Fernando has extensive research experience in machine learning and data mining, including the evaluation and interpretation of classification models. His research interests involve the design of new machine learning algorithms that create interpretable models and data pre-processing methods for feature selection and construction. His recent work has focused on machine learning techniques that incorporate domain knowledge to extract knowledge that is both accurate and useful for the user. He also has expertise in optimising the creation of machine learning models using computational intelligence techniques.
Fernando received his PhD in 2010 from the University of Kent, working on ant colony optimisation (ACO) algorithms for data mining, in particular the creation of ACO algorithms for the bioinformatics problem of predicting protein functions. He was a Post-Doctoral Research Associate working on the EPSRC-funded project "Refactoring and Neutrality in Genetic Programming" (EP/H020217/1) at the University of Kent (2010-2012) and obtained his BSc in Computer Science from Pontificia Universidade Catolica do Parana (Brazil) in 2002.
Fernando belongs to the following research groups:
Fernando's main research interests include:
Fernando is always happy to discuss research ideas with potential research students and postdocs. In terms of funding, the School of Computing runs an annual PhD scholarship competition. The University has also put together a useful list of funding opportunities for postdoctoral research.
I teach on (or am otherwise involved with) the following modules:
Fellow of the Higher Education Academy