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👀 I’m interested in the Satisfiability of Machine and Deep Learning (Explainable AI, Causality..), I am passionated for mathematics and its applications like energy domaine, sustainability, ...
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🥇 MS.E in Computer Science (summa cum laude) from Ecole Polytechnique - Institut Polytechnique of Paris, France. Currently, I follow a Ph.D. program at the same institute.
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💞️ Open to collaborate on Explainability for Generative Models
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📫 How to reach me khlaid.oublal@polytechnique.edu (
.org[for graduate email]) | or khlaid.oublal@ip-paris.fr -
Training at Mathematical Institute, University of Oxford
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Summer School Oxford, Machine Learning (OxML2023): Generative Models on NLP & Finance
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Current work:
- Satisfiability modulo theories, Neural networks as a sub-symbolic approach with Pr. Sergio Mover
- Deep Q-Learning systems to avoid collisions 802.11bf electric scooter with Pr. Keun-Woo Lim
- Explainable Models for sequential data with Pr. François Roueff and Pr. Said Ladjal. Follow-up by Pr. Cristian Jutten.
- OpenXAI for time series with Stanford University (ongoing...)
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I collaborate to @huggingface Time Series Large Models
News 📣:
- [January 2024]🚀 Paper accepted at ICLR 2024: Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference
- [September 2023] Paper accepted at NeurIPS 2023: DISCOV
- [March 2023] Paper accepter at ICML 2023: Temporal Attention Bottleneck is Informative?
