My current research work is mainly focused on explainability and fairness in machine learning, particularly the trade-off of fairness-performance that arises when we try to mitigate unfairness.
Previously, I worked on the following topics: semi-supervised clustering, preference mining, and recommender systems.
· MSc and BSc in CS, UFU 🇧🇷 (sup. S. de Amo, M. C. Barioni)
📢 Open for work: I am seeking new opportunities where I can contribute my expertise in explainability and fairness in machine learning to drive impactful projects. If you know of any roles or collaborations that align with my skills, I’d love to connect!
Algorithmic inspection for ML/DL models
Links: Academic website | Tool website
What’s new
- [Feb 24] Presenting at Forum Industriel de l’AfIA, Paris
- [Oct 23] Presenting at Journées du CIS, Paris
- Survey on Fairness Notions and Related Tensions. EURO Journal, 2023
- Reducing Unintended Bias of ML Models on Tabular and Textual Data. DSAA, 2021
G. Alves 