Description

I am a first year Ph.D. candidate in Department of Statistics (LaMME) , Paris Saclay University, advised by Professor Agathe Guilloux & Professor Blaise Hanczar. I obtained M.Sc. in Data Science from Paris Saclay University, France.

My main research interest lies in the field of probabilistic modeling, machine learning and causal inference, that is, designs and analysis for evaluating treatments and interventions in randomized experiments and observational studies, and their applications to health studies (also known as comparative effectiveness research) and social sciences.

Skills:

- Data Science ( ML/DL/RL/NLP)

- Software engineering

- Applied mathematics

What I am able to do for you:

- To reassemble strategic knowledge from your different sources of data, by doing statistical analysis by testing many mathematical models until reaching an optimal result (forecasting, anomaly detection, classification, extraction of rules of associations ... etc).

- Find patterns in your records, the different correlations, in order to detect anomalies and predict time series, so better understand your data.

- Cross and consolidate all of your data in order to allow a monitoring of

different business KPIs on a dashboard.

- Suggest improvements to apply to your current system.

Solutions can be python-based application development, or notebooks, with an implementation of interactive visualizations to facilitate understanding of the results.

Langues

Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Japonais
Notions

Préférences en matière de lieu de travail

Accepte de travailler sur site

Paris (jusqu’à 50 km), Paris (jusqu’à 20 km)

LaMME
Phd
CENTRES DE RECHERCHE
septembre 2020 - Aujourd'hui (5 ans et 9 mois)
Évry, France
Deep learning for estimating the individual treatment effect in a longitudinal context.
Supervisors : Pr. Agathe Guilloux (LaMME) & Pr. Blaise Hanczar (IBISC).

#DomainAdaptation #CausalInference #LongitudinalData
Deep Learning Machine learning causal inference Domain adaptation
LaMME
Research Internship | Deep learning & Causal inference
CENTRES DE RECHERCHE
avril 2020 - Aujourd'hui (6 ans et 2 mois)
Évry, France
Deep learning for estimating the individual treatment effect in a longitudinal context
Supervisors : Pr. Agathe Guilloux (LaMME) & Pr. Blaise Hanczar (IBISC).
Deep Learning Machine learning Python TensorFlow
CMAP Ecole Polytechnique
Research internship : Deep reinforcement learning & VQA
HIGH TECH
avril 2019 - août 2019 (4 mois)
Palaiseau, France
Research internship of 4 months under the supervision of Pr. Eric Moulines et Alice Martin.
Topic: Implement deep reinforcement learning algorithms on a "Visually Grounded Question Answering"task to improve dialogue generation . http://vixra.org/pdf/1909.0074v1.pdf
Machine learning Deep Learning reinforcement learning Python Pytorch TensorFlow

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Master's degree (M2) Data Science
Université Paris-Saclay - UEVE
➸ First semester : ➧ Reinforcement learning - Ecole Polytechnique ➧ Stochastic calculus - UEVE ➧ Theoretical guidelines for high-dimensional data analysis - Université Paris Sud ➧ Longitudinal data analysis - ENSIIE ➧ Statistics for stochastic processes - UEVE ➧ Non-parametric statistics - UEVE ➧ Introduction to Databases - UEVE ➧ Advanced topics in Databases - UEVE ➧ Scientific Python, R, Calcul parallèle GPU - UEVE ➧ Unsupervised learning - UEVE ➧ Machine learning - ENSIIE ➧ Financial Econometry - UEVE ➧ Numerical pricing methods - UEVE ➸ Second semester : ➧ Deep learning - UEVE ➧ Computational statistics - Telecom SudParis ➧ Graphical models - UEVE ➧ IT quant - UEVE ➧ Data camp - UEVE
Master 1 Mathématiqeus & Interactions
Université Paris Saclay - UEVE
2019
• My web page : www.ayoubabraich.fr • Relevant courses : - Programming in C - Functional analysis - Data analysis - Advanced probabilities - Statistical modeling - EDP and Hilbertian methods - Advanced regression - Financial markets - Stochastic processes - Time series - C ++ and VBA - Optimization and applications - Numerical analyzes of PDEs and finite elements - Statistical learning and regularized methods - Financial mathematics