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+34 946 567 842
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+34 946 567 843
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iurteaga@bcamath.org
Information of interest
I am a tenure-tracked Ikerbasque Research Fellow in the Machine Learning group at BCAM, funded by LaCaixa Foundation's Junior Leader-Incoming award.
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.
- 2022 Ikerbasque Research Fellow
- 2022 LaCaixa Foundation’s Junior Leader Incoming
- 2021 STAT Wunderkind. In recognition of my early-career scientific work and contributions on statistical modeling and machine learning for mobile health data.
- 2016 Best Electrical and Computer Engineering Graduate Student, Armstrong Memorial Research Foundation, Stony Brook University.
- 2016 Provost Graduate Lecture Series Speaker, Stony Brook University, USA.