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Education
- PhD in applied mathematics: Research in machine learning for predictive maintenance of power electronic modules through time-series forecasting, surrogate modeling, and stochastic processes, ENS Paris-Saclay, 2026.
- Masters MVA (Mathématiques, vision, apprentissage), ENS Paris-Saclay. Graduated with highest honors, 2022.
- Engineering degree in Applied mathematics, ENSTA Paris. Graduated with highest honors, 16.2/20 Grade, 2022.
- Preparatory class Institut préparatoire aux études d’ingénieurs Tunis (IPEIT). Ranked First Nationwide in the National Entrance Examination for Engineering Schools, 2019.
Work experience
- Winter 2023: Freelance
- Algorithmic trading based on a risk-return deep reinforcement learning algorithm
- Implemented a deep reinforcement learning agent for portfolio optimization, using a Markov decision process framework and recurrent neural networks to model financial time series
- Spring 2022: Research internship
- Datakalab
- Data-Driven quantization of neural networks for image classification and segmentation
- Supervisors: Edouard Yvinec, Kevin Bailly
- Spring 2021: Research internship
- CREST-ENSAE
- Statistical modeling for Cross platform audience estimation
- Supervisor: Guillaume Lecué
Skills
- Machine learning
- Pytorch
- Tensorflow
- Numpy
- Pandas
- Programming
- Python
- Probabilistic modeling
- Statistics
- Dynamic Time Warping
- Optimization
Miscellaneous
- Ranked First Nationwide in the National Entrance Examination for Engineering Schools (Tunisia)
- Baccalaureate 2017: Ranked 1st regionally (Ben Arous) in the mathematics specialty (section mathématiques)
- Competitive programming : Ranked 34/97 in the competitive programming competition SWERC (South Western Europe Regional Contest)
- Maths and logic : Top 20 in the Tunisian national exam of mathematics and participation in the training course organized by the Tunisian association of mathematics (ATSM)
- Freelance : Practice Question Writer at Study.com
Publications
Talks
Data-Efficient Reliability Assessment Using Machine Learning: Application to Lifetime Estimation of Power Electronic Modules
Talk at Amphi Dorothy Hodgkin, ENS Paris-Saclay, Gif-sur-Yvette, France
AI 4 Maintenance : Remaining Useful Life Prediction of Power Modules
Poster at La cité, Toulouse, France
Data-driven Metamodels for Failure Analysis of Power Electronic Modules
Talk at Palais de la Bourse, Bordeaux, France
L’IA pour la fiabilité des modules de puissance
Talk at Université Gustave Eiffel, Versailles, France
Prognostics of power electronic modules using machine learning
Poster at Conservatoire national des arts et métiers, Paris, France
Combining machine learning with finite element simulations for fast computation in power module failure Analysis due to wire bond degradation
Talk at Arts et Métiers – ENSAM (Paris Campus), Paris, France
Méthodes data-driven pour l’estimation de la durée de vie restante des modules de puissance
Talk at ENS Paris-Saclay, Gif-sur-Yvette, France
Physics-informed Markov chains for remaining useful life prediction of wire bonds in power electronic modules
Poster at Paganini Conference Center, Parma, Italy
Estimating the remaining useful life of power electronic modules using aging tests
Talk at Amphi Dorothy Hodgkin, ENS Paris-Saclay, Gif-sur-Yvette, France
Hybrid modeling for remaining useful life prediction in power module prognosis
Talk at Four Points by Sheraton Catania Hotel & Conference Center, Aci Castello, Catania, Italy