Machine Learning and Optimization
Machine Learning (ML) is a branch of Artificial Intelligence dedicated to developing new algorithms and methodologies capable of identifying patterns and making decisions without explicit programming. Beyond practical applications, progress in this field depends on creating novel theoretical and computational approaches that enhance the efficiency, interpretability, and generalization capacity of models.
This research project investigates advanced ML methods, spanning traditional techniques, such as deep neural networks and probabilistic models, to emerging approaches including self-supervised learning, generative models, federated learning, and reinforcement learning. Additionally, the project aims to improve strategies for explainability and interpretability to make models more transparent and trustworthy, especially in critical applications.
A second fundamental pillar of this project is Optimization, a field that integrates with ML to improve model performance and solve complex problems across different domains. The project focuses on the design and application of methods for solving problems using linear, nonlinear, integer, and mixed-integer programming (through exact and/or heuristic methods), as well as bio-inspired metaheuristics such as ant colony optimization, genetic algorithms, and particle swarm optimization. Optimization techniques are applied to tasks such as tuning machine learning model parameters, feature selection, and neural network architecture design.
Finally, Affective Computing explores how ML algorithms can interpret, process, and respond to human emotional states. This includes investigating new methods for fusing physiological and emotional signals. The goal is to advance the development of systems capable of adapting their responses in more natural and empathetic ways, with applications ranging from conversational interfaces to interactive robotics.
Faculty Members Involved:
- Eduardo Bezerra da Silva (coordinator)
- Gustavo Paiva Guedes e Silva
- Diogo Silveira Mendonça
- Diego Moreira de Araújo Carvalho
- Laura Silva de Assis