Compilers Researcher @ LAC
Master (MSc) of Computer Science (2019 - Now)
|Institution||Universidade Federal de Minas Gerias (UFMG), Brazil|
|Advisor||Fernando Magno Quintão Pereira|
Bachelor (BSc) of Computer Science (2014 - 2018)
|Institution||Pontifícia Universidade Católica de Minas Gerais (PUC Minas), Brazil|
|Grade||First Class Honors ( Second place on PUC Minas Computer Science Undergraduate Thesis Competition )|
|Thesis||NeurOMP: Automatic code paralellization using Reinforcement Learning|
|Advisor||Luís Fabrício Wanderley Góes|
Purposed a new Compiler Optimization, called NeurOMP, that uses the QLearning algorithm to automatic paralellize C/C++ code using OpenMP 4.0. Results shown that this method achieved the same speedup as a human specialist.
|Journal||IEEE Transactions on Computational Intelligence and AI in Games|
Presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. The system can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.
|Conference||European Conference on Parallel Processing|
Study of an unconventional low-cost energy-efficient HPC cluster composed of Raspberry Pi nodes when executing data mining algorithms compared with a Intel Xeon Phi. Results have shown that the Raspberry Pi cluster can consume up to 88.35% and 85.17% less power than Intel Xeon Phi when running Apriori and K-Means, respectively.
|Conference||XVI Simpósio em Sistemas Computacionais de Alto Desempenho|
Development and evaluation of parallel version of the Apriori algorithm in OpenMP exploring the computational power of multicore and manycore architectures. The parallel Apriori achieves a performance gain of up to 10x on a multicore also on a manycore.
|Topics||NLP, Deep Learning, Stemming|
Usage of a Seq2Seq Neural network model to create a multilanguage stemming strategy.
|Topics||Machine Learning, Automatic Parallelism, Compiler's Optimization|
Group of Machine Learning based Compiler's Optimization.
|Language||C#, Ruby, Java|
|Topics||Software Engineering, Startups|
A Social Startup that aims to join people who want to do good, asociations who needed help and shops which wanted to colaborate to this ecosystem.
|Topics||Graph Theory, Software Engineering|
A general purpose library to work with graph problems.
|Job Title||Data Scientist|
|Period||08/2018 - 03/2019|
Optimized an industry manufacturing process, reducing their material cost, saving 20 million dollars per year. It uses XGBoost to predict relevant variables to the cost function, which is minimized using a Genetic Algorithm heuristic.
|Job Title||Data Science Intern|
|Period||09/2017 - 07/2018|
Built a study content recommendation system based on students exams results. It uses several techniques and models used in Natural Language processing such as: TFIDF, Support Vector Machines (SVM) and Entity Tagging to try to improve the search engine, built using Elastic Search.
Developed a dynamic pricing application using Apache Spark to raise profits by 40% on retail segment. The developed systems automatically prepare the companies data, train and evaluates a Random Forest Models (to predict sales) using back test, most of the data are time series and finally it finds the best price to each product.
Optimized an e-commerce search engine using Apache Spark and a Vectorial Space Model.
|Job Title||Machine Learning Mentor and Reviewer|
|Period||02/2018 - 11/2018|
Helped to teach more than 100 students how Machine Learning works and how to use it to solve real life problems. I have teach students about: Deep Learning (NN, CNN, RNN and GANs), Random Forests, SVMs, Linear Regression, Classification Trees (CART algorithm) and Reinforcement Learning.
|Job Title||Full Stack Web Developer Intern|
|Period||08/2016 - 09/2017|
Projected and implemented a real-time chat app using Node.js, React and Socket.io to help customers solve their doubts, problems and needs.
Developed a scalable backend infrastructure using Node.js, express, docker and MongoDB capable to handle a hundred orders a second.
|Job Title||Artificial Inteligence Engineering Intern|
|Period||04/2016 - 08/2016|
Built Speck, a Ruby on Rails solution based on IBM Watson Personality Insights and Tone Analyser APIs to personalize teaching experience based on student personality.
Made a application to help malls decide which shops to open. It was built using Node.Js and IBM Watson Tradeoff Analytics API. It also scraps for each brands Tweets and uses IBM Watson Tone Analyser to detected people emotion about it.
|Job Title||Undergraduate Student Researcher|
|Period||02/2015 - 07/2018|
CNPq Researcher Scholarship
Active participation on three research projects.
Three articles published.
One award won.