Gabriel Ruiz

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Office: Math & Sciences Bldg. 8145, University of California - Los Angeles


I am a PhD student in statistics at UCLA doing algorithmic and theoretical work on graphical models and data mining with motivations that include bioinformatics and slight gaps in their statistical theory. My main areas of interest include causal inference, causal discovery, and generally the intersection of machine learning and statistics. My advisors are Qing Zhou and Oscar Madrid Padilla.

I received my B.S. in statistics from University of Calfornia, Riverside in 2017 and had the pleasure of working there with Dr. Subir Ghosh through the MARC U program.

A copy of my cv can be found here.


NSF GRFP DGE-1650604

Preprints and Working Papers

Structural Equation Modeling

Causal Inference

Published/Accepted papers

Work Experience

I had the pleasure of working for Dmitri Zaykin at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina in Summer 2016 on simulation work related to multiple hypothesis testing in genetics. In Summer 2017, I worked at Draper Laboratory in Cambridge, MA with an engineering team in the Perception and Localization group thanks to the GEM Consortium Fellowship. And as a repeat data scientist intern at Adobe Inc., I worked on churn classification models and causal modeling in Summers 2019 and 2021, respectively.


I have been a Teaching Assistant for Statistics 10: Introductory Statistics (6x), Statistics 100A: Probability with Texas Hold ‘Em Examples (1x), Statistics 100C: Linear Models (1x), and Statistics 200C: High Dimensional Statistics (1x).


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