Data Scientist & Machine Learning Engineer
Python · SQL · Tabular Machine Learning · LLMs · AI Agents
I develop open-source tools and machine learning projects, from applied data analysis to experimental algorithms and reusable Python libraries.
My work focuses on tabular machine learning, feature engineering, model evaluation, and reproducible experimentation. I also explore language models, AI agents, and machine learning implementations from first principles.
I value reliable software engineering practices, including automated testing, clear documentation, reproducibility, and transparent reporting of results and limitations.
Two of my machine learning libraries are available on PyPI.
A Python library for tabular classification and regression based on adaptive coded memory and shared multi-resolution encoding.
- Binary and multiclass classification, and regression
- scikit-learn-style estimator API
- Automatic handling of mixed tabular data
- Experimental modeling approach with a documented stable API
Install: pip install codadapt
A PyTorch-based library for tabular classification and regression, designed to simplify neural network workflows on structured data.
- Binary and multiclass classification, and regression
- Automatic numerical and categorical preprocessing
- scikit-learn-compatible API
- Model evaluation, testing, and reproducible workflows
Install: pip install neurotabular
Expert in Datasets and Notebooks
- Datasets: #24 of 11,989 (14 medals)
- Notebooks: #249 of 59,944 (28 medals)
I publish datasets and notebooks focused on data quality, exploratory analysis, feature engineering, and predictive modeling.
| Project | Description |
|---|---|
| MorphTree | Experimental tabular machine learning library with native classification algorithms, Pure/NumPy backends, and optional Optuna tuning. |
| BurOCRazia | Offline application for identifying Italian administrative documents using OCR and a traceable SQLite catalog. |
| Building Agentic AutoML | Experimental framework exploring agent-driven ML workflows and iterative model evaluation. |
| Building LLM | Educational implementation progressing from statistical language models to a decoder-only Transformer. |
| Building Decision Trees | Decision tree implementations from first principles, including recursive trees, missing-value handling, and gradient boosting. |
| Home Credit Default Risk | Credit-risk modeling with feature engineering, gradient boosting, and SHAP-based interpretation. |
| Customer Support Agent | AI agent for request classification, automated handling, and human escalation. |
Languages: Python, SQL
Data analysis: pandas, NumPy, Jupyter
Machine learning: scikit-learn, XGBoost, LightGBM, SHAP
Deep learning: PyTorch, TensorFlow
Development: Git, GitHub, testing, reproducible experiments
Selected training programs:
- Google Data Analytics Professional Certificate, Google
- Production Machine Learning Systems, Google Cloud
- Supervised Machine Learning: Classification, IBM
- Supervised Machine Learning: Regression, IBM
- Unsupervised Machine Learning, IBM
- Getting Started with TensorFlow 2, Imperial College London
- Customising Your Models with TensorFlow 2, Imperial College London
- Natural Language Processing on Google Cloud, Google Cloud
- Computer Vision Fundamentals with Google Cloud, Google Cloud
- Databases and SQL for Data Science with Python, IBM