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Olá! 👋

Welcome to my Github page! My name is Mateus Maia, and I am a Ph.D. student at Maynooth University (Ireland) under the supervision of Professor Andrew Parnell and co-supervision of Professor Keefe Murphy. My ongoing thesis focuses on developing extensions to Bayesian Additive Regression Trees (BART).

Before this, I completed my Master's degree in Statistics at the Federal University of Bahia (Brazil), where I worked under the guidance of Professor Anderson Ara. During my master's program, I built novel ensemble settings and developed a model called "Random Machines" that used a novel ensemble technique using Support Vector Machines as base learners. This approach achieved a great predictive capacity in my experiments. I also hold a Bsc. degree in Geophysics by the Federal University of Bahia (Brazil).

My current research interests include tree-based models, statistical learning, Bayesian statistical learning, and ensemble models. I have advanced knowledge of R programming and a good understanding of C++, Python, Julia, and LaTeX.

This Github page will host some of my works, including my research, codes, and projects. I hope this page will help others interested in machine learning and statistical modeling, and I am always open to collaborate or discuss new ideas. Please feel free to contact me if you have any questions or comments.

Mateus Maia's Projects

18s191 icon 18s191

Course 18.S191 at MIT, fall 2020 - Introduction to computational thinking with Julia:

armbart icon armbart

Armadillo RCPP implementation to use BART

article-resources icon article-resources

A repository for the source code, notebooks, data, files, and other assets used in the data science and machine learning articles on LearnDataSci

bart icon bart

My minimal (and very slow) version of BART for future extensions

bart2 icon bart2

My rcpp implementation of Bayesian Additive Regression Trees model (Chipman, 2010)

bart3 icon bart3

Address convergence in BART

bart4 icon bart4

Improved version of bart2 repository which implements a fast BART model. Used as base for future BART extensions.

bart5 icon bart5

RCPP implementation with classification output

btbart icon btbart

BART using binary tree data structure

copygpbart icon copygpbart

Another approach to calculate the prediction sample from GP-BART

datainbahia icon datainbahia

Presenteation at the V Data in Bahia - AdaBoosting the weighted wisdom of the crowd

ensemblemethods icon ensemblemethods

Slides of the short-course given to the Gamma research group of the Federal University of Bahia

fisher_matrix_plot icon fisher_matrix_plot

An piece of code to plot a matrix of multiple pairwise test between multiple categorical variables using Fisher's Exact Test.

gpbart icon gpbart

Implementation of GP-BART algorithm.

gprcpp icon gprcpp

A Gaussian Processes implementation in RCPP

hbart icon hbart

My (slow) implementation of heteroscedasticity Bayesian Additive Regression Trees (Pratola,2018)

hm804_nigp icon hm804_nigp

R implementation of the model "Gaussian Process Training with Input Noise" from Andrew McHutchon & Carl Edward Rasmussen (2011)

hugo-academic icon hugo-academic

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