I'm a Postdoctoral Fellow at Hi! PARIS doing research on interpretable ML for finance. Prior to this, I was an Adjunct Professor of Mathematics at Universitat Pompeu Fabra and of Statistics at BarcelonaTech (UPC). Before that, I earned my Ph.D. in Statistics at the University of Wisconsin - Madison as a "la Caixa" Fellow, advised by Professor Wei-Yin Loh.
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. In a world that is being pushed towards ever-increasing complexity and opacity, I am instead redefining the boundaries of what white-box models for tabular data can accomplish.
Before joining UW-Madison, I worked as a financial risk analyst at the European Central Bank, where I led some early ML projects in the Directorate of Risk Management, back in 2017. Earlier, I was a Master's student at BarcelonaTech (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 Python TRUST package trust-free is hosted on PyPI and can be downloaded for free and installed via pip install trust-free.
trust-free is a Python package for fitting interpretable regression and classification 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, recently published as PRICAI 2025 proceedings on Lecture Notes in Artificial Intelligence (Springer Nature).
The package currently supports multiclass classification, standard regression and experimental time-series regression tasks.
Upcoming Talks: