Amir Dib
Research Data Scientist

I completed a Phd at ENS Paris-Saclay under the supervision of Nicolas VAYATIS and Mathilde MOUGEOT titled High dimensional pattern learning for symbolic time-series. My research focused on ML model for symbolic time series classification applied to anomaly detection. It covers related problem in statistical learning theory, bayesian inference and pattern extraction. The PhD manuscript and the PhD defense' slides are available online.


Ph.D Machine learning
2017 - 2021
ENS Paris-Saclay
M.Sc. Probability & Finance (DEA El Karoui)
2014 - 2015
Sorbonne University, Polytechnique
M.Sc. Fundamental Physics
2012 - 2015
Paris-Saclay University

Work Experience

Lead Research Scientist
2021 - end 2022
PhD Candidate
2017 - 2021
ENS Paris-Saclay, French National Railway Company (SNCF)
Research Data Scientist.
2015 - 2017
French National Railway Company (SNCF)
Data Scientist.
2015 - 2015

Recent Publications

A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction, European Conference on Machine Learning (ECML PKDD 2022)
Victor Amblard , Amir Dib , Noëlie Cherrier , Guillaume Barthe
High dimensional pattern-based learning for temporal time series, Thesis
Amir Dib
Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review, Preprint
Marie Garin , Limnios Myrto , Marie Garin , Bargiotas, Ioannis , Boulant Olivier , Chick Stephen , Dib Amir , Evgenious Theorodors , Fekom Mathilde , Kalogeratos Argyris
Localized pattern mining, To be submitted.
Amir Dib* , Cyrus Cousins*
Bayesian Feature Discovery for Predictive Maintenance, IEEE European Signal Processing Conference (EUSIPCO 2021)
Amir Dib , Charles Truong , Laurent Oudre , Mathilde Mougeot , Nicolas Vayatis , Heloise Nonne
Quantized Variational Inference, Conference on Neural Information Processing Systems (NeurIPS 2020).
Amir DIB



Le projet vise à la mise en place d’un outil d’aide à la décision pour les hôpitaux et autres unités sensibles soumis à l’attrition du personnel atteint par le COVID-19. L’objectif est de produire des rapports journaliers permettant à la fois de visualiser l’évolution de l’épidémie à l’échelle de l’unité, mais aussi de proposer des projections à quelques jours et enfin d’évaluer a priori les scénarios de redéploiement des personnels ou de réorganisation interne.

Aave (ETHLend)

ETHLend is a fully decentralized financial marketplace built on top of the Ethereum Network allowing lenders and borrowers from all over the world to create peer to peer lending agreements in a secure and transparent way using Blockchain and Smart Contracts.