M. Donald, D. Fyfe, and C. , The kernel self organising map, Proceedings of 4th International Conference on knowledge-based intelligence engineering systems and applied technologies, pp.317-320, 2000.

K. Lau, H. Yin, and S. Hubbard, Kernel self-organising maps for classification, Neurocomputing, vol.69, pp.2033-2040, 2006.

R. Boulet, B. Jouve, F. Rossi, and N. Villa, Batch kernel SOM and related laplacian methods for social network analysis, Neurocomputing, vol.71, pp.1257-1273, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00202339

B. Hammer and A. Hasenfuss, Topographic mapping of large dissimilarity data sets, Neural Computation, vol.22, issue.9, pp.2229-2284, 2010.

M. Olteanu and N. Villa-vialaneix, On-line relational and multiple relational SOM. Neurocomputing (2013) Forthcoming
URL : https://hal.archives-ouvertes.fr/hal-01063831

, Academic positions, 2007.

, Applied Mathematics at Université Paris 1 Panthéon Sorbonne and member of the SAMM research team. 2017 -2019 Sabbatical leave and research position at

. Post, . Miaj, J. Inra, and J. En, Teaching and research assistant (ATER) at Université Paris 1 Panthéon Sorbonne. 2002 -2005 Teaching assistant at Université Paris 5 in the Statistics department Research associate at Université Paris 1 Panthéon Sorbonne, SAMOS team, PhD in Applied Mathematics, 2005.

M. , Applied Mathematics, Université Paris 1 -Université Paris 7, with Honors (B)

B. , Applied Mathematics, University of Bucharest, with Honors (TB)

, Bachelor's degree in Mathematics and Physics, with Honors (TB)

, Co-supervised with Marie Chavent, and Jérôme Lacaille (Safran Aircraft Engines). This PhD is funded by Safran Aircraft Engines, 2019.

, Co-supervised with Sira Allende Alonso, Dafne Garcia de Armas, 2018.

C. Laroche, Co-supervised with Fabrice Rossi, 2018.

C. Faure, Co-supervised with Jean-Marc Bardet (SAMM, Université Paris 1), and Jérôme Lacaille, 2015.

, This PhD was funded by Safran Aircraft Engines. Thesis defended in September 2018, on Change-point detection and identification of causes in turbojet engine operation during flights and test benches

B. Postdoc's, -Co-supervision with Nathalie Vialaneix (MIA, INRA, Toulouse) of J. Boelaert's postdoc March, 2013.

C. Interns, , 2018.

, Co-supervision with Elisabeta Vergu (MaIAGE, INRA) of Kevin Pame's internship (Master of Statistics, Self-organizing clustering in a large dynamical network

, Co-supervision with Nathalie Vialaneix (MIA, INRA, Toulouse) of Laura Bendhaiba's internship (GIS Engineer, PolyTech'Lille)

, Hidden Markov models for integer-valued times series, 2011.

J. , Analyzing career paths with self-organizing maps for categorical time series, Supervision of Sébastien Massoni's internship, 2008.

D. Phd-jurys, , 2017.

. Examiner, PhD defended by I. Gorynin (Université Paris Saclay), co-supervised by W. Pieczynski and E. Monfrini, Bayesian state estimation in partially observed Markov processes, 2014.

, Mathematical modeling of dynamical systems in epidemiology

, Aalto University School of Science and Technology, Finland), supervised by A. Lendasse, Assessment of spatio-temporal data bases: time series prediction and missing value problem, 2010.

E. Other, Applied mathematics in economics and finance) and second year (M2 TIDE, Data-science for business decision and analytics) master thesis supervision. On average, two dissertations per year. 2007 -Apprentice supervision in the master program M2 TIDE, 2006.

, Campus France. Co-coordinator with Sira Allende Alonso, Fundings and grants 2019-2020 Socio-spatial dynamics in large cities: from new mathematical models to new multidisciplinary perspectives. Project funded by Partenariats Hubert Curien (PHC)

, Variable importance and variable selection for high-dimensional clustering in an industrial context Project funded by Safran Aircraft Engines. Co-coordinator with Marie Chavent (INRIA, pp.2019-2022

, Temporality as a multidisciplinary approach Project funded by Université Paris 1 Panthéon Sorbonne. Co-coordinator with Stéphane Lamassé, 2018.

, segregation(s), integration(s) Project funded by Université Panthéon Sorbonne. Co-coordinator with Julien Randon-Furling, 2018.

, Feasibility study: phytopharmacovigilance (PPV) data mining. Creation of a tool for detecting emersions for the PPV. Project funded by ANSES. Co-coordinator with Fabrice Rossi, pp.2018-2021

, CADENCE: spread of epidemic processes on dynamical networks of animal movements with application to cattle in France ANR grant, Coordinator : Elisabeta Vergu, pp.2017-2021

, Change-point detection and identification of causes in turbojet engine operation during flights and test benches

, Temporality as a multidisciplinary approach Project funded by Université Paris 1 Panthéon Sorbonne. Co-coordinator with Stéphane Lamassé, pp.2013-2014

, LaCOSA II (International Conference on Sequence Analysis and Related Methods), WSOM+ (Workshop on Self Organizing Maps), ESANN (European Symposium on Artificial Neural Networks), IWANN (International Work Conference on Artificial Neural Networks), ICOR (International Conference on Operations Research), MASHS (Modèles et apprentissage en sciences humaines et sociales)

, Temporality: perceptions and analysis, a series of six interdisciplinary seminars, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+), Co-organized with J-C. Lamirel (LORIA, Nancy) and M. Cottrell (Université Paris 1 Panthéon Sorbonne, 2014.

, Modeling and statistical learning in humanities and social sciences, Co-organized with N. Vialaneix (MIA, INRA) and M. Cottrell, 2012.

, Modeling and data analysis in biomedical systems, Special session at the International Workshop on Artificial Neural Networks (IWANN, Statistical learning, Special session for the "Journées MAS de la SMAI" meeting, 2008.

, Machine learning. Lecture for graduate level (8h). Spring school Interdisciplinary College, 2009.

, Survival Analysis, Time series, for master students in Economics, Applied Mathematics, Data-science, vol.5

, Quantitative methods in humanities and social sciences (6h per year), 2011.

, Lecture for PhD students in Economics, Université Paris 1. 2007-2016 Training in mathematics for Economics teaching-degree candidates (CAPES SES, Agrégation SES ). 54h per year, 2008.

, Undergraduate level 2003-2016 On average 60 hours per year. Lectures and tutorials in Statistics, Probability theory, Linear, 2019.

, Time series, for students in Economics, Applied Mathematics, Biology. Université Paris, vol.5

, Administrative responsibilities and tasks 2018-2020 Elected member of the University senate, 2016.

, Member of the board of the apprentice training center CFA Formasup

, Université Paris 1 Panthéon Sorbonne. Sandwich program. 2012-2016 Elected member of the Education Committee of the University (Université Paris 1 Pantéon Sorbonne) and member of its permanent commission, pp.2013-2016, 2010.

, Elected member of the research committee of the Mathematics and Computer Science department

, Member of several selection committees for Associate Professorship, pp.3-2015, 2010.

M. Olteanu, A. Hazan, M. Cottrell, and J. Randon-furling, Multidimensional urban segregation: towards a neural network measure, Neural Computing and Applications, vol.31, issue.6, pp.1-13, 2019.

. M. Mo2, J. Olteanu, W. A. Randon-furling, and . Clark, Segregation through the multiscalar lens, Proceedings of the National Academy of Sciences, vol.116, issue.25, pp.11250-12254, 2019.

. J. Mo3, M. Randon-furling, A. Olteanu, and . Lucquiaud, From urban segregation to spatial structure detection, 2018.

. M. Mo4, M. Cottrell, F. Olteanu, N. Rossi, and . Villa-vialaneix, Self-organizing maps, theory and applications, Revista Investigacion Operacional, vol.39, issue.1, pp.1-22, 2018.

. J. Mo5, M. Alerini, J. Olteanu, and . Ridgway, Markov and the Duchy of Savoy: segmenting a century with regimeswitching models, Journal de la Société Française de Statistique, vol.158, issue.2, pp.89-117, 2017.

. J. Mo6, M. Mariette, N. Olteanu, and . Villa-vialaneix, Efficient interpretable variants of online SOM for large dissimilarity data, Neurocomputing, vol.225, pp.31-48, 2017.

. M. Mo7, N. Olteanu, and . Villa-vialaneix, Using SOMbrero for clustering and visualizing graphs, Journal de la Société Française de Statistique, vol.156, issue.3, pp.95-119, 2015.

. M. Mo8, N. Olteanu, and . Villa-vialaneix, On-line relational and multiple relational SOM, vol.147, pp.15-30, 2015.

. M. Mo9, M. Cottrell, F. Olteanu, J. Rossi, N. Rynkiewicz et al., Neural networks for complex data, vol.26, pp.373-380, 2012.

. M. Mo10, J. Olteanu, and . Rynkiewicz, Asymptotic properties of autoregressive regime-switching models, ESAIM P&S, vol.16, pp.25-47, 2012.

. M. Mo11, J. Olteanu, and . Rynkiewicz, Asymptotic properties of mixture-of-experts models, Neurocomputing, vol.74, issue.9, pp.1444-1449, 2011.

. M. Mo12, J. Olteanu, and . Rynkiewicz, Estimating the number of components in a mixture of multilayer perceptrons, Neurocomputing, vol.71, issue.7-9, pp.1321-1329, 2008.

M. Olteanu, V. Nicolas, B. Schaeffer, C. Denys, A. Missoup et al., Nonlinear projection methods for visualizing Barcode data and application on two data sets, Molecular Ecology Resources, vol.13, issue.6, pp.976-990, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01519688

. F. Mo14, K. Austerlitz, M. Bleakley, O. Olteanu, C. David et al., DNA barcode analysis : comparing phylogenetic and statistical classification methods, BMC Bioinformatics, vol.10, issue.14, 2009.

. M. Mo15, G. Boyer-xambeu, P. Deleplace, L. Gaubert, M. Gillard et al., The periodization of the international bimetallism, Revista Investigacion Operacional, vol.28, issue.2, pp.143-156, 2007.

. M. Mo16 and . Olteanu, A descriptive method to evaluate the number of regimes in a switching autoregressive model, Neural Networks, vol.19, pp.963-972, 2006.

. B. Mo17, M. Maillet, J. Olteanu, and . Rynkiewicz, Caractérisation des crises financièresà l'aide de modèles hybrides (HMC-MLP), Revue d'économie politique, vol.4, pp.489-506, 2004.

B. Editorials,

M. Cottrell, M. Olteanu, J. Rouchier, and N. Villa-vialaneix, Editorial of the special issue of RNTI -MASHS 2011/2012 : Modèles et Apprentissage en Sciences Humaines et Sociales. Revue Des Nouvelles Technologies De l'Information, SHS-1, pp.97-110, 2012.

C. J. Book-chapters-mo19, L. Gravier, D. Nahassia, N. Michel, M. Verdier et al., Processus -Trajectoire, Les mots-clefs des systèmes de

. M. Mo20, J. Olteanu, and . Alerini, Quelques réflexions sur la périodisation en histoire, Dans les dédales du web: Historiens en territoires numériques, Editions de la Sorbonne, pp.57-85, 2019.

. E. Mo21, M. Garcia-garaluz, G. Atencia, M. Joya, and . Olteanu, Modeling dengue epidemics with autoregressive switching Markov models, Bioinspired systems: Computational and Ambient Intelligence, pp.886-892, 2009.

-. M. Mo22, G. Boyer-xambeu, P. Deleplace, L. Gaubert, M. Gillard et al., Kolonnen maps and time-series algorithms: a clear convergence, Encyclopedia of Artificial Intelligence, 2008.

-. M. Mo23, G. Boyer-xambeu, P. Deleplace, L. Gaubert, M. Gillard et al., Mixing Kohonen algorithm, Markov switching model and detection of multiple change-points : an application to monetary history, Computational and Ambient Intelligence

, International conferences with peer-reviewed proceedings

M. Olteanu and J. Lamirel, When clustering the multiscalar fingerprint of the city reveals its segregation patterns, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (Proceedings of WSOM+ 2019, pp.140-149, 2019.

. M. Mo25, J. Olteanu, W. Randon-furling, and . Clark, Spatial analysis in high resolution geo-data, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.559-564, 2019.

. J. Mo26, M. Alerini, M. Cottrell, and . Olteanu, Hidden Markov models for time series of continuous proportions with excess zeros, Advances in Computational Intelligence. 14th International Work-Conference on Artificial Neural Networks, IWANN 2017. Proceedings, Part II, pp.198-209, 2017.

. M. Mo27, M. Cottrell, J. Olteanu, A. Randon-furling, and . Hazan, Multidimensional urban segregation: an exploratory case study, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM 2017+), 2017.

. J. Mo28, M. Mariette, F. Olteanu, N. Rossi, and . Villa-vialaneix, Accelerating stochastic kernel SOM, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.269-274, 2017.

. M. Mo29, N. Olteanu, and . Villa-vialaneix, Sparse online self-organizing maps for large relational data, Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016, vol.428, pp.27-37, 2016.

. N. Mo30, M. Bourgeois, S. Cottrell, M. Lamassé, and . Olteanu, Search for meaning through the study of cooccurrences in texts, Advances in Computational Intelligence (Proceedings of IWANN 2015), pp.578-591, 2015.

. L. Mo31, J. Bendhaiba, M. Boelaert, N. Olteanu, and . Villa-vialaneix, SOMbrero: an R package for numeric and non-numeric self-organizing maps, Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014), pp.219-228, 2014.

. J. Mo32, M. Mariette, N. Olteanu, and . Villa-vialaneix, Bagged kernel SOM, Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014), pp.45-54, 2014.

. C. Mo33, M. Cierco-ayrolles, N. Olteanu, and . Villa-vialaneix, Multiple kernel self-organizing maps, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.83-88, 2013.

. S. Mo34, M. Massoni, N. Olteanu, and . Villa-vialaneix, Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study, Advances in Computational Intelligence (Proceedings of IWANN 2013), pp.69-79, 2013.

. M. Mo35, M. Cottrell, N. Olteanu, and . Villa-vialaneix, Online relational SOM for dissimilarity data, Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2012, pp.13-22, 2012.

. M. Mo36, J. Olteanu, and . Ridgway, Hidden Markov models for time series of counts with excess zeros, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.133-138, 2012.

. M. Mo37, J. Olteanu, and . Rynkiewicz, Asymptotic properties of mixture-of-experts models, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.207-212, 2010.

. S. Mo38, M. Massoni, P. Olteanu, and . Rousset, Career-path analysis using optimal matching and self-organizing maps, Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2009), pp.154-162, 2009.

. S. Mo39, M. Massoni, P. Olteanu, and . Rousset, Analyse des trajectoires d'insertion professionnelle avec un algorithme de Kohonen pour données catégorielles, Proceedings of MASHS 2009 (Modelling and leArning in Social and Human Sciences), 2009.

M. Olteanu and J. Rynkiewicz, Estimating the number of components of a mixture autoregressive model, Proceedings of the European Symposium on Time Series Prediction, pp.143-154, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00655590

. M. Mo41, J. Olteanu, and . Rynkiewicz, Estimating the number of components in a mixture of multilayer perceptrons, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.403-408, 2007.

M. Cottrell, C. Faure, J. Lacaille, and M. Olteanu, Anomaly detection for bivariate signals, Advances in Computational Intelligence (Proceedings of IWANN 2019), pp.162-173, 2019.

. M. Mo43, C. Cottrell, J. Faure, M. Lacaille, and . Olteanu, Detection of abnormal flights using fickle instances in SOM maps, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (Proceedings of WSOM+ 2019, pp.120-129, 2019.

. C. Mo44, M. Faure, J. Olteanu, J. Bardet, and . Lacaille, Using self-organizing maps for clustering and labelling aircraft engine data phases, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM 2017+), 2017.

. J. Mo45, C. Bardet, J. Faure, M. Lacaille, and . Olteanu, Comparison of three algorithms for parametric changepoint detection, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.89-94, 2016.

. S. Mo46, M. Massoni, P. Olteanu, and . Rousset, Career-path analysis using drifting Markov models (DMM) and self-organizing maps, Proceedings of MASHS 2010 (Modelling and leArning in Social and Human Sciences), pp.171-179, 2010.

. Mo47, . Ch, S. Bouveyron, M. Girard, and . Olteanu, Supervised classification of categorical data with uncertain labels for DNA barcoding, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.22-34, 2009.

. M. Mo48 and . Olteanu, Revisiting linear and nonlinear methodologies for time series prediction: application to ESTSP'08 competition data, Proceedings of the European Symposium on Time Series Prediction (ESTSP, pp.139-148, 2008.

. M. Mo49 and . Olteanu, A descriptive method to evaluate the number of regimes in a switching autoregressive model, Proceedings of the International Workshop on Self-Organizing Maps (WSOM 2005), pp.259-266, 2005.

. B. Mo50, M. Maillet, J. Olteanu, and . Rynkiewicz, Nonlinear analysis of shocks when financial markets are subject to changes in regime, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.87-92, 2004.

E. , Invited conferences The speaker is in bold font

. S. Mo51, M. Lamassé, and . Olteanu, Detecting the evolution phases of a text production, 12th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2019.

. Mo52, M. Alerini, and . Olteanu, Markov and the Dukes of Savoy: A temporal analysis of the Piedmontese-Savoyard legislation, 12th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2019.

M. Olteanu, N. Vialaneix, J. Marietta, G. Beaunée, K. Pame et al., Clustering complex data with kernel and relational SOM. An application to cattle trading networks, The 22nd Conference of the Romanian Society of Probability and Statistics, 2019.

. J. Mo54, M. Randon-furling, W. Olteanu, and . Clark, The distorted city -capturing the complexity of perceived segregation, ECSR Workshop, 2019.

M. Olteanu and J. Randon-furling, Assessing segregation in complex networks through a multi-focal approach, 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2018.

M. Olteanu, K. Pame, G. Beaunée, C. Bidot, and E. Vergu, Clustering and visualizing large cattle-trading networks using self-organizing maps, 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2018.

M. Olteanu, J. Randon-furling, and W. Clark, Migrations and segregation in European cities, D4I Joint Research Center -European Commission Workshop, 2018.

M. Olteanu and P. Rousset, Using big data in order better to visualise the competences associated with jobs: the birth of an experimental project, CEREQ Workshop, 2017.

M. Olteanu and N. Villa-vialaneix, Using SOMbrero for clustering and visualizing complex data, 9th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01519707

M. Cottrell, M. Olteanu, F. Rossi, and N. Villa-vialaneix, Theoretical and applied aspects of the selforganizing maps, Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016, vol.428, pp.27-37, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01270701

. J. Mo61, M. Alerini, and . Olteanu, Markov et les ducs de Savoie : analyse de la temporalité du droit piémontosavoyard, Workshop on "Data-mining in human and social sciences : issues and perspectives, 2012.

M. Olteanu, Etude des trajectoires d'insertion professionnelleà l'aide de chaines de Markov nonhomogènes et de cartes auto-organisées, Ateliers d'Ouverture, CEREQ, 2011.

W. Clark, M. Olteanu, and J. Randon-furling, Segregation beyond scale: assessing the individual perceptions of migrant residential segregation, European Colloquium on Theoretical and Quantitative Geography (ECTQG), 2019.

M. Olteanu, J. Randon-furling, and W. Clark, Focal distances and distortion coefficients: assessing the individual perception of multiscalar segregation, 2019.

M. Olteanu, K. Pame, G. Beaunée, C. Bidot, and E. Vergu, Clustering and visualizing large cattle-trade networks using relational self-organizing maps, 51èmes Journées de Statistique de la SFdS, 2019.

W. Clark, J. Randon-furling, and M. Olteanu, A new method for analyzing ethnic mixing: Southern California as an exemplar, American Association of Geographers Meeting (AAG 2019), 2019.

M. Olteanu and J. Randon-furling, Converging to the city: a myriad trajectories, Conference in Complex Systems (CCS 2018), 2018.

W. Clark, J. Randon-furling, and M. Olteanu, A new method for analyzing ethnic mixing: studies from Southern California, 23rd International Conference on Computational Statistics, 2018.

W. Clark, J. Randon-furling, and M. Olteanu, A new method for analyzing ethnic mixing: studies from Southern California, European Network for Housing Research (ENHR) Conference, 2018.

M. Olteanu, G. Beaunée, C. Bidot, K. Pame, and E. Vergu, Clustering and visualizing large cattle-trading networks using self-organizing maps, International School and Conference on Network Science, 2018.

M. Olteanu and J. Randon-furling, Multiscalar socio-spatial dynamics in the city, BIFI International Conference on Complex Systems, 2018.

M. Olteanu, G. Beaunée, C. Bidot, C. Laredo, and E. Vergu, Using SOMbrero for clustering and visualizing large cattle-trading networks, BIFI International Conference on Complex Systems, 2018.

M. Olteanu and J. Randon-furling, Analyzing spatial dissimilarities via effective time-series, International Work-Conference on Time Series Analysis (ITISE 2017), 2017.

J. Alerini and M. Olteanu, Exploring a century of Savoy history using hidden-Markov models with Betainflated distributions, International Work-Conference on Time Series Analysis (ITISE 2017), 2017.

J. Bardet, C. Faure, J. Lacaille, and M. Olteanu, Design aircraft engine bivariate data phases using change-point detection methods and self-organizing maps, International Work-Conference on Time Series Analysis (ITISE 2017), 2017.

M. Olteanu and N. Villa, Using SOMbrero for clustering and visualizing complex data, International Workshop on Operations Research (IWOR 2017), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01519707

P. Alpi, M. Olteanu, and J. Yilmaz, Unsupervised learning for panel data, LaCOSA II International conference on sequence analysis and related methods, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01519710

M. Olteanu and N. Villa-vialaneix, Classification et visualisation de graphes avec SOMbrero, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01171557

M. Olteanu and N. Villa-vialaneix, Multiple dissimilarity SOM for clustering and visualizing graphs with node and edge attributes, International Conference on Machine Learning (ICML 2015, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01175731

M. Olteanu and N. Villa-vialaneix, Self-organizing maps for clustering visualization of bipartite graphs, 46èmes Journées de Statistique de la SFdS (JDS 2014), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01001991

L. Bendhaiba, M. Olteanu, and N. Villa-vialaneix, SOMbrero : cartes auto-organisatrices stochastiques pour l'intégration de données décrites par des tableaux de dissimilarités, 2013.

C. Cierco-ayrolles, M. Olteanu, and N. Villa-vialaneix, Carte auto-organisatrice pour graphesétiquetés, Extraction et Gestion des Connaissances (EGC 2013), 2013.

M. Olteanu and J. Ridgway, DiscreteTS : two hidden-Markov models for time series of count data, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00717493

J. Alerini, M. Olteanu, and J. Ridgway, Modélisation de séries temporellesà valeurs entières par des modèles autorégressifsà changements de régime, 44èmes Journées de Statistique de la SFdS, 2012.

J. Alerini, M. Olteanu, and J. Ridgway, An application of regime-switching models to historical data, 10th International Conference on Operations Research (ICOR 2012), 2012.
URL : https://hal.archives-ouvertes.fr/hal-00707136

C. Laredo, V. Nicolas, and M. Olteanu, On the use of self-organizing maps for the representation of Barcoding data : an application to Hylomyscus data, 4th International Barcode of Life Conference, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00655591

S. Massoni, M. Olteanu, and P. Rousset, Career-path analysis using drifting Markov models (DMM) and selforganizing maps, 9th International Conference on Operations Research (ICOR 2010), 2010.
URL : https://hal.archives-ouvertes.fr/hal-00443530

M. Olteanu and J. Rynkiewicz, Consistency of the Bayesian Information Criterion for a class of mixture autoregressive models, The 11th Conference of the Romanian Society of Probability and Statistics, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00308541

M. T. Boyer-xambeu, G. Deleplace, P. Gaubert, L. Gillard, I. Kammoun et al., Combining Markov switching models and the detection of change-points with the SOM algorithm to explain a temporal process, 8th International Conference on Operations Research, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00989942

F. Austerlitz, K. Bleakley, M. Olteanu, O. David, C. Laredo et al., Comparing phylogenetic and statistical classification methods for DNA barcoding, 2nd International Barcode of Life Conference, 2007.

M. T. Boyer-xambeu, G. Deleplace, P. Gaubert, L. Gillard, and M. Olteanu, The periodization of the international bimetallism, 7th International Conference on Operations Research (ICOR 2006), pp.1821-1873, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00308859

M. Olteanu and J. Rynkiewicz, Estimating the number of regimes in an autoregressive model with Markov switching, 7th International Conference on Operations Research (ICOR 2006), 2006.

B. Maillet, M. Olteanu, and J. Rynkiewicz, Nonlinear analysis of shocks when financial markets are subject to changes in regime, Colloque Econométrie des Valeurs Mobilières, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00308477

M. Olteanu and J. Rynkiewicz, Prévision d'un indice des chocs du marché avec des modèles hybrides HMM-MLP, Approches Connexionnistes en Sciences Economiques et de Gestion (ACSEG 2003), 2003.

T. M. Hoang and M. Olteanu, Coupled self-organizing maps for the bi-clustering of microarray data, Intelligent Data Analysis in Medicine and Pharmacology, 2003.

G. Software, .. N. Vialaneix, J. Mariette, M. Olteanu, F. Rossi et al., SOMbrero: SOM Bound to Realize Euclidean and Relational Outputs, 2019.

F. A. Duboin, Raccoolta per ordine di materie delle leggi cioè editti, manifesti, ecc., pubblicati negli stati della Real Casa di Savoia fino all'8 dicembre 1798, pp.1818-1869

T. Couzin, Contribution piémontaiseà la genèse de l'État italien. L'historicité de la « Raccolta per ordine di materie delle leggi, Bolettino Storico-Bibliografico Subalpino, pp.101-120, 2008.

. Ch, L. Truong, N. Oudre, and . Vayatis, A review of change point detection methods, 2018.

W. Zucchini, I. L. Macdonald, and R. Langrock, Hidden Markov models for time series: an introduction using, 2017.

O. Cappé, E. Moulines, and T. Ryden, Inference in Hidden Markov Models, 2005.

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