Date: 24 February
Time: 1 p.m.
Venue: PA2 Amphitheatre, Mathematics Building, Alameda Campus

Speaker: Teresa Salazar (Priberam Labs)
Title: “Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning”

Abstract:

Machine learning models can become unfair when different groups experience changes in data over time, a phenomenon called group-specific concept drift. This challenge is amplified in Federated Learning, where clients may encounter different drifts while training a shared model. This talk introduces the problem and presents a distributed, group-aware approach that detects and adapts to such drifts, helping maintain fairness in dynamic environments.

Speaker bio:

Teresa Salazar is an ML/NLP Researcher at Priberam, where she conducts applied research on Natural Language Processing in an industrial setting. She holds a B.Sc. in Informatics Engineering from the University of Coimbra (2018), an M.Sc. in Informatics from the University of Edinburgh (2019), and a PhD from the University of Coimbra (2025), where her research focused on fairness in machine learning. In parallel with her industry work, she serves as an Invited Professor at the University of Coimbra.

Priberam is a member of the IST Spin-Off Community®.

The Priberam Machine Learning Lunch Seminars are free of charge. Prior registration is required.

More information and registration.