I'm a recent PhD graduate from the the Department of Statistics at the University of Wisconsin - Madison, "la Caixa" Fellow, advised by Professor Wei-Yin Loh, a world expert in tree-based methods. My focus is on trustworthy AI, and I'm currently developing TRUST (Transparent, Robust and Ultra-Sparse Trees) - the most interpretable model tree algorithm ever created. Our results show that TRUST often matches or exceeds the accuracy of popular black-box machine learning models like Random Forests, while remaining fully explainable. My goal is simple: to provide innovative and safe AI tools that allow users in high-stakes domains to stop choosing between accuracy and interpretability - and, in doing so, make a positive impact on society.
Before joining UW-Madison, I worked as a financial risk analyst at the European Central Bank. Earlier, I was a Master's student at Barcelona Tech (UPC), an exchange student-athlete at Carnegie Mellon University and a double-degree undergraduate student at Universitat Pompeu Fabra (UPF).
Here on GitHub, I version-control the latest developments related to my TRUST algorithm. My free (pre-compiled) Python TRUST package trust-free is hosted on PyPI and can be downloaded for free and installed on macOs or Linux via pip install trust-free.
trust-free is a Python package for fitting interpretable regression models using Transparent, Robust, and Ultra-Sparse Trees (TRUST) — a new generation of Linear Model Trees (LMTs) with high accuracy and intuitive explanations. It is based on my peer-reviewed paper, presented at the 22nd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2025).
The package currently supports standard regression and experimental time-series regression tasks. Future releases will also tackle other tasks such as classification.