Work:

Machine Learning Engineer working for over four years in the industry using machine learning in scalable systems to add value and solve business problems.

I also have a Master’s degree in Numerical Methods from UFPR. I use Python and AWS/GCP as my main work tools

Research

Reinfocement learning: In my Master’s thesis I studied reinfocement learning, where I developed a new algorithm called QSVR. The algorithm uses a Support Vector Regression algorithm call Online-SVR to estimate the Q-function of a environment. The results show it can perform better than the Deep Q-Network and Q-learning in a environment with small state space and a discrete action space. The code of the project is in this repository.

Other machine learning algorithms: I also worked on using neural networks along GARCH models in a hybrid approach to improve the forecasting of time series related to soybean price volatility. Another article I worked on uses the framework of Bayesian networks along with time series to predict diseases spread along the Brazilian territory.