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  • ENSAE Paris | Institut Polytechnique
  • Paris
  • LinkedIn in/moranenzo

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moranenzo/README.md

Enzo Moran

I am a machine learning and statistical engineering student at ENSAE Paris. I am interested in ML, deep learning, and optimization.

Currently on a gap year at INRIA Chile, I am working on a transformer-based algorithm for symbolic regression. I also worked on ensembling strategies at BNP Paribas Data & IA Lab, where I explored several approaches such as mixture-of-experts, bandit-algorithms, and reward weighting.

If you have any question about my work, feel free to reach out at enzo.moran@ensae.fr.

Below are projects from both academic work at ENSAE Paris and personal initiatives.

Projects

Wasserstein IRIS Clustering

Applying Wasserstein K-Means to cluster IRIS (small geographical units defined by INSEE) based on income and age distributions.
Involves Python, Machine Learning, and Optimal Transport. Repo here


Music Genre Classifier

Predicting music genres based on audio features from Spotify’s API. Benchmarked several models such as RandomForest, XGBoost, and CatBoost.
Involves Python, Machine Learning, and Data Preprocessing. Repo here


Optimal Transport Median

Define a median for multivariate datasets using optimal transport theory.
Involves Python, Optimal Transport, and Optimization. Repo here


Tile Arrangement Solver

Solving a tile arrangement puzzle using BFS and A* algorithms. A GUI built in Pygame is available.
Involves Python and Graph algorithms (BFS, A*). Repo here


Radical Right and Gender Gap

Analyzing the gender gap in political support for the radical right using statistical methods.
Involves R and Statistical analysis. Repo here


Contactless Smart Cards

Developing a system for secure data exchange using contactless smart cards and RFID technology.
Involves C++, Python, Physics, and RFID. Repo here

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  1. Music-Genre-Classifier Music-Genre-Classifier Public

    The PY-Music-Genre-Classifier predicts music genres based on audio features extracted from Spotify's API. The project uses supervised learning models such as RandomForest, XGBoost, and CatBoost to …

    Jupyter Notebook 1

  2. Tile-Arrangement-Solver Tile-Arrangement-Solver Public

    This repository is a Python project designed to solve a tile rearrangement puzzle. It uses multiple graph traversal algorithms (BFS, an improved BFS, and A*) to find optimized solutions. The projec…

    Python 2

  3. Wasserstein-IRIS-Clustering Wasserstein-IRIS-Clustering Public

    This repository is part of my ML internship with Vincent Divol, where I explored Wasserstein clustering to analyze and cluster French IRIS based on income distributions.

    Jupyter Notebook 1