====== Aurélie Fischer ====== {{aureliefischer1.jpg?172x230 |Aurélie Fischer}} Associate Professor\\ Laboratoire de Probabilités, Statistique et Modélisation\\ Université Paris Cité **Address :**\\ UFR de Mathématiques\\ Bâtiment Sophie Germain\\ 75205 Paris Cedex 13\\ France **Office :** 504 **Phone :** +33 1 57 27 91 10 **E-mail :** [[aurelie.fischer@u-paris.fr|aurelie.fischer -at- u-paris.fr]] \\ \\ [[index|Version française]] de cette page. \\ =====Curriculum Vitae===== My {{cv-en.pdf|CV}}. ==== Past and current positions ==== * Since sept. 2012 : Associate Professor at [[https://www.lpsm.paris/|LPSM]], [[http://www.u-paris.fr/|Université Paris Cité]], in the [[https://www.lpsm.paris/stats/|statistics team]]. * 2011/2012 : Temporary teaching and research position at [[http://w3.mi.parisdescartes.fr/map5/|MAP5]] and [[http://www.iut.parisdescartes.fr//|IUT]] Paris Descartes. * 2008/2011 : PhD student and teaching assistant at [[http://www.lsta.upmc.fr/|LSTA]], [[http://www.upmc.fr/|Université Pierre et Marie Curie]]. ==== Reports ==== {{theseAurelieFischer.pdf|PhD thesis}}, Advisor [[http://www.lsta.upmc.fr/biau.html|Gérard Biau]], Defense June 2011. [[https://1drv.ms/b/s!AgtZO8Y-afZgirJSfosJthECIs54rg|Mémoire d'Habilitation à Diriger des Recherches]], Defense June 2022. ===== Research ===== ==== Research interests ==== Supervised and unsupervised statistical learning : * Quantization, clustering * Bregman divergences * Principal curves * High dimension * Model selection * Aggregation * Applications in climate sciences and sociology ==== ANR project GeoDSIC ==== Link to the [[https://sites.google.com/view/projetanrgeodsic|project page]]. ==== Publications and preprints ==== * Fischer, A. (2010). {{Quantization and clustering with Bregman divergences.pdf|Quantization and clustering with Bregman divergences}}, //Journal of Multivariate Analysis//, Vol. 101, p. 2207-2221. * Fischer, A. (2011). {{On the number of groups in clustering.pdf|On the number of groups in clustering}}, //Statistics and Probability Letters//, Vol. 81, p. 1771–1781. * Biau, G. & Fischer, A. (2012). {{Parameter selection for principal curves.pdf|Parameter selection for principal curves}}, //IEEE Transactions on Information Theory//, Vol. 58, p. 1924-1939. * Auder, B. & Fischer, A. (2012). {{Projection-based curve clustering.pdf|Projection-based curve clustering}}, //Journal of Statistical Computation and Simulation//, Vol. 82, p. 1145-1168. * Fischer, A. (2013). {{Selecting the length of a principal curve.pdf|Selecting the length of a principal curve within a Gaussian Model}}, //Electronic Journal of Statistics//, Vol. 7, p. 342-363. * Alsheh Ali, M., Seguin., J, Fischer, A., Mignet, N., Wendling, L. and Hurtut, T. (2013). {{ispa2013.pdf|Comparison of the spatial organization in colorectal tumors using second-order statistics and functional ANOVA}}, //ISPA 2013//. * Fischer, A. (2014). {{ClusteringCourbesPrincipales.pdf|Deux méthodes d’apprentissage non supervisé : synthèse sur la méthode des centres mobiles et présentation des courbes principales}}, //Journal de la Société Française de Statistique//, Vol. 155(2), p. 2-35. * Dedecker, J., Fischer, A. and Michel, B. (2015). {{wasserstein.pdf|Improved rates for Wasserstein deconvolution with ordinary smooth error in dimension one}}, //Electronic Journal of Statistics//, Vol. 9, p. 234-265. * Fischer, A. (2015). {{On two extensions of the vector quantization scheme.pdf|On two extensions of the vector quantization scheme}}, //Journal de la Société Française de Statistique//, Vol. 156(1), p. 51-75. * Biau, G., Fischer, A., Guedj, B. and Malley, J. (2015). {{A nonlinear aggregation strategy.pdf|COBRA: A combined regression strategy}}, //Journal of Multivariate Analysis//, Vol. 146, p. 18-28. * Fischer, A., Montuelle, L., Mougeot, M. and Picard, D. (2017). {{RealTimeWindPowerForecast.pdf|Statistical learning for wind power : a modeling and stability study towards forecasting}}, //Wind Energy//, Vol. 20, p. 2037–2047. * Alonzo B., Plougonven R., Mougeot M., Fischer, A. Dupre, A. and Drobinski, P. (2018). {{Downscaling.pdf|From Numerical Weather Prediction outputs to accurate local surface Wind speed : statistical modelling and forecasts}}, In //Renewable Energy : Forecasting and Risk Management//, Springer Proceedings in Mathematics & Statistics. * Fischer, A. & Mougeot, M. (2019). {{mixcobra-art.pdf|Aggregation using input-output trade-off}}, //Journal of Statistical Planning and Inference//, Vol. 200, p. 1-19. * Delattre, S. & Fischer, A. (2020). {{courbes-art.pdf|On principal curves with a length constraint}}, //Annales de l'Institut Henri Poincaré//, Vol. 56, p. 2108-2140. * Fischer, A. & Picard, D. (2020). {{smooth-clustering-rev2.pdf|On change-point estimation under Sobolev sparsity}}, //Electronic Journal of Statistics//, Vol. 14, p. 1648-1689. * Brécheteau, C., Fischer, A. & Levrard, C. (2020). {{RobustBregmanClustering.pdf|Robust Bregman Clustering}}, //The Annals of Statistics//, Vol. 49, p. 1679-1701. * Goutham, N., Alonzo, B., Dupré, A., Plougonven, R., Doctors, R., Liao, L., Mougeot, M., Fischer, A. and Drobinski, P. (2021). {{machinelearningmethodswind.pdf|Using machine learning methods to improve surface wind from the outputs of a Numerical Weather Prediction model}}, //Boundary-Layer Meteorology//, Vol. 179, p. 133-161. * Fischer, A., Has, S. and Mougeot, M. (2021). {{kfc.pdf|A clusterwise supervised learning procedure based on aggregation of distances}}, //Journal of Statistical Computation and Simulation//, Vol. 91, p. 2307-2327. * Kluth, G., Ripoll, J.-F., Has, S., Fischer,A., Mougeot, M. and Camporeale, E. (2022). {{Frontiers.pdf|Machine Learning Methods Applied to the Global Modeling of Event-Driven Pitch Angle Diffusion Coefficients During High-Speed Streams}}, //Frontiers in Physics, Space Physics//. * Delattre, S. & Fischer, A. (2023). {{courbes-estim-new.pdf|Estimation via length-constrained generalized empirical principal curves under small noise}}. * Has, S., Plougonven, R., Fischer, A., Rani, R., Lott, F., Hertzog, A., Podglajen, A. and Corcos, M. (2023). Reconstructing balloon-observed gravity wave momentum fluxes using machine learning and input from ERA5. * Delattre, S. & Fischer, A. (2024). Convergence rates in curve estimation. ===== Teaching ===== ====2023/2024==== == First Semester == * Introduction to machine learning (M2). * Statistical learning (M2). == Second Semester == * AI & Society (M1)