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My name is Estevão Prado and I'm a senior research associate in statistical machine learning in the Department of Mathematics & Statistics at Lancaster University under a fellowship partnered with The Alan Turing Institute. I work with Professor Christopher Nemeth on the development of novel scalable Markov Chain Monte Carlo (MCMC) methods for large datasets.

I completed my PhD in Statistics at Maynooth University (Ireland) under the supervision of Professor Andrew Parnell and Dr. Rafael Moral where I worked on extensions to probabilistic tree-based machine learning algorithms. I hold an MRes in Statistics from the Federal University of Minas Gerais (Brazil) and a first-class honours Bsc in Statistics from the Federal University of Paraná (Brazil).

My main research interests lie in tree-based methods, Bayesian non-parametric regression, MCMC and computational statistics. Besides academia, I worked as a data scientist for 3.5 years at Bradesco and HSBC banks with statistical modelling for credit purposes. My programming background involves advanced knowledge of R (+10 yr), SAS (+5 yr), SQL (+5 yr) and, more recently, an intermediate level of Python.

Estevão's Projects

ambarti icon ambarti

R scripts to reproduce the results presented in the paper "Bayesian additive regression trees for genotype by environment interaction models". The Annals of Applied Statistics 17 (3) (2023).

bcf-discussion-paper icon bcf-discussion-paper

R scripts to run the simulation results of the discussion paper "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" (Hahn et al, 2020)

csp-bart icon csp-bart

R scripts and data sets that can be used to reproduce the results presented in the paper "Accounting for shared covariates in semi-parametric Bayesian additive regression trees".

dbarts icon dbarts

Discrete Bayesian Additive Regression Trees Sampler

mh-with-flexible-sub-sampling icon mh-with-flexible-sub-sampling

Python scripts and data sets that can be used to reproduce the results presented in the paper "Metropolis-Hastings with fast, flexible sub-sampling".

motr-bart icon motr-bart

R scripts and data sets that can be used to reproduce the results presented in the paper "Bayesian additive regression trees with model trees". Statistics and Computing 31, 20 (2021).

motr-bart-for-treatment-effects icon motr-bart-for-treatment-effects

This repository contains R scripts and synthetic data sets that can be used to fit the MOTR-BART (Prado et al., 2021) for treatment effects.

semibart icon semibart

R package for Bayesian semiparametric regression and structural mean models

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