Welcome to my AI & Data Science portfolio!
Here, I showcase hands-on experience in machine learning, deep learning, statistical modeling, and AI applications in diverse areas such as robotics.
Description:
A collection of end-to-end machine learning and deep learning projects, including data preprocessing, model training, evaluation, and visualization.
Skills: Python, NumPy, Pandas, scikit-learn, TensorFlow/PyTorch, feature engineering, hyperparameter tuning.
Key Projects:
- Classification and regression models on public datasets.
- Neural network experiments exploring architectures and hyperparameters.
Description:
Statistical analysis of Life Expectancy trends across genders and countries and assessment of the potential existence of a theoretical limit to human lifespan using Extreme Value Theory (GPD).
Skills: R, R Shiny, Statistical modeling, Data visualisation, Data analysis, plotly, tidyverse.
Key Insights:
- Trends in life expectancy across countries and demographics.
- Application of Extreme Value Theory to estimate human lifespan limit.
Description:
Integration of Large Language Models with ROS2 to develop a high-level robotic task planner capable of reasoning and autonomous navigation.
Skills: Python, ROS2, Nav2, Linux (Ubuntu/WSL), Motion planning, AI reasoning, System integration.
Key Achievements:
- Demonstrated autonomous planning capabilities guided by LLM reasoning.
- Bridges advanced AI concepts with real-world robotics applications.
📍 IMAG Building, Université Grenoble Alpes, France
📅 January 21–23, 2026
🏛️ Organized by SBI4C Chair, MIAI Institute
Description:
Currently participating in a hands-on hackathon focused on modern Bayesian inference techniques for complex simulators, with an emphasis on likelihood-free inference and uncertainty quantification in scientific models.
Topics & Techniques:
- Bayesian inference and probabilistic modeling
- Simulation-Based Inference (SBI)
- Neural Posterior Estimation (SNPE)
- Identifiability and uncertainty quantification
- Inference for dynamical systems (e.g. epidemiological SIR models)
Hands-on Work:
- Implemented SBI pipelines using PyTorch and the
sbilibrary - Performed parameter inference for deterministic and stochastic simulators
- Analyzed posterior distributions and posterior predictive checks
- Studied the impact of noise, data limitations, and model identifiability
Skills: Bayesian statistics, probabilistic reasoning, simulation-based modeling, PyTorch, scientific computing, data visualization.
- Strong foundation in machine learning, deep learning, and statistical modeling, with growing expertise in Bayesian inference and probabilistic methods.
- Hands-on experience applying AI and statistics to real-world and research-driven problems, including simulation-based inference and uncertainty quantification.
- Proven ability to integrate advanced AI techniques (LLMs) with robotics and autonomous systems.
- Comfortable working across the full pipeline: modeling, inference, experimentation, and interpretation.
- Committed to clear documentation, reproducibility, and high-quality technical presentation.