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  • Lancaster University
  • Lancaster, UK

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ebprado/README.md

Hey 👋

My name is Estevão Prado and I'm a senior research associate in statistical machine learning in the Department of Mathematical Sciences at Lancaster University under a fellowship partnered with The Alan Turing Institute. I work with Professors Christopher Nemeth and Chris Sherlock 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.

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  1. MH-with-scalable-subsampling MH-with-scalable-subsampling Public

    Python scripts and data sets that can be used to reproduce the results presented in the paper "Metropolis-Hastings with Scalable Subsampling".

    Python

  2. AMBARTI AMBARTI Public

    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).

    R 4

  3. MOTR-BART MOTR-BART Public

    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).

    R 9 4

  4. CSP-BART CSP-BART Public

    R scripts and data sets to reproduce the results in the paper "Accounting for shared covariates in semi-parametric Bayesian additive regression trees". The Annals of Applied Statistics (to appear).

    R 4 2