Skip to content
View ebprado's full-sized avatar
  • Lancaster University
  • Lancaster, UK

Block or report ebprado

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
ebprado/README.md

Hey 👋

My name is Estevão Prado and I'm a senior Data Scientist in the Department of Data Intelligence at Bradesco bank, Brazil. I develop credit risk models which automate/scale credit decisions and mitigate risk using statistics and machine learning. My programming background involves advanced knowledge of R (e.g., dplyr, ggplot), SAS, SQL and Python (e.g., PySpark, pandas).

Prior to my current position, I was a senior research associate (i.e., a post-doctoral) in statistical machine learning at Lancaster University, UK, under a fellowship partnered with The Alan Turing Institute. I worked 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.e., BART). 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).

See my Google Scholar profile for my publications.

Pinned Loading

  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 1

  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