Research
Research Interest:
Mathilde Mougeot's research activity is motivated by questions related to concrete applications stemming from collaborative projects with the socio-economic world. Her research focuses on scientific issues related to predictive models in various contexts, such as those of high dimensionality, structured data (structure at the variable level or at the observation level), data representation (feature computation), model aggregation, data frugality by model transfer (adaptation of a pre-calibrated model to a new task) or by hybrid models (integration of knowledge from physics into models).
key words: Data Science, Machine learning, Data Mining, Non parametric Statistics.
HDR, thesis manuscript:
HDR (2015) Contributions to Statistics and Data Science for industrial applications.
From neural networks to Sparse linear models.
Doctorat (1992) Méthodes connexionnistes appliquées à la compression d'images et à l'auto-organisation
du système visuel des mammifères. Université Paris XI-Orsay, Paris-Saclay.
Editorial work:
Special issue on artificial intelligence and climate change. IEEE Technology and Society Magazine, vol 39, issue 2, 2020.
Renewable Energy: Forecasting and Risk Management. Springer, 2017, ISBN 978-3-319-99052-1.
Publications:
Jaber, E., Chabridon, V., Remy, E., Baudin, M., Lucor, D., Mougeot, M., & Iooss, B. (2024). Sensitivity Analyses of a Multi-Physics Long-Term Clogging Model For Steam Generators. arXiv
Jaber, Edgar, et al. (2024) Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees." arXiv
Oubari, F., Meunier, R., Décatoire, R., & Mougeot, M. (2023). A Meta-Generation framework for Industrial System Generation. arXiv.
de Mathelin, A., Deheeger, F., Mougeot, M., & Vayatis, N. (2023). Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification. arXiv.
Quintana, G. I., Li, Z., Vancamberg, L., Mougeot, M., Desolneux, A., & Muller, S. (2023) Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification, Bioengineering, 10(5) (link).
Garin, M., De Mathelin, A., Mougeot, M., & Vayatis, N. (2023). Personalized One-Shot Collaborative Learning. In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 114-121). IEEE (link).
Nguyen, Khoa.T.N, Dairay, T., Meunier, R., Mougeot, M. (2023) Fixed-Budget Online Adaptive Learning for Physics-Informed Neural Networks. Towards Parameterized Problem Inference, Lecture Notes in Computer Science, Springer volume 14073 (link).
Truong, C., Atiq, M., Minvielle, L., Serra, R., Mougeot, M., & Vayatis, N. (2023). A Data Set for Fall Detection with Smart Floor Sensors. Image Processing On Line, 13, 183-197. (link).
de Mathelin, A., Deheeger, F., Mougeot, M., Vayatis, N. (2023) From Theoretical to Practical Transfer Learning: the ADAPT library, Federated and Transfer Learning Springer book. (link).
Nguyen, Khoa.T.N, Dairay, T., Meunier, R., Mougeot, M. (2022) Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process, Engineering Applications of Artificial Intelligence, vol. 141 (arxiv).
Kluth, G., Ripoll, J.-F., Haas, S., Fisher, A. Mougeot, M. and Camporeale (2022) Machine Learning Methods Applied to the Global Modeling of Event-Driven Pitch Angle Diffusion Coefficients During High-Speed Streams, Frontiers in Physics (link).
de Mathelin, A., Deheeger, F., Mougeot, M., Vayatis, N. (2022) Fast and Accurate Importance Weighting for Correcting Sample Bias, European Conference on Machine Learning, ECML PKDD 2022.
Merida, A., Kalogeratos, A., Mougeot, M. (2022) To tree or not to tree? Assessing the impact of smoothing the decision boundaries. Int. Conf. on Artificial Neural Networks (ICANN 22).
de Mathelin, A., Deheeger, F., Mougeot, M., Vayatis, N. (2022) Discrepancy-Based Active Learning for Domain Adaptation, International Conference on Learning Representations, ICLR 2022. (hal).
Minvielle, L., Atiq, M., Truong, C., Serra, R., Mougeot, M., Vayatis, N. (2021) A Data Set for Fall Detection with Smart Floor Sensors , submitted.
Atiq, M., Peignier, S. and Mougeot, M. (2021) Constrained prediction time random forests using equivalent trees and genetic programming : application to fall detection model embedding. IEEE 33st International Conference on Tools with Artificial Intelligence (ICTAI).
de Mathelin, A. Richard, G, Deheeger, F. Mougeot, M. and Vayatis, N. (2021) Adversarial Weighting for Domain Adaptation in Regression. IEEE 33st International Conference on Tools with Artificial Intelligence (ICTAI). Hal.
Dib, A., Truong, C., Oudre, L., Mougeot, M., Vayatis, N. (2021) Bayesian Feature Discovery for predictive Maintenance. 29th European Signal Processing Conference (EUSIPCO). DOI: 10.23919/EUSIPCO54536.2021.9616188. Hal.
de Mathelin, A., Deheeger, F. Richard, G. Mougeot, M. and Vayatis, N. (2021) ADAPT: Awesome Domain Adaptation Python Toolbox. Arxiv.
Laurent, V., Vo Van, O., Mougeot, M. and Ghidaglia, J.-M. (2021) Machine Learning based prediction of fatigue events in railway rails. 15th World Congress on Engineering Asset Management (WCEAM) proceedings, DOI: 10.1007/978-3-030-96794-9_42.
Fischer, A., Has S., Mougeot, M. (2021) KFC: A clusterwise supervised learning procedure based on the aggregation of distances, Journal of Statistical Computation and Simulation, DOI: 10.1080/00949655.2021.1891539 , ArXiv.
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 outputs of a Numerical Weather Prediction model, Boundary-Layer meteorology, DOI: 10.1007/s10546-020-00586-x.
Publications before 2020...
Luers, A., Langlois, L., Mougeot, M., Kharaghani, S. & Luccioni A. (2020) Sustainability in the Digital Age. IEEE Technology and Society Magazine, vol. 39, 2.
Saillant T., Weill J.C., Mougeot, M. (2020) Predicting Job Power consumption based on RJMS submission data in HPC systems. Springer’s Lecture Notes in Computer Science 12151, ISC High Performance. Hal.
Richard, G., de Mathelin, A., Hebrail, G., Mougeot, M., Vayatis, N. (2020) Unsupervised Multi-Source Domain Adaptation for Regression. ECML PKDD 2020. Hal
Fikri, H., Guegin, M., Laurent, V., Mougeot, M., Vayatis, N., Yang, C. Ghidaglia, J.M. (2020) Hybrid Modeling for Lifetime Prediction, Engineering Assets and Public Infrastructures in the Age of Digitalization, Springer. link.
Merida, A., Kalogeratos, A., Mougeot, M. (2020) Model family selection for classification using Neural Decision Trees. (Archiv).
Minvielle L., Atiq M., Peignier S., Mougeot M. (2019) Transfer Learning on Decision Tree with class Imbalance. IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI).
Fischer, A. Mougeot, M. (2019) Aggregation using input-output trade-off. Journal of Statistical Planning and Inference. Volume 200, pp. 1-19.
Richard G., Hébrail G., Mougeot M., Vayatis N. (2019) DenseNets for Time Series Classification: towards automation of time series pre-processing with CNNs. KDD'2019 - Workshop on Mining and Learning from Time Series.
Mougeot, M, Picard, D, Lefieux, V, Marchand, M. (2018) Homogeneous climate regions using learning algorithms. Springer book in Renewable Energy: Forecasting and Risk Management. ISBN 978-3-319-99052-1.
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. Springer book in Renewable Energy: Forecasting and Risk Management. ISBN 978-3-319-99052-1.
Laurent, V., Mougeot, M., Yang, C., Hafid, F. Ghidaglia, J.M., N. Vayatis (2018) Statistical Learning to assess overhead Line Lifespan. Proceedings of Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion. MedPower2018, Dubronick, Croatia, November 2018.
Fischer A., Montuelle L., Mougeot M., Picard D. (2017) Statistical learning for wind power: a modeling and stability study towards forecasting. Wind Energy.
Minvielle L., Atiq M., Serra R., Mougeot M., Vayatis N. (2017) Fall detection using smart floor sensor and supervised learning. IEEE Engineering in Medicine and Biologie Conference.
Mathilde Mougeot (2017) Forecasting the French National Electricity Consumption: from Sparse Models to Aggregated Forecasts. Model choice and model aggregation. SFDS Technip.
Abdel-Sayed M., Duclos D., Faÿ G., Lacaille J., Mougeot M. (2016) Nmf-decomposition for anomaly detection applied to vibration analysis. The International Journal of Condition Monitoring, the British Institute of Non-Destructive Testing 6(3).
Abdel-Sayed M., Duclos D., Faÿ G., Lacaille J., Mougeot M. (2016) Dictionary comparison for anomaly detection on aircraft engine spectrograms. Machine Learning and Data Mining in Pattern Recognition, 9729, Lecture Notes in computer science book series.
Mougeot M., Picard D., Lefieux V., Maillard-Teyssier L. (2015) Modeling and Stochastic Learning for Forecasting in High Dimension. Springer Lecture Notes in Statistics, p 161-182.
Mougeot M., Picard D., Tribouley K. (2014) LOL selection in high dimension. Computational Statistics and Data Analysis, p 743-757, archiv.
Dematteo A., Vayatis N., Mougeot M., Clémençon S. (2014) Risk assessment in Sloshing: How to deal with Multidimentional Datasets. International Society of Offshore and Polar Engineers, (ISOPE), june.
Mougeot M., Picard D., Tribouley K. (2013) Sparse approximation and fit of intraday load curves in a high dimensional framework. Advances in Adaptive Data Analysis, p1-23.
Mougeot M., Picard D., Tribouley K. (2013) Grouping Strategies and Thresholding for High Dimensional Linear Models, rejoinder. Journal of Statistical Planning and Inference with discussion, 143, p 1457-1465,
Mougeot M., Picard D. Tribouley K. (2013) Grouping Strategies and Thresholding for High Dimensional Linear Models. Journal of Statistical Planning and Inference with discussion, 143, p 1417-1438, archiv,
Fraizier E, Delattre S, Mougeot M, Faure Ph., Roy G., Poggi F. (2012) A new probabilistic tool for the determination and optimization of multiphase Equation Of State parameters: application to tin. DYMAT 2012- 10th International Conference on the Mechanical and Physical behavious of Materials under Dynamic Loading. Freiburg, Germany, September 2nd-7th, 2012. ISBN:978-2-7598-0757-4.
Mougeot M., Picard P. et Tribouley K. (2012) Learning Out of Leaders: Regression for high dimension. J. R. Stat. Soc. Ser. B Stat. Methodol. vol 74, pp. 1-39.
Clémençon S., Dematteo A., Mougeot M., Vayatis N., (2012) Sloshing in the shipping industry: risk modelling through multivariate heavy-tail analysis.
Mougeot M. and Azencott R. (2011) Traffic safety: non-linear causation analysis. Safety And Security Engineering.
Mougeot M. and Tribouley K. (2010) Procedure of test to compare tail indices. Annals of the Institute of Statistical Mathematics: Volume 62, Issue 2 (2010), Page 383.
Stirnemann J.J., Mougeot M., Proulx F., Nasr B., Essaoui M., Fouron J.C., Ville Y. (2010) Profiling fetal cardiac function in twin to twin transfusion syndrome. Ultrasound Obstet Gynecol 2010; 35: 19 to 27.
Kerkyacharian G., Mougeot M., Picard D. and Tribouley K. (2009) LOL: Learning out Leaders. Ronald A. DeVore, Angela Kunoth (Eds.), Multiscale, Nonlinear and Adaptive Approximation, Springer.
Mougeot M. and Azencott R.(2008) Information theoretical methods dedicated to accidents analysis for GIDAS database. ESAR 08. Hannovre.
Butucea C, Mougeot M, and Tribouley K. (2007) Functionnal approach for excess mass estimation in the density model. Electronics journal of statistics. Vol 1, 2007.
Cadet O, Harper C. and Mougeot M. (2005) Monitoring Energy Performance of Compressors with an innovative auto-adaptive approach. ISA 2005, Chicago.
Mougeot M. (1997) Synchronization and Oscillations in the visual cortex: a stochastic model using a spike memory term. European Symposium on Artificial Neural Networks, Bruges April 1997.
Mougeot M. (1996) Biological Model of synchronization in the visual cortex.World Congress on Neural Networks, San Diego 1996. (*)
Mougeot M. and Azencott R. (1991) Unsupervised learning for the visual cortex (layer IV): model and simulations. IJCNN 91. Internal Joint Conference on Neural Network.
Mougeot M, Azencott R.and Angéniol, B (1991) Image compression with batckpropagation: improvement of the visual restoration using different cost functions. Neural Networks, Vol. 4, pp. 467-476.
Mougeot M. and Barrow R. (1990) From Static to dynamic image compression.Proceedings of International Neural Network Conference -ICNN-, July 9-13, Paris 1990. International Conference on Neural Networks.
Mougeot M, Azencott R. and Angéniol, B (1990) A study of image compression with backpropagation. NATO ASI series vol. F68, 333-336.
Teaching Publications :
M. Mougeot M., G. Stoltz G., (2015) La statistique Connectée. Journal Statistique et Enseignement, SFDS.
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Mougeot M. (2007) Impact of new technologies for Teaching statistics. Institut International de Statistique, 22 -29 aout 2007, Lisbonne.
Carter L. and Mougeot M. (1998) Use of Excel in a first course in Statistics for Mathematical Students. ICATS-5, The first International Conference on Teaching Statistics, Singapore, June 1998.
Carter L. and Mougeot M. (1994) Simulations to illustrate results in theoretical statistics. Proceedings of four International Conference on teachings Statistics Marrakech, july 1994.
Carter L. and Mougeot M. (1993) Enseignement des statistiques assisté par ordinateur pour Economistes. Actes des XXV journées de statistiques de l' ASU, Vannes 24-28 Mai 1993.
European Projects reports:
Mougeot M. (2009) Self-learning modules for off-line optimisation of quality by adjustments of control parameters. Deliverable n°D2.3. Technical document INNOTEX european project n° 030312.
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Mougeot M. (2008) Detection of quality risks based on process sensors recordings. Deliverable n°D2.2. Technical document INNOTEX european project n° 030312.
Azencott R., Mougeot M. and Wang J. (2008) Auto adaptive elimination of degraded sensor inputs. Specifications and test results . ADHER Technical document, Deliverable Work package WP 3.4. FP6/Aeronautics project AST5-CT-2006-030907. Restricted.
Azencott R., Mougeot M. and Wang J. (2008) Automated Diagnosis for Helicopter Engines and Rotating parts. ADHER Technical document, Deliverable Work package WP 3.3. FP6/Aeronautics project AST5-CT-2006-030907. Restricted.
Azencott R., Kreiss J.P., Mougeot M., Pastor P., Pfeiffer M.,Siebert S., Zangmeister T. (2007) Analysis Methods for Accident Causation Studies. Project No. 027763 – TRACE.
Mougeot M. (2007) Root causes Diagnosis component. Deliverable n°D2.1. Technical document INNOTEX european project n° 030312.
Azencott R., Mougeot M. and Wang J. (2007) Impact Analysis of contextual variables on vibrations . ADHER Technical document, Deliverable Work package WP 3.2. FP6/Aeronautics project AST5-CT-2006-030907. Restricted.
Azencott R., Mougeot M. and Wang J. (2007) State of the art for off the shelf vibrations diagnosis softwares. ADHER Technical document, Deliverable Work package WP 3.1. FP6/Aeronautics project AST5-CT-2006-030907. Restricted.
Mougeot M. and Azencott R. (2007) Application to informations theoretic methods to GIDAS data base. Deliverable 3.3-2. Technical document TRACE European project n° 027763.
Mougeot M. and Azencott R. (2006) Information theoretic methods and algorithms for accident causation analysis. Deliverable 3.3-2. Technical document TRACE
Proceedings :...
Mougeot M. and Tribouley K. (2008) Goodness-of-fit independance test based on Excess Mass properties for copula. Proceedings Multimodality and related topics.
Mougeot M. Applications des estimateurs d' Excess Mass. Modélisation Statistiques des réseaux. Journées MAS de la SMAI. Rennes, 27-28 août 2008
Mougeot M. (2001) Applications des méthodes d analyse d influence sur la qualité dans l industrie des procédés. Colloque PROSETIA n°6 organisé par l INRA. 19-21 mars 2001.