Hi. I'm Iñigo Urteaga.

I am a tenure-track 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 an Associate Research Scientist from 2018 to 2022 in the Applied Physics and Applied Mathematics department at Columbia University, jointly affiliated with its Data Science Institute. Previously, I was a data-science postdoctoral scientist at Columbia University from 2016 to 2018 working with Prof. Chris Wiggins and Prof. Noémie Elhadad on statistical machine learning for healthcare data, in the context of electronic health records and self-tracked data.

I completed my Ph.D. in Electrical Engineering at Stony Brook University in 2016 under the supervision of Prof. Petar M. Djurić. My dissertation is entitled "Sequential Monte Carlo methods for inference and prediction of time-series". My research attracted interest in econometrics (for prediction in stochastic volatility models) and biomedicine (for the study of fetal heart-rate signals).

I obtained my degree in telecommunications engineering from the UPV/EHU Faculty of Engineering in Bilbao, Spain, and worked in Research and Development projects at Tecnalia.

These are my main areas of research.

Bayesian Theory

I am interested in probabilistic modeling in general, and Bayesian statistics in particular. I study Bayesian methods and their application to a plethora of problems.

Approximate Inference

I have specialized in the study and application of Monte Carlo and Variational methods, such as Sequential Monte Carlo, MCMC, and variational techniques.

Stochastic Processes

My dissertation is on the description, estimation and prediction of stochastic processes. I have extensively worked with ARMA, FARIMA and related models for time-series data.

Non-parametric Bayesian models

I am interested in the adoption of non-parametric Bayesian models (e.g., Gaussian, Dirichlet and Pitman-Yor processes) for statistical learning.

The multi-armed bandit problem

I have been working on the Bayesian analysis of the multi-armed bandit problem, by exploring the use of approximate inference and flexible Bayesian reward modeling.

Reinforcement learning & prescriptive modeling

I am interested in the study of sequential decision processes in general, and its application to real-life applications: e.g., healthcare in particular.

Here is a timeline of my professional experience.

  • Ikerbasque Research Fellow

    Machine Learning
    Basque Center for Applied Mathematics
  • Associate Research Scientist

    Applied Mathematics
    Columbia University
  • Postdoctoral Research Scientist

    Columbia University
  • Ph.D. Electrical Engineering

    Stony Brook University
  • Researcher

    Tecnalia Telecom
  • Telecommunication Engineer

  • Research Assistant

    Colorado School of Mines
  • M.S. Telecommunication Engineering

    ETSI Bilbao (UPV/EHU)

I am honored to have received ...

2022 La Caixa Foundation's Postdoctoral Junior Leader - Incoming

Fellowship awarded to research
Statistical machine learning for real-life time-varying phenomena, collected via not-at-random measurement processes.

2021 STAT Wunderkind

In recognition of my early-career scientific work and significant contributions on statistical modeling and machine learning for mobile health data

NeurIPS Top Reviewer

10% highest-scoring reviewers at NeurIPS 2020

Amongst the 400 highest-scoring reviewers at NeurIPS 2019

30% highest-scoring reviewers at NeurIPS 2018

2016 Best Graduate EE Student award

Best Graduate Student in Electrical and Computer Engineering at Stony Brook University

Armstrong Memorial Research Foundation

Spring 2016 Provost Graduate Lecture Series Speaker

Provost Graduate Lecture talk at Stony Brook University

Lecture available online Youtube

Fall 2015 Distinguished Travel Award

For outstanding research by Stony Brook University graduate students

Distinguised Travel Award by the Stony Brook Graduate School

2015 Professional Development Award

GSEU award for developing Graduate Student's full professional potential.

New York State and Graduate Student Employees Union

Torres Quevedo Research Fellowship

2009-2011 fellowship PTQ-09-02-01814 for young researchers

Ministerio de Ciencia e Innovación, España

Find more details in my full CV

Download my CV

You can contact me via email

iurteaga @ bcamath.org

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