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Équipe de recherche :Research Team:
Signal, Statistique et Apprentissage (S2A)Signal, Statistics and Learning (S2A)
Laboratoire :Laboratory:
Laboratoire Traitement et Communication de l'Information (LTCI)Information Processing and Communication Laboratory (LTCI)
Département :Department:
Image, Données, Signal (IDS)Image, Data, Signal (IDS)
Pavlo Mozharovskyi joined Télécom Paris in 2018. After having finished his studies at Kyiv Polytechnic Institute in automation control and informatics, he obtained a PhD degree at the University of Cologne in 2014, where he conducted research in nonparametric and computational statistics and classification. He has been postdoctoral fellow of the Centre Henri Lebesgue at Agrocampus Ouest in Rennes for a year working on imputation of missing values, and then joined the CREST laboratory at the National School of Statistics and Information Analysis (ENSAI). Currently he is Associate Professor in the Team Signal, Statistique et Apprentissage (S²A) of the Information Processing and Communication Laboratory (LTCI). His main research interests lie in the areas of data depth, machine learning, computational statistics, robust statistics, explainable AI, multivariate data analysis, functional data analysis, data envelopment analysis.
Complete Curriculum vitae.
Courses taught in scholar year 2020-2021:
- Statistics (2nd year, MDI220).
- Non-supervised learning (Special course for Natixis).
- Statistical Learning and Data Mining (Specialised Master Big Data, MDI343).
- Tail Events Analysis : Robustness, Outliers and Models for Extreme Values (Master Data Science, DATA918).
Courses taught in scholar year 2019-2020:
- Statistical Module (Master Artificial Intelligence, MDI721).
- Statistical Learning (Master Artificial Intelligence, IA710).
- Statistical Learning and Data Mining (Specialised Master Big Data, MDI343).
- Statistics: Linear Models (2nd year, SD-TSIA204).
- Machine Learning for Multimedia (Master Multimedia, MN915).
- Advanced Machine Learning (Specialised Master Big Data, MDI341).
- Machine Learning (2nd year, SD-TSIA210).
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Tail Events Analysis : Robustness, Outliers and Models for Extreme Values (Master Data Science, DATA918).
Courses taught in scholar year 2018-2019:
- Statistics (2nd year, MDI220).
- Linear Models (Specialised Master Big Data, MDI720).
- Statistical Learning and Data Mining (Specialised Master Big Data, MDI343/MDI724).
- Statistics: Linear Models (2nd year, SD-TSIA204).
- Machine Learning for Multimedia (Master Multimedia, MN915).
- Advanced Machine Learning (Specialised Master Big Data, MDI341/MDI732).
- Machine Learning (2nd year, SD-TSIA210 (CrD)).
Review: one year of research 2022
Faculty Members, Innovation — 13/06/2023The document depicts the great variety of scientific fields, research projects and applications generated by this abundant ecosystem.Rétrospective : un an de recherche
Faculty Members, Innovation — 13/06/2023L'ouvrage présente la grande diversité des domaines scientifiques du numérique, les travaux des 18 équipes de recherche et les [...]
Data science et performance industrielle
Corporate Partnerships, Data Science & AI — 17/06/2022Valeo, partenaire industriel de la chaire Data Science & Artificial Intelligence for Digitalized Industry & [...]
Review: one year of research
Faculty Members, Innovation — 28/04/2022The generously illustrated 94-page document depicts the great variety of scientific fields, research projects and applications generated [...]Rétrospective : un an de recherche
Faculty Members, Innovation — 28/04/2022L'ouvrage présente, en 94 pages richement illustrées, la grande diversité des domaines scientifiques du numérique, les travaux [...]
Un an de recherche et d’innovation, prix de l'ARCES!
Faculty Members, Innovation — 08/10/2021L’Association des Responsables de Com' de l'Ens'Sup' décerne un des Prix de la Com’ 2021 à l'École qui a su [...]
La détection d’anomalies : un domaine en exploration
Data Science & AI — 01/07/2021Si la collecte, le stockage et l’analyse sont les premiers traitements appliqués aux données massives qui viennent [...]
Un an de recherche et d’innovation
Faculty Members, Innovation — 10/03/2021L'ouvrage présente, en 84 pages richement illustrées, la grande diversité des domaines scientifiques du numérique, les travaux [...]A year of research and innovation
Faculty Members, Innovation — 10/03/2021The generously illustrated 84-page document depicts the great variety of scientific fields, research projects and applications [...]
Interprétation, responsabilité et robustesse dans le machine learning
Digital Trust, Data Science & AI — 17/02/2021De nos jours, la science des données, le machine learning, les solutions basées sur [...]Interpretability, Accountability and Robustness in Machine Learning
Digital Trust, Data Science & AI — 16/02/2021Nowadays, data science, machine learning, artificial intelligence based solutions being [...]
Une IA explicable : flexibilité et spécificité du contexte
Digital Trust, Data Science & AI, Faculty Members — 23/04/2020L'initiative Operational AI Ethics de Télécom Paris vient de publier son premier [...]Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach
Digital Trust, Data Science & AI — 23/04/2020Telecom Paris' Operational AI Ethics initiative has just published its first report [...]
4 postes de post-doctorant·es en apprentissage statistique à Télécom Paris
Data Science & AI — 15/10/20194 postes de post-doctorant·es sont actuellement proposés pour des missions portant sur [...]