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 leading 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 of my TRUST algorithm. I also host my free (pre-compiled) Python TRUST package 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 state-of-the-art accuracy and intuitive explanations. It is based on my peer-reviewed paper, accepted 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.