Aurélie Fischer

Assistant Professor
Laboratoire de Probabilités, Statistique et Modélisation
Université de Paris

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 -at- univ-paris-diderot.fr

Version française de cette page.

My CV.

• Since sept. 2012 : Assistant Professor at LPSM, Université Paris Diderot, in the statistics team.
• 2011/2012 : Temporary teaching and research position at MAP5 and IUT Paris Descartes.
• 2008/2011 : PhD student and teaching assistant at LSTA, Université Pierre et Marie Curie.

PhD thesis, Advisor Gérard Biau, Defense June 2011.

Mémoire d'Habilitation à Diriger des Recherches, 2021.

Supervised and unsupervised statistical learning :

• Quantization, clustering
• Bregman divergences
• Principal curves
• High dimension
• Model selection
• Aggregation
• Applications in biology, wind energy, linguistics

• Fischer, A. (2010). 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, Statistics and Probability Letters, Vol. 81, p. 1771–1781.
• Biau, G. & Fischer, A. (2012). Parameter selection for principal curves, IEEE Transactions on Information Theory, Vol. 58, p. 1924-1939.
• Auder, B. & Fischer, A. (2012). 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 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). Comparison of the spatial organization in colorectal tumors using second-order statistics and functional ANOVA, ISPA 2013.
• Fischer, A. (2014). 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). 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, Journal de la Société Française de Statistique, Vol. 156(1), p. 51-75.
• Biau, G., Fischer, A., Guedj, B. and Malley, J. (2015). COBRA: A combined regression strategy, Journal of Multivariate Analysis, Vol. 146, p. 18-28.
• Fischer, A., Montuelle, L., Mougeot, M. and Picard, D. (2017). 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). 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). Aggregation using input-output trade-off, Journal of Statistical Planning and Inference, Vol. 200, p. 1-19.
• Delattre, S. & Fischer, A. (2020). On principal curves with a length constraint, Annales de l'Institut Henri Poincaré, Vol. 56, p. 2108-2140.
• Fischer, A. & Picard, D. (2020). On change-point estimation under Sobolev sparsity, Electronic Journal of Statistics, Vol. 14, p. 1648-1689.
• Brécheteau, C., Fischer, A. & Levrard, C. (2020). Robust Bregman Clustering, The Annals of Statistics.
• Goutham, N., Alonzo, B., Dupré, A., Plougonven, R., Doctors, R., Liao, L., Mougeot, M., Fischer, A. and Drobinski, P. (2021). 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). 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). 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. (2022). Estimation via length-constrained generalized empirical principal curves under small noise.

• Introduction to machine learning (M2).

• Statistical learning (M2).