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Céline Lévy-leduc

Professeur des universités en mathématiques

Directrice de l'UFR de mathématiques du département MMIP (Modélisation Mathématique, Informatique et Physique) d'AgroParisTech

AgroParisTech (département MMIP) 
Campus Agro Paris-Saclay 
22, place de l'agronomie 
91120 Palaiseau (France)

Tél : 01.89.10.09.56.

Email : celine.levy-leduc@agroparistech.fr

Responsable AgroParisTech du Master 2 "Mathématiques pour les Sciences du Vivant" (MSV) de l'université Paris-Saclay

Co-responsable avec Christophe Giraud de la spécialisation "Machine learning en biologie et médecine" de ce master.

Co-responsable avec Marie Doumic du programme thématique : "Mathématiques pour les sciences du vivant" au sein de la FMJH (Fondation Mathématique Jacques Hadamard).

Membre du conseil de la Graduate School de Mathématiques de l'université Paris-Saclay.

 

Publications

Articles dans des revues internationales à comité de lecture

  1. M. E. Savino, C. Lévy-Leduc. A novel variable selection method in nonlinear multivariate models using B-splines with an application to geoscience, soumis. ⟨hal-04434820v2⟩
  2. M. E. Savino, C. Lévy-Leduc. A novel approach for estimating functions in the multivariate setting based on an adaptive knot selection for B-splines with an application to a chemical system used in geoscience, soumis. arXiv:2306.00686
  3. B. Decouard, N. B Chowdhury, A. Saou, M. Rigault, I. Quillere, T. Sapir, A. Marmagne, C. Paysant le Roux, A. Launay-Avon, F. Guerard, B. Gakiere, C. Mauve, C. Lévy-Leduc, P. Barbillon, R. Saha, B. Hirel, P-E Courty, D. Wipf, A. Dellagi. Maize (Zea mays L.) interaction with the arbuscular mycorrhizal fungus Rhizophagus irregularis allows mitigation of nitrogen deficiency stress: physiological and molecular characterization, bioRxiv
  4. V.A Reisen, C. Lévy-Leduc, C. C. Solci. Asymptotic properties of a novel M-estimator for ARMA models based on the Whittle approximation, soumis.
  5. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, C. Bailly, L. Rajjou. Variable selection in sparse multivariate GLARMA models: Application to germination control by environment, soumis. arXiv:2208.14721
  6. W. Zhu, C. Lévy-Leduc, N. Ternès. Variable selection in high-dimensional logistic regression models using a whitening approach, soumis. arXiv:2206.14850
  7. W. Zhu, E. Adjakossa, C. Lévy-Leduc, N. Ternès. Sign consistency of the generalized Elastic Net estimator, soumis. arXiv:2106.05454
  8. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet. Sign consistent estimation in a sparse Poisson model, Statistics and Probability Letters, vol. 209, p. 110107, 2024. arXiv:2303.14020
  9. V.A. Reisen, C. Lévy-Leduc, E.Z. Monte, P. Bondon. A dimension reduction factor approach for high-dimensional time series with long memory. A robust alternative method, Statistical Papers, 2023 [doi].
  10. W. Zhu, C. Lévy-Leduc, N. Ternès. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso, BMC Bioinformatics, vol. 24, n. 25, 2023. arXiv:2202.01970
  11. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet. Variable selection in sparse GLARMA models, Statistics, vol. 56, n. 4, p. 755-784, 2022. arXiv:2007.08623, [doi]
  12. C. Lévy-Leduc, P. Bondon, V.A. Reisen. A spectral approach to estimate the autocovariance function, Journal of Statistical Planning and Inference, vol. 221, p. 281-298, 2022. [doi], [Hal]
  13. M. Savino, C. Lévy-Leduc, M. Leconte, B. Cochepin. An active learning approach for improving the performance of equilibrium based chemical simulations, Computational Geosciences, vol. 26, p. 365-380, 2022, [doi], arXiv:2110.08111
  14. M. Perrot-Dockès, C. Lévy-Leduc, L. Rajjou. Estimation of large block structured covariance matrices: Application to "multi-omic" approaches to study seed quality, Journal of the Royal Statistical Society: Series C, vol. 71, n. 1, p. 119-147, 2022. [doi], arXiv:1806.10093
  15. W. Zhu, C. Lévy-Leduc, N. Ternès. A variable selection approach for highly correlated predictors in high-dimensional genomic data, Bioinformatics, vol. 37, n. 16, p. 2238–2244, 2021, [doi], arXiv:2007.10768
  16. A. Sarnaglia, V.A. Reisen, P. Bondon, C. Lévy-Leduc. M-regression spectral estimator  for periodic ARMA models. An empirical investigation, Stochastic Environmental Research, vol. 35, p. 653-664, 2021, [doi]
  17. C. Denis, E. Lebarbier, C. Lévy-Leduc, O. Martin and L. Sansonnet. A novel regularized approach for functional data clustering: An application to milking kinetics in dairy goats, Journal of the Royal Statistical Society: Series C, vol. 69, n.3, p. 623-640, 2020, arXiv:1907.09192
  18. M. Grandclaudon, M. Perrot-Dockès, C. Trichot, O. Mostafa-Abouzid, W. Abou-Jaoudé, F. Berger, P. Hupé, D. Thieffry, L. Sansonnet, J. Chiquet, C. Lévy-Leduc, V. Soumelis. A quantitative multivariate model of human dendritic cell-T helper cell communication, Cell, vol. 179, n. 2, p. 432-447, 2019. [link]
  19. A.M. Sgrancio, V.A. Reisen, F.A. Ziegelmann, E.Z. Monte, H. H. Aranda Cotta and C. Lévy-Leduc. Robust factor modeling for high-dimensional time series: An application to air pollution data, Applied Mathematics and Computation, vol. 346, p.842-852, 2019. [link]
  20. M. Perrot-Dockès, C. Lévy-Leduc, J. Chiquet, L. Sansonnet, M. Brégère, M.P. Etienne, S. Robin, G. Genta-Jouve. A variable selection approach in the multivariate linear model: An application to LC-MS metabolomics data, Statistical Applications in Genetics and Molecular Biology, vol. 17, n. 5, 2018. [link]
  21. V. Brault, C. Lévy-Leduc, A. Mathieu, A. Jullien. Change-point estimation in the multivariate model taking into account the dependence: Application to the vegetative development of oilseed rape, Journal of Agricultural, Biological, and Environmental Statistics, vol. 23, n. 3, p. 374-389, 2018. [link]
  22. A. Bonnet, C. Lévy-Leduc, E. Gassiat, R. Toro, T. Bourgeron. Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models, Journal of the Royal Statistical Society: Series C, vol. 67, n. 4, p. 813-839, 2018. [link]
  23. F.A. Fajardo, V.A. Reisen, C. Lévy-Leduc, M.S. Taqqu. Robust periodogram for time series with long-range dependence: an application to pollution levels, Statistics, vol. 52, n.3, p. 665-683, 2018. [doi]
  24. M. Perrot-Dockès, C. Lévy-Leduc, L. Sansonnet, J. Chiquet. Variable selection in multivariate linear models with high-dimensional covariance matrix estimation, Journal of Multivariate Analysis, vol. 166, p. 78 - 97, 2018. [link]
  25. V. Brault, S. Ouadah, L. Sansonnet, C. Lévy-Leduc. Nonparametric homogeneity tests and multiple change-point estimation for analyzing large Hi-C data matrices, Journal of Multivariate Analysis, vol. 165, p. 143-165, 2018. [link]
  26. V. Brault, M. Delattre, E. Lebarbier, T. Mary-Huard, C. Lévy-Leduc. Estimating the number of change-points in a two-dimensional segmentation model without penalization, Scandinavian Journal of Statistics, vol. 44, n. 2, p. 563-580, 2017. [link]
  27. V.A. Reisen, C. Lévy-Leduc, M.S. Taqqu. An M-estimator for the long-memory parameter, Journal of Statistical Planning and Inference, vol. 187, p. 44-55, 2017. [link]
  28. V. Brault, J. Chiquet, C. Lévy-Leduc. Efficient block boundaries estimation in block-wise constant matrices: An application to HiC data, Electronic Journal of Statistics, vol. 11, n. 1, p. 1570-1599, 2017. [link]
  29. S. Chakar, E. Lebarbier, C. Lévy-Leduc, S. Robin. A robust approach to multiple change-point estimation in an AR(1) process, Bernoulli, vol. 23, n. 2, p. 1408-1447, 2017. [link]
  30. V. A. Reisen, C. Lévy-Leduc, M. Bourguignon, H. Boistard. Robust Dickey-Fuller tests based on ranks for time series with additive outliers, Metrika, vol. 80, n. 1, p. 115–131, 2017. [link]
  31. M. Jala, C. Lévy-Leduc, E. Moulines, E. Conil, J. Wiart. Sequential design of computer experiments for the assessment of fetus exposure to electromagnetic fields. Technometrics, vol. 58, n. 1, p. 30-42, 2016. [link]
  32. A. Lung-Yut-Fong, C. Lévy-Leduc, O. Cappé. Homogeneity and change-point detection tests for multivariate data using rank statistics, Journal de la Société Française de Statistique, vol. 156, n. 4, p. 133-162, 2015. [link]
  33. A. Bonnet, E. Gassiat, C. Lévy-Leduc. Heritability estimation in high dimensional linear mixed models. Electronic Journal of Statistics 2015, Vol. 9, n.2, p. 2099-2129, 2015. [link]
  34. C. Lévy-Leduc, M. Delattre, T. Mary-Huard, S. Robin. Two-dimensional segmentation for analyzing HiC data, Bioinformatics, vol. 30, n.17, p. 386-392, 2014. [link]
  35. C. Lévy-Leduc, M. S. Taqqu. Hermite ranks and U-statistics, Metrika, vol. 77, n. 1, p 105-136, 2014. [link]
  36. V. A. Reisen, A.J. Sarnaglia, N.C Reis, C. Lévy-Leduc and J.M Santos. Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility, Environmental Modelling & Software, vol. 51, p. 286–295, 2014. [link]
  37. C. Lévy-Leduc, M. S. Taqqu. Long-range dependence and the ranks of decompositions, AMS Contemporary Mathematics, vol. 601, p. 289-305, 2013. [link]
  38. O. Kouamo, C. Lévy-Leduc, E. Moulines. Central limit theorem for the robust log-regression wavelet estimation of the memory parameter in the Gaussian semi-parametric context, Bernoulli, vol. 19, n. 1, p. 172-204, 2013 [link]
  39. A. Lung-Yut-Fong, C. Lévy-Leduc, O. Cappé. Distributed detection/localization of change-points in high-dimensional network traffic data, Statistics and Computing, vol. 22, n. 2, p. 485-496, 2012  [link]
  40. C. Lévy-Leduc, H. Boistard, E. Moulines, M. S. Taqqu, V. A. Reisen. Asymptotic properties of U-processes under long-range dependence, Annals of Statistics, vol. 39, n. 3, p. 1399-1426, 2011. [link]
  41. C. Lévy-Leduc, H. Boistard, E. Moulines, M. S. Taqqu, V. A. Reisen. Large sample behavior of some well-known robust estimators under long-range dependence, Statistics, vol. 45, n. 1, p. 59-71, 2011. [link]
  42. C. Lévy-Leduc, H. Boistard, E. Moulines, M. S. Taqqu, V. A. Reisen. Robust estimation of the scale and of the autocovariance function of Gaussian short and long-range dependent processes, Journal of Time Series Analysis, vol. 32, n. 2, p. 135-156, 2011. [link]
  43. Z. Harchaoui, C. Lévy-Leduc. Multiple change-point estimation with a total variation penalty, Journal of the American Statistical Association, vol. 105, n. 492, p. 1480-1493, 2010. [link]
  44. A. J. Q. Sarnaglia, V. A. Reisen, C. Lévy-Leduc. Robust estimation of periodic autoregressive processes in the presence of additive outliers, Journal of Multivariate Analysis, vol. 101, n. 9, p. 2168-2183, 2010. [link]
  45. C. Lévy-Leduc, F. Roueff. Detection and localization of change-points in high-dimensional network traffic data, Annals of Applied Statistics, vol. 3, n. 2, p. 637-662, 2009. [link]
  46. C. Lévy-Leduc, E. Moulines, F. Roueff. Frequency estimation based on the cumulated Lomb-Scargle periodogram, Journal of Time Series Analysis, vol. 29, n. 6, p. 1104-1131, 2008. [link]
  47. I. Castillo, C. Lévy-Leduc, C. Matias. Exact adaptive estimation of a periodic function with unknown period, Mathematical Methods of Statistics, vol. 15, n. 2, p. 146-175, 2006. [link]
  48. E. Gassiat, C. Lévy-Leduc. Efficient semiparametric estimation of the periods in a superposition of periodic functions with unknown shape, Journal of Time Series Analysis, vol. 27, n. 6, p. 877-910, 2006. [link]
  49. C. Lévy-Leduc. Efficient frequency estimation from a particular almost periodic function, Journal of Time Series Analysis, vol. 27, n. 5, p.637-670, 2006. [link]
  50. M. Fromont, C. Lévy-Leduc. Adaptive tests for periodic signals detection with applications to laser vibrometry, ESAIM Probability and Statistics, vol. 10, p. 46-75, 2005. [link]
  51. M. Lavielle, C. Lévy-Leduc. Semiparametric estimation of the frequency of unknown periodic functions and its application to laser vibrometry signals, IEEE Transactions on Signal Processing, vol. 53, n. 7, p. 2306-2315, 2005. [link]

Chapitres de livres

  1. M. Perrot-Dockès, C. Lévy-Leduc, G. Cueff, L. Rajjou. Sélection de variables dans le modèle linéaire général en grande dimension : application à des approches ``multi-omiques'' pour l'étude de la qualité des graines, chapitre du livre : "Intégration de données biologiques approches informatiques et statistiques" sous la direction de Christine Froideveaux, Marie-Laure Martin-Magniette et Guillem Rigaill, ISTE Editions, ISBN 9781789480306, 2022. 
  2. V.A. Reisen, C. Lévy-Leduc, H.H.A. Cotta, P. Bondon, M. Ispany. An overview of robust spectral estimators, Cyclostationarity: Theory and Methods - IV - Contributions to the 10th Workshop on Cyclostationary Systems and Their Applications, February 2017, Grodek, Poland, Editors: Fakher, C., Leskow, J., Zimroz, R., Wyłomańska, A., Dudek, A., published by Springer, 2019, https://doi.org/10.1007/978-3-030-22529-2.
  3. V.A. Reisen, C. Lévy-Leduc, H.H.A. Cotta. Long-memory models under outliers: an application to air pollution levels. Air and Noise Pollution, vol. 3 of the Series "Environmental Science and Engineering (12 Vols.)", 2017.
  4. C. Lévy-Leduc. Several approaches for detecting anomalies in network traffic data, In: Nicholas Heard, Niall Adams , Data analysis for network cyber-security. GBR : Niall Adams and Nicholas Heard, 2014.

Articles dans des conférences avec comité de lecture

  1. M. Savino, C. Lévy-Leduc. A novel approach for estimating functions in the multivariate setting based on an adaptive knot selection for B-splines, COMPSTAT 2023.
  2. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, C. Bailly, L. Rajjou. Variable selection in sparse multivariate GLARMA models: Application to germination control by environment, Statistical Methods for Post-Genomic Data, 2023.
  3. W. Zhu, C. Lévy-Leduc, N. Ternès. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso, International Society for Clinical Biostatistics, 2022.
  4. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, T. Blein. Variable selection in sparse GLARMA models, Statistical Methods for Post-Genomic Data, 2022.
  5. W. Zhu, C. Lévy-Leduc, N. Ternès. A variable selection approach for highly correlated predictors in high-dimensional genomic data, Statistical Methods for Post-Genomic Data, 2022.
  6. W. Zhu, C. Lévy-Leduc, N. Ternès. A variable selection approach for highly correlated predictors in high-dimensional settings: An application to gene expression data. International Society for Clinical Biostatistics, 2020.
  7. H. Cotta, V. Reisen, P. Bondon, C. Lévy-Leduc. Robust autocovariance estimation from the frequency domain. International conference on Time Series and Forecasting, 2018.
  8. A. J. Q. Sarnaglia, V. Reisen, P. Bondon, C. Lévy-Leduc. A robust estimation approach for fitting a PARMA model to real data.  IEEE International Workshop on Statistical Signal Processing, 2016.
  9. V. Brault, J. Chiquet, C. Lévy-Leduc. Fast Detection of Block Boundaries in Block-wise Constant Matrices. Machine Learning and Data Mining, 2016.
  10. C. Lévy-Leduc, M. Delattre, T. Mary-Huard, S. Robin. Two-dimensional segmentation for analyzing HiC data, ECCB 2014.
  11. M. Jala, C. Lévy-Leduc, E. Moulines, E. Conil, J. Wiart. Sequential design of computer experiments for parameter estimation, EUSIPCO 2012.
  12. C. Lévy-Leduc, M. S. Taqqu, E. Moulines, H. Boistard, V. A. Reisen. Asymptotic properties of U-processes under long-range dependence and applications, Bulletin of the International Statistical Institute, 2011.
  13. A. Lung-Yut-Fong, C. Lévy-Leduc, O. Cappé. Estimation robuste de ruptures multiples dans un signal multivarié, GRETSI 2011.
  14. O. Kouamo, C. Lévy-Leduc, E. Moulines. Robust estimation of the memory parameter of Gaussian time series using wavelets, IEEE International Workshop on Statistical Signal Processing (SSP) 2011.
  15. A. Lung-Yut-Fong, C. Lévy-Leduc, O. Cappé. Robust retrospective multiple change-point estimation for multivariate data, IEEE International Workshop on Statistical Signal Processing (SSP) 2011.
  16. T. Rebafka, C. Lévy-Leduc, M. Charbit. Regularization methods for intercepted radar signals, IEEE Radar Conderence 2011.
  17. A. Lung-Yut-Fong, C. Lévy-Leduc, O. Cappé. Robust changepoint detection based on multivariate rank statistics, International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2011.
  18. A. Lung-Yut-Fong, C. Lévy-Leduc, O. Cappé. Distributed detection/localization of network anomalies using rank tests, IEEE International Workshop on Statistical Signal Processing (SSP) 2009.
  19. A. Lung-Yut-Fong, O. Cappé, C. Lévy-Leduc, F. Roueff. Détection et localisation décentralisées d'anomalies dans le trafic internet, GRETSI 2009.
  20. C. Lévy-Leduc. Detection of network anomalies using rank tests, EUSIPCO 2008.
  21. Z. Harchaoui, C. Lévy-Leduc. Catching change-points with Lasso, NIPS 2007. 
  22. B. Benmammar, C. Lévy-Leduc, F. Roueff. Algorithme de détection d'attaques de type SYN Flooding, GRETSI 2007.
  23. Z. Harchaoui, C. Lévy-Leduc. Méthode de détection de ruptures utilisant l'algorithme LARS, GRETSI 2007.
  24. C. Lévy-Leduc. Frequency estimation from a particular almost periodic function, ICASSP 2006.
  25. C. Lévy-Leduc, M. Prenat. Laser vibrometry: estimation of the frequency of a rotating object, PSIP 2005.
  26. C. Lévy-Leduc. Algorithmes d'estimation de la fréquence de fonctions périodiques inconnues et applications à la vibrométrie laser, GRETSI 2003. 

Vulgarisation

  1. C. Lévy-Leduc, S. Robin. Les nouveaux défis de la biologie moléculaire. Symbiose, 2015.

Logiciels

1. W. Zhu, C. Lévy-Leduc, N. Ternès (2022). R package: PPLasso (available on the CRAN). This package proposes a new method to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data.  For further details we refer the reader to the paper: W. Zhu, C. Lévy-Leduc, N. Ternès. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso, soumis. arXiv:2202.01970

2. M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet (2021). R package: GlarmaVarSel (available on the CRAN). This package proposes a novel variable selection approach in sparse GLARMA models.  For further details we refer the reader to the paper: M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet. Variable selection in sparse GLARMA models, soumis. arXiv:2007.08623

3. W. Zhu, C. Lévy-Leduc, N. Ternès (2020). R package: WLasso (available on the CRAN). This package proposes a novel variable selection approach taking into account the correlations that may exist between the predictors of the design matrix in a high-dimensional linear model. Our approach consists in rewriting the initial high-dimensional linear model to remove the correlation between the predictors and in applying the generalized Lasso criterion. For further details we refer the reader to the paper: W. Zhu, C. Lévy-Leduc, N. Ternès. A variable selection approach for highly correlated predictors in high-dimensional genomic data, Bioinformatics, vol. 37, n. 16, p. 2238–2244, 2021  arXiv:2007.10768.

4. M. Perrot-Dockès, C. Lévy-Leduc (2018). R package: BlockCov (available on the CRAN). This package is dedicated to the computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. For further details we refer the reader to the paper Perrot-Dockes and Lévy-Leduc (2018), to appear in Journal of the Royal Statistical Society, Series C. arXiv:1806.10093.

5. M. Perrot-Dockès, C. Lévy-Leduc, J. Chiquet (2017). R package: MultiVarSel (available on the CRAN). This package is dedicated to the variable selection issue in high dimensional multivariate linear models taking into account the dependence between the columns of the observation matrix. The corresponding methodology is described in the paper: M. Perrot-Dockès, C. Lévy-Leduc, J. Chiquet, L. Sansonnet, M. Brégère, M.P. Etienne, S. Robin, G. Genta-Jouve. A variable selection approach in the multivariate linear model: An application to LC-MS metabolomics data, Statistical Applications in Genetics and Molecular Biology, vol. 17, n. 5, 2018.

6. A. Bonnet, C. Lévy-Leduc (2015). R package: EstHer (available on the CRAN) for estimating the heritability in high dimensional sparse linear mixed models using variable selection. The methodology used in this package is described in the paper: A. Bonnet, C. Lévy-Leduc, E. Gassiat, R. Toro, T. Bourgeron. Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models, Journal of the Royal Statistical Society: Series C, vol. 67, n. 4, p. 813-839, 2018.

7. C. Lévy-Leduc (2014). R package: HiCseg (available on the CRAN) which allows you to detect domains in HiC data. The methodology that is used in this package is described in the paper “Two-dimensional segmentation for analyzing HiC data” by C. Lévy-Leduc, M. Delattre, T. Mary-Huard and S. Robin, Bioinformatics, vol. 30, n.17, p. 386-392, 2014.

8. S. Chakar, E. Lebarbier, C. Lévy-Leduc, S. Robin (2014). R package: AR1seg (available on the CRAN) corresponds to the implementation of the robust approach for estimating change-points in the mean of an AR(1) Gaussian process by using the methodology described in the paper that we wrote arXiv 1403.1958

9. B. Benmammar, C. Lévy-Leduc, F. Roueff (2008). TopRank software (developped in C for detecting and localizing network anomalies), registered at the “Agence pour la Protection des Programmes”, IDDN.FR.001.100004.000.S.P.2008.000.20700, in 2008.

Publications