Experience

I work in the Corporate Security Modelling team at Bradesco. My role involves developing and monitoring machine learning models to detect and prevent fraud.

I’m also responsible for statistical analyses and hypothesis testing, as well as data manipulation to generate effective solutions and valuable insights. My primary goal is to significantly reduce fraud-related losses.

Thesis: Network science and machine learning applied to criminal networks

I conducted research on network and data science applied to corruption and organized crime, focusing on extracting meaningful patterns from criminal activity data. My work aimed to uncover underlying structures, rules, and mechanisms shaping these networks, contributing to a deeper understanding of their dynamics.

Thesis: The dynamics of political corruption networks

I explored the structural evolution of political corruption networks using network science and data science. My research revealed unexpected patterns, including linearity in community structure evolution, trends in the growth of repeat offenders, and a coalescence-like process in network formation. Based on these statistical similarities, we developed a corruption network model capable of replicating empirical findings.

Thesis: Time series analysis via complexity-entropy curves

I introduced a novel parameter, embedding delay, to analyze time series from various sources. By extending the complexity-entropy curve approach, this method proved effective in detecting periodic patterns within noisy signals.