Machine Learning and Artificial Intelligence

The goal of our research in artificial intelligence and machine learning is to understand, represent, model, learn and reason about problems in the real world. We create AI technologies that benefit society and increase social awareness. The theoretical methods we develop and employ span all varieties of deep learning, classical statistical learning, computational biology, knowledge representation, Bayesian inference, causal modelling, logical reasoning, probabilistic reasoning, visual reasoning, stochastic processes, human-like computing, and natural language processing. We apply these methods and approaches in numerous settings such as personalised medicine and education, automated theorem proving, policy work and data, systems applications, sensor networks, and end-to-end solutions in the African context.