics Program, KAUSTVerified email at kaust.edu.sa
Simon ByrneCliMA project, California Institute of TechnologyVerified email at caltech.edu
Colin CampbellUniversity of BristolVerified email at bris.ac.uk
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Andrew B. DuncanImperial College London and Alan Turing InstituteVerified email at imperial.ac.uk
Vassilios StathopoulosResearch Associate, University College LondonVerified email at ucl.ac.uk
Andrew M StuartComputing + Mathematical Sciences, CaltechVerified email at caltech.edu
Walter KolchDirector, Systems Biology Ireland, University College DublinVerified email at ucd.ie
Roman RosipalSenior Researcher at Slovak Academy of Sciences & CTO at Pacific Development and Technology, LLCVerified email at savba.sk
Kate Elizabeth JonesProfessor Ecology and Biodiversity, University College LondonVerified email at ucl.ac.uk
Ioannis Brilakis, PhDLaing O'Rourke Professor of Civil & Information Engineering, University of CambridgeVerified email at cam.ac.uk

Mark Girolami

University of Cambridge

Verified email at cam.ac.uk - Homepage

Computational Statistics Bayesian Statistics Machine Learning Data Centric Engineering


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Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources TW Lee, M Girolami, TJ Sejnowski Neural computation 11 (2), 417-441, 1999	2562	1999
Riemann manifold langevin and hamiltonian monte carlo methods M Girolami, B Calderhead Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2011	2033	2011
Mercer kernel-based clustering in feature space M Girolami IEEE transactions on neural networks 13 (3), 780-784, 2002	1205	2002
Blind source separation of more sources than mixtures using overcomplete representations TW Lee, MS Lewicki, M Girolami, TJ Sejnowski IEEE signal processing letters 6 (4), 87-90, 1999	642	1999
A unifying information-theoretic framework for independent component analysis TW Lee, M Girolami, AJ Bell, TJ Sejnowski Computers & Mathematics with Applications 39 (11), 1-21, 2000	612	2000
Hamiltonian Monte Carlo for hierarchical models M Betancourt, M Girolami Current trends in Bayesian methodology with applications 79 (30), 2-4, 2015	596	2015
Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease DM Good, P Zürbig, A Argiles, HW Bauer, G Behrens, JJ Coon, M Dakna, ... Molecular & cellular proteomics 9 (11), 2424-2437, 2010	577	2010
Construction with digital twin information systems R Sacks, I Brilakis, E Pikas, HS Xie, M Girolami Data-Centric Engineering 1, e14, 2020	522	2020
A first course in machine learning S Rogers, M Girolami Chapman and Hall/CRC, 2016	389	2016
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights A Malekloo, E Ozer, M AlHamaydeh, M Girolami Structural Health Monitoring 21 (4), 1906-1955, 2022	380	2022
Clinical proteomics: a need to define the field and to begin to set adequate standards H Mischak, R Apweiler, RE Banks, M Conaway, J Coon, A Dominiczak, ... PROTEOMICS–Clinical Applications 1 (2), 148-156, 2007	361	2007
Advances in urinary proteome analysis and biomarker discovery D Fliser, J Novak, V Thongboonkerd, V Jankowski, MA Girolami, ... Journal of the American Society of Nephrology 18 (4), 1057-1071, 2007	349	2007
On an equivalence between PLSI and LDA M Girolami, A Kabán Proceedings of the 26th annual international ACM SIGIR conference on …, 2003	346	2003
Recommendations for biomarker identification and qualification in clinical proteomics H Mischak, G Allmaier, R Apweiler, T Attwood, M Baumann, A Benigni, ... Science translational medicine 2 (46), 46ps42-46ps42, 2010	339	2010
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015	321	2015
Kernel PCA for feature extraction and de-noising in nonlinear regression R Rosipal, M Girolami, LJ Trejo, A Cichocki Neural Computing & Applications 10, 231-243, 2001	308	2001
Probability density estimation from optimally condensed data samples M Girolami, C He IEEE Transactions on pattern analysis and machine intelligence 25 (10), 1253 …, 2003	306	2003
Variational Bayesian multinomial probit regression with Gaussian process priors M Girolami, S Rogers Neural Computation 18 (8), 1790-1817, 2006	299	2006
Sparse multinomial logistic regression via bayesian l1 regularisation G Cawley, N Talbot, M Girolami Advances in neural information processing systems 19, 2006	299	2006
Building information modelling, artificial intelligence and construction tech R Sacks, M Girolami, I Brilakis Developments in the Built Environment 4, 100011, 2020	274	2020

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