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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)).
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