Maestría en Ciencia de Datos: Alfredo Garbuno Iñigo

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Alfredo Garbuno Iñigo

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alfredo.garbuno@itam.mx

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agarbuno.github.io

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Alfredo Garbuno Iñigo es Licenciado en Matemáticas Aplicadas y Actuaría, y Maestro en Ciencia de Datos por el ITAM. Realizó sus estudios de doctorado en la Universidad de Liverpool en Reino Unido. Al finalizar, realizó una estancia postdoctoral en los departamentos de Matemáticas Computacionales y Geología y Ciencias Planetarias en el Instituto Tecnológico de California (Caltech).

Actualmente es profesor de tiempo completo en el Departamento de Estadística del ITAM y colabora en el proyecto CliMA, para incorporar incertidumbre en los procesos de asimilación de datos satelitales.

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Intereses de investigación

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  • Problemas Inversos Bayesianos
  • Asimilación de Datos
  • Ecuaciones Diferenciales Estocásticas
  • Estadística Bayesiana no Parámetrica
  • Análisis Numérico

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Publicaciones

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Garbuno-Inigo, A., Nusken, N., and Reich, S. (2020). “Affine invariant interacting Langevin dynamics for Bayesian inference”. SIAM Journal on Applied Dynamical Systems, to appear.

Cleary, E., Garbuno-Inigo, A., Lan, S., Schneider, T., and Stuart, A.M. (2020) “Calibrate, Emulate, Sample”. Journal of Computational Physics, to appear.

Garbuno-Inigo, A., Hoffmann F., Li W., and Stuart, A.M. (2019). “Interacting Langevin Diffusions: Gradient Structure And Ensemble Kalman Sampler”. SIAM Journal on Applied Dynamical Systems, to appear.

DiazDelaO, F. A., Garbuno-Inigo, A., Au, S. K., & Yoshida, I. (2017). “Bayesian updating and model class selection with Subset Simulation”. Computer Methods in Applied Mechanics and Engineering, 317, 1102–1121.

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Cursos

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Modelación Bayesiana.
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El programa de la Maestría en Ciencia de Datos es una iniciativa del ITAM para satisfacer la creciente demanda nacional e internacional de profesionistas con conocimientos sólidos en el modelado a partir de datos (en especial grandes datos) para ayudar a la toma de decisiones estratégicas.

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