• Research interests
• Monograph
• Articles
• Seminars
• Web documents
• Bibliometry

Research interests

• Data smoothing
• Machine learning
• Statistical software
• Statistical applications for experimental data
• https://orcid.org/0000-0002-1198-3482

Monograph

Multivariate Kernel Smoothing and Its Applications (2018) with J.E. Chacón, Chapman & Hall/CRC. URL: mvstat.net/mvksa

Articles

• Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service (2022) with P. Papoutsis et al. Journal of Applied Statistics 50, 1310 - 1333.
• A New Nearest Neighbor Median Shift Clustering for Binary Data with G. Beck, M. Lebbah, H. Azzag. ICANN 2021: Artificial Neural Networks and Machine Learning, pp. 101 - 112
• Relaxing door-to-door matching reduces passenger waiting times: A workflow for the analysis of driver GPS traces in a stochastic carpooling service (2021) with P. Papoutsis, S. Fennia et al. Transportation Engineering 4, 100061.
• A Big Data platform for enhancing life imaging activities (2019) with L. Abidi et al. In J. Darmont and S. Loudcher, eds, Utilizing Big Data Paradigms for Business Intelligence, pp. 39 - 71.
• A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering (2019) with G. Beck et al. Journal of Parallel and Distributed Computing 134, 128 - 139.
• Tail density estimation for exploratory data analysis using kernel methods (2019) with B. Beranger, S.E. Perkins-Kirkpatrick, S.A. Sisson. Journal of Nonparametric Statistics 31, 144 - 174.
• Mean-shift : Clustering scalable et distribué (in French) (2018) with G. Beck, H. Azzag, M. Lebbah, C. Cérin. EGC 2018: Revue des Nouvelles Technologies de l'Information, 415 - 425.
• A complete data science workflow for insurance field (2018) with M. Ghesmoune, M. Lebbah, H. Azzag, S. Benbernou, M. Ouziri. 2018 IEEE International Conference on Big Data, 1925 - 1930.
• Big Data: from collection to visualization (2017) with M. Ghesmoune, H. Azzag, S. Benbernou, M. Lebbah, M. Ouziri. Machine Learning 106, 837 - 862.
• The ocean has depth: two- versus three-dimensional use estimators in a demersal reef fish (2017) with K. Lee, C. Huveneers, R.G. Harcourt. Marine Ecology Progress Series 572, 223 - 241.
• Distributed mean shift clustering with approximate nearest neighbours (2016) with G. Beck, H. Azzag, M. Lebbah. 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 3110 - 3115.
• Non-parametric smoothed estimation of multivariate distribution and survival functions, and receiver operating characteristic curves (2016) Journal of the Korean Statistical Society 45, 33 - 50.
• Nearest neighbour estimators of density derivatives, with application to mean shift clustering (2016) with G. Beck, H. Azzag, M. Lebbah. Pattern Recognition Letters 80, 224 - 230.
• Efficient recursive algorithms for functionals based on higher order derivatives of the multivariate Gaussian density (2015) with J.E. Chacón. Statistics and Computing 25, 959 - 974. R script file.
• Spherically symmetric multivariate beta family kernels (2015) Statistics and Probability Letters 104, 141 - 145.
• Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data (2014) with S. Pyne et al. PLoS ONE 9, e100334.
• A novel organelle map framework for high-content cell morphology analysis in high throughput (2014) with K. Schauer et al. Journal of Biomolecular Screening 19, 317 - 324.
• Data-driven density estimation, with applications to nonparametric clustering and bump hunting (2013) with J.E. Chacón. Electronic Journal of Statistics 7, 499 - 532.
• Local significant differences from nonparametric two-sample tests (2013) Journal of Nonparametric Statistics 25, 635 - 645.
• Studying intracellular trafficking pathways with probabilistic density maps (2013) with K. Schauer, C.S. Gomes-Santos, B. Goud. Methods in Cell Biology 118, 326 - 343.
• First closed-form density based framework for automatic detection for cellular morphology changes (2012) with B. Goud, K. Schauer. Proceedings of the National Academy of Sciences 109, 8382 - 8387.
• Unconstrained pilot selectors for smoothed cross validation (2011) with J.E. Chacón. Australian & New Zealand Journal of Statistics 53, 331 - 351.
• Asymptotics for general multivariate kernel density derivative estimators (2011) with J.E. Chacón, M.P. Wand. Statistica Sinica 21, 807 - 840.
• Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices (2010) with J.E. Chacón. Test 19, 375 - 398.
• Ezrin tunes T cell activation by controlling Dlg1 and microtubule positioning at the immunological synapse (2010) with R. Lasserre et al. EMBO Journal 29, 2301 - 2314.
• Probabilistic density maps to study global endomembrane organization (2010) with K. Schauer et al. Nature Methods 7, 560 - 567.
• Chromosome arm length and nuclear constraints determine the dynamic relationship of yeast subtelomeres (2010) with P. Thérizols, B. Dujon, E. Fabre, C. Zimmer. Proceedings of the National Academy of Sciences 107, 2025 - 2030.
• Highest density difference region estimation with application to flow cytometric data (2009) with I. Koch, M.P. Wand. Biometrical Journal 3, 504 - 521.
• High-resolution statistical mapping reveals gene territories in live yeast Nature Methods (2008) with A.B. Berger et al. 5, 1031 - 1037.
• Feature significance for multivariate kernel density estimation (2008) with A. Cowling, I. Koch, M.P. Wand. Computational Statistics and Data Analysis 52, 4225 - 4242.
• ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R (2007) Journal of Statistical Software 21(7), 1 - 16.
• Feature significance for multivariate data and kernel density estimation (2006) with A. Cowling, I. Koch, M.P. Wand. In Proceedings of the 8th Workshop on Nonparametric Statistical Analysis and Related Areas, Keio University, Tokyo, 27-28 March 2006, 34 - 42.
• Cross-validation bandwidth matrices for multivariate kernel density estimation (2005) with M.L. Hazelton. Scandinavian Journal of Statistics 32, 485 - 506.
• Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation (2005) with M.L. Hazelton. Journal of Multivariate Analysis 93, 417 - 433.
• Plug-in bandwidth selectors for bivariate kernel density estimation (2003) with M.L. Hazelton. Journal of Nonparametric Statistics 15, 17 - 30.
• Efficient day-to-day simulation of traffic systems with applications to pre-trip information with M.L. Hazelton. In Proceedings of the 8th World Congress on Intelligent Transport Systems [CD-ROM].

Ph.D. thesis

• Bandwidth selectors for multivariate kernel density estimation. PhD thesis (2004) University of Western Australia, Australia. Errata.

Seminars

• Interaction of abstract and concrete questions for kernel estimators. Theoretical and Applied Statistics Laboratory (LSTA), University Pierre and Marie Curie - Paris 6, Paris, France, 27 Nov 2012.
• Quantitative statistical analysis of biological experimental data. Institut Curie, Paris, France, Mar 2010.
• Statistical reconstruction of yeast nuclear organisation. Institut Pasteur, Paris, France, Oct 2008.
• A tour of kernel smoothing. Institut Pasteur, Paris, France, Oct 2007.
• Estimating highest density difference regions using generalised chi-squared tests. Fred Hutchinson Cancer Research Center, Seattle, USA. Nov 2006.
• Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation. University of Sydney, Sydney, Australia. Aug 2006.
• Feature significance for multivariate kernel density estimation.
  • Massey University, Palmerston North, New Zealand, July 2006.
  • Australian Statistics Conference/ New Zealand Statistical Association Conference, Auckland, New Zealand, July 2006.
  • University of New South Wales, Sydney, Australia, May 2006.
• Software for multivariate kernel smoothing University of New South Wales, Sydney, Australia, Sept 2005.
• Bandwidth selectors for multivariate kernel density estimation
  • Macquarie University, Sydney, Australia, Mar 2005.
  • Université Laval, Québec, Canada, Aug 2004.
  • Université de Montréal, Montréal, Canada, Aug 2004.
• Applications of bivariate kernel density estimators Macquarie University, Sydney, Australia, Dec 2003.
• Plug-in bandwidth selectors for bivariate kernel density estimation
  • University of Western Australia, Australia, May 2002
  • International Conference on Current Advances and Trends in Non-parametric Statistics, Crete, Greece, July 2002
  • Oxford University, United Kingdon, May 2003.
• Efficient day-to-day simulation of traffic systems with applications to pre-trip information, 8th World Congress on Intelligent Transport Systems, Sydney, Australia, Oct 2001.
• An introduction to kernel density estimation, University of Western Australia, Australia, May 2001.
• Testing food-intake hypotheses using the ABS National Nutrition Survey. Workshop on Sample Surveys and Social and Economic Statistics, University of Wollongong, Wollongong, Australia, June 2000.

Web documents

• R vignette: Kernel density estimates for tidy and geospatial data in the eks package
• R vignette: ks: Kernel density estimation for bivariate data
• R vignette: feature: an R package for feature significance for multivariate kernel density estimation
• R vignette: Using prim for bump hunting
• R vignette: Using prim to estimate highest density difference regions
• Wikipedia page: Multivariate kernel density estimation

Bibliometry

• Google Scholar
• Scopus (preview)