PhD Defense: Explainable Classification of Uncertain Time Series

When:
13/12/2022 all-day
2022-12-13T01:00:00+01:00
2022-12-13T01:00:00+01:00

Date : 2022-12-13
Lieu : ISIMA, Salle du conseil (A102) and visio

Hello,

I hope you are doing well. 

I have the great pleasure to invite you to my PhD defense entitled Explainable Classification of Uncertain Time Series. The defense will take place on the 13th of December 2022 at 2 pm in room A102 (Salle du Conseil) at ISIMA.  You are also invited to share some drinks and candies after the defense in the room A104 right after the defense.

How to attend remotely?There will be two channels to attend the defense remotely:
– By Microsoft Teams using this link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_YWNmMDQ1MDAtYWFlOC00MDNjLWE3NTMtNjY5ODkxOTVhMDFm%40thread.v2/0?context=%7b%22Tid%22%3a%225a16bd04-b475-49ff-b11a-c6c8359db1b1%22%2c%22Oid%22%3a%22949eb4b9-6120-456f-95a8-6ec37948db76%22%7d 
– By YouTube using this link: https://youtu.be/EW1Wp3Fg-1Q. Feel free to leave a thumb up if you like the presentation and a thumb down if you did not. I will also be happy to read any comment you may have about the presentation.

Here is the abstract of the presentation: Time series classification is one of the most studied theoretical and applied fields of time series analysis. Many classical machine learning as well as deep learning algorithms, have been developed during the last decade to accurately perform time series classification. However, the case where the time series are uncertain is still under-explored. In this work, we discuss the importance of uncertainty handling in machine learning in general and in time series classification in particular. We propose efficient, robust and explainable methods for the classification of uncertain time series. We assess our methods on simulated datasets, but also on a real scenario in the astrophysics in which uncertainty in preponderant. The results we obtained are understandable and trustable by astronomers. Our proposed methods are tools that will facilitate the understanding of the universe in which we life in particular, and the field of uncertain time classification in general.

Here is the composition of the Jury:
Anthony BAGNALL (R) – University of East AngliaSebastien DESTERCKE (R) –  Heudiasyc, University of Technology of Compiegne

Elisa FROMONT (E) – IRISA, University of Rennes 1Emmanuel GANGLER (E) – LPC, University Clermont AuvergneDavid HILL (E) – LIMOS, University Clermont Auvergne
Themis PALPANAS (E) – LIPADE, Universite Paris CiteEngelbert
MEPHU NGUIFO (A) – LIMOS, University Clermont Auvergne(R): Reviewer, (E): Examinator, (A): Advisor

I am looking forward to defending my work in front of you.

Best regards

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