Iñigo Urteaga

I am a tenure-track Ramón y Cajal and Ikerbasque Research Fellow in the Machine Learning group at the Basque Center for Applied Mathematics (BCAM).

I have specialized in statistical machine learning, computational Bayesian statistics, approximate inference methods, and sequential decision processes. I study statistical models and algorithms to extract information from data, for computer systems to effectively learn how to perform descriptive, predictive, and prescriptive tasks. My research is on methodological and applied aspects of probabilistic machine learning.

I was a LaCaixa Foundation Junior Leader at BCAM from 2022 to 2025, a Visiting Scholar at the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard in Fall 2024, and an Associate Research Scientist at Columbia University from 2018 to 2022, affiliated with its Applied Physics and Applied Mathematics department and Data Science Institute.

I attained my PhD in Electrical Engineering (2016) from Stony Brook University, NY, USA; and was a post-doctoral scientist across the Applied Mathematics and Biomedical Informatics departments at Columbia University (2016-2018).

Please visit my personal webpage for more details.