Machine Learning
Federated learning
Distributed learning scenarios where data remains close to its source while models or updates are coordinated across participants.
Research
Artificial Intelligence research under privacy, distribution, and continuous change.
My research sits at the intersection of privacy-preserving machine learning and data stream mining: learning from continuously arriving, non-IID data under concept drift while keeping raw data protected through federated learning and homomorphic encryption. Most PPML work assumes static datasets; my focus is the online, drifting, resource-constrained regime that regulated enterprise data actually presents.
Research themes
Privacy
Homomorphic encryption
Privacy-preserving computation patterns that enable selected operations over encrypted data, with explicit attention to performance tradeoffs.
Adaptive ML
Data stream mining
Learning from continuously arriving data under drift, latency, memory, non-IID distribution, and adaptation constraints.
Applied Research
Enterprise applications of privacy-preserving ML
Research framing for regulated domains where collaboration, confidentiality, and practical deployment constraints must coexist.
Research statement
My academic training in Artificial Intelligence has been developed at PUCPR, from postgraduate studies through an MSc and my current PhD research.
Building on that academic path, the research connects cryptographic and privacy-enhancing techniques with adaptive machine learning algorithms for secure training and inference in distributed environments.
It considers supervised learning in continuous data streams, including concept drift, non-IID data, incremental model adaptation, and deployment constraints in regulated or data-sensitive domains.
I am seeking a doctoral research visit (sandwich) abroad to advance this work on privacy-preserving learning over evolving data streams. I bring access to real, regulated, privacy-sensitive enterprise data and hands-on experience deploying AI in mission-critical systems — a complement to strong academic PPML groups.
Service, teaching, recognition, and languages
Associate member of the Brazilian Computer Society (SBC) since 2024. Teaches at PUCPR. Silver medal, Brazilian Informatics Olympiad. Full professional proficiency in English and Spanish; native Portuguese.