Iñigo Urteaga

I am a a tenure-track LaCaixa Foundation Junior Leader and Ikerbasque Research Fellow in the Machine Learning group at BCAM.

I was previously (2018-2022) an Associate Research Scientist at Columbia University's Applied Physics and Applied Mathematics department, jointly affiliated with Columbia's 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).

My research interest is in statistical machine learning, computational Bayesian statistics, approximate inference methods, and sequential decision processes. Namely, I study statistical models and algorithms to extract information from data, for computer systems to effectively learn how to perform real-life tasks.

My body of research is in methodological and applied aspects of probabilistic machine learning for descriptive, predictive, and prescriptive tasks. I develop robust and efficient computational tools for inference, prediction and control, with applications to a wide range of disciplines, from healthcare to online digital services.

Please visit my personal webpage for more details.