As a passionate professional specializing in data automation and machine learning, I build end-to-end pipelines that turn messy inputs into reliable, actionable data. I’ve automated large-scale web scraping and data-flow management to support analytics and decision-making. I currently freelance part-time with DoubleRo’s Elephant Stock wall-art company, delivering data pipelines and ML-assisted tools for the ART, SEO, and Development teams. I’m a self-starter with strong research communication skills, focused on shipping solutions that work.

Academically, I am a PhD candidate in Informatics Engineering at FEUP (University of Porto) researching semi-synthetic data asTypes Of Parallelsim In Machine Learning Algorithms a systematic way to analyze and reduce overfitting, including in federated (non-IID) settings. My work combines empirical studies with efficient training pipelines in Python/C++ and OpenMP. I have multiple peer-reviewed publications (IEEE/Elsevier/SCOPUS) and a RosPatent-registered software patent for a neural-network-based optimization program. My goal is to produce methods that improve generalization, robustness, and the responsible use of AI in real applications. I am open to collaboration with researchers and practitioners working in these areas.