With a background in applied mathematics and machine learning, I am particularly passionate about Graph Time Series Forecasting and Graph Reinforcement Learning, with applications to biological and engineering systems.
Publications & Working Papers
- Ramzi Dakhmouche, Ivan Lunati, and Hossein Gorji. "Robust Symbolic Regression for Network Trajectory Inference."
Transactions on Machine Learning Research 2025.
International Conference on Learning Representations 2024 MLGenX.
- Robust Deep Operator Learning. Submitted
- Ramzi Dakhmouche, and Hossein Gorji. Network System Forecasting Despite Topology Perturbation. In preparation
- Ramzi Dakhmouche, Ivan Lunati, and Hossein Gorji. Scalable Uncertainty-Aware Symbolic Regression for Network Model Discovery. In preparation
Resume
My resume can be downloaded here .
Master thesis update: September 15th, 2022
- Ericsson provisional patent filed : Multi-Task Bandits method for personalized sample efficient network parameter optimization.
- Master thesis defended.
- Machine learning paper under preparation.
Some of the classes I particularly enjoyed, this year:
As a Mathematics, Vision, Learning (MVL) Master's student at École Normale Supérieure (ENS) Paris-Saclay, these are some of the classes I particularly liked this year:
- Reinforcement learning, by Dr. Matteo Pirotta, Facebook AI research.
- Graphs in machine learning, by Dr. Daniele Calandriello, DeepMind Paris.
- Computational statistics, by Prof. Stéphanie Allasonnière, Université Paris Cité.
- Learning for time series, by Prof. Laurent Oudre , ENS Paris-Saclay.
- Advanced topics in Markov chains, by Prof. Eric Moulines, Ecole Polytechnique.
- Large scale optimization, by Dr. Emilie Chouzenoux, Inria Saclay.
Medium Articles
I have recently started writing medium articles and tutorials in which I share and explain classical but also state-of-the-art machine learning algorithms, methods and insights. Feel free follow me on medium if you're interested by such topics !
Ideas behind its proof of convergence.
An interesting a posteriori confidence score.
Article discussing generalization inequalities for ML algorithms.
Article discussing generalization inequalities for ML algorithms.
Tutorial on a classical statistical prediction method, which sometimes serves as a good benchmark.
Some quotes I find interesting