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
- G Ruiz, OH Madrid-Padilla, Q Zhou. “Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores.” arXiv Preprint, 2022. [Link][Causal Discovery review slides].
- G Ruiz, OH Madrid-Padilla, Q Zhou. “Scaleable causal discovery with tunable tail decay and statistical guarantees under power law or expoentially decaying tails.” Working Paper, 2022+.
- G Ruiz, OH Madrid-Padilla. “Non-asymptotic confidence bands on the probability an individual benefits from treatment (PIBT).” arXiv Preprint, 2022+. [Link][Poster].
- OH Madrid-Padilla, P Ding, Y Chen, G Ruiz. “A causal fused lasso for interpretable heterogeneous treatment effects.” arXiv Preprint, 2021. [Link].
- M Burkhart, G Ruiz. “Neuroevolutionary Feature Representations for Causal Inference.” International Conference on Computational Science, (to appear) 2022.
- OA Vsevolozhskaya, G Ruiz, DV Zaykin. “Bayesian prediction intervals for assessing P-value variability in prospective replication studies.” Translational Psychiatry, 2017. [Link]
- OA Vsevolozhskaya, CL Kuo, G Ruiz, L Diatchenko, DV Zaykin. “The more you test, the more you find: The smallest Pvalues become increasingly enriched with real findings as more tests are conducted.” Genetic Epidemiology, 2017. [Link]
- S Ghosh, G Ruiz, B Wales. “Subsampled data-based alternative regularized estimators.”
Journal of Data Science, 2020.
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).