I hold a bachelor’s degree in Mechanical Engineering and a master’s degree in Computational Modelling, both from the Federal University of Juiz de Fora (UFJF). My research interests lie at the intersection of pattern recognition, machine learning, and applied data science.
Throughout my academic and professional journey, I have actively contributed to multidisciplinary projects, which have broadened my understanding of complex problem-solving and fostered a collaborative mindset. These experiences have reinforced my passion for utilising computational tools and data-driven approaches to address challenges across diverse fields.
Project Information
Programme: Federated Analytics
Project Title: Anomaly Detection in Federated Learning in Healthcare
Summary:
The use of machine learning (ML) for anomaly detection has become increasingly important, enabling process optimisation and facilitating early diagnosis. However, concerns about privacy, security, and data fragmentation make it difficult to access large sets of real-world data. As a result, a major challenge arises in the context of data mining and ML, as these techniques often require substantial amounts of data for training.
In this context, federated learning (FL) has emerged as a promising technique for addressing these issues, as its decentralised approach allows machine learning models to be trained locally on multiple users’ devices without the need to share raw data directly, thus ensuring individual privacy and preventing data leakage. Additionally, by enabling training to occur on users’ devices, federated learning fosters collaboration between individuals and organisations in a distributed manner, leveraging the potential of data while safeguarding privacy and information security.
