Biography Anastase Charantonis

Anastase Charantonis



Anastase is an academic with an international background. His initial university studies were in mathematics with a major in statistics from the Capodistria University in Athens, Greece. He went on to obtain his post-graduate degree from the Ecole Polytechnique of Paris in Remote Sensing of the Atmosphere and Ocean, majoring in processing and analyzing environmental data.

His subsequent Post-doctoral thesis and contracts (post-doc from LOCEAN/IPSL, Conservatoire National des Arts et Metiers - CNAM, Institut des Mines Télécom SudParis, École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - ENSIIE) have led him to focus on designing statistical methods to model the environment.

Anastase has been particularly active in classification and clustering methods, hidden Markov chains, as well as current deep learning methods combining modern architectures such as Long Short-Term Memory based Convolutional Recurrent Neural Networks. Thanks to a scholarship from the French Defense Procurement Agency (DGA), his thesis at the Université Pierre et Marie Curie under the supervision of Prof. Sylvie Thiria was entitled “Inversional ocean surface data methodology to reconstruct vertical profiles using hidden Marcov chains and self-organizing maps”.

Anastase’s plans for future research include continuing to design statistical methods to model complex systems. The close relations he has fostered with environmental laboratories have led him to become familiar with a number of the real issues at stake and he is therefore interested in working on the interaction between statistical modeling and big data techniques using the latest breakthroughs in research. Anastase provides all types of statistical modeling services based on the challenging demands our customers face and is of great service when it comes to the complex situations involved in the tasks entrusted to us.


A.A. Charantonis et al. “Retrieving vertical profiles of temperature profiles along the ARAMIS rail from satellite observations by using hidden Markov models and self organizing maps.” Remote Sensing of the Environment (2017).
C. Chapman; A. A. Charantonis, "Reconstruction of Subsurface Velocities From Satellite Observations Using Iterative Self-Organizing Maps," in IEEE Geoscience and Remote Sensing Letters, vol.PP, no.99, pp.1-4 (2016)
Karagianni, E. A., et al. "Radio Refractivity and Rain-Rate Estimations over Northwest Aegean Archipelagos for Electromagnetic Wave Attenuation Modelling." Information, Communication and Environment: Marine Navigation and Safety of Sea Transportation (2015): 67.
Charantonis, A. A., Badran, F., & Thiria, S. (2015). “Retrieving the evolution of vertical profiles of Chlorophyll -A from satellite observations using Hidden Markov Models and Self Organizing Topological Maps.” Remote Sensing of Environment, 163, 229 239.
Parard, G., Charantonis, A. A., & Rutgerson, A. (2015). “Remote sensing the sea surface CO 2 of the Baltic Sea using the SOMLO methodology.” Biogeosciences, 12(11), 3369 3384.
Parard, G., A. A. Charantonis, and A. Rutgerson. "Remote sensing algorithm for sea surface CO 2 in the Baltic Sea." Biogeosciences Discussions 11.8 (2014): 12255 12294.
Charantonis, A. A., F. Badran, and S. Thiria. “PROFHMM_UNC: Introducing a Priori Knowledge for Completing Missing Values of Multidimensional Time Series.” Int'l J. of

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