Machine Learning Foundations

This field is concerned with analysing, modifying, and synthesising signals such as sound, images, sensor readings, or radio waves, applied to communications, healthcare and medicine, and biomedical research. 

Key researchers: Mario Figueiredo

We use data to help us understand and model cause-and-effect relationships between variables, with important real-life applications including in healthcare and medicine, biomedical research, technology, environment and climate science, and others. 

Key researchers: Mario Figueiredo

We develop algorithms and systems that can automatically deduce logical conclusions from a set of premises or facts, using computational methods to solve problems involving logic and reasoning, thus enabling machines to make decisions, prove theorems, and deduce new knowledge from existing information. 

Key researchers: Inês Lynce, Alessandro Gianola

Our research concerns adjusting model parameters (like weights in a neural network) to minimise or maximise an objective function — typically a loss function that measures how well the model is performing. Our research focuses on constraint satisfaction, Boolean optimisation.

Key researchers: Inês Lynce, Mario Figueiredo, Vasco Manquinho

Discovering patterns, trends, correlations, and useful information from large datasets using various analytical techniques, statistical methods, and machine learning algorithms, in order to extract valuable insights from raw data that would be difficult to find through traditional querying or manual analysis. 

Key researchers: Alessandro Gianola

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Key researchers: Arlindo Oliveira