Portrait of Fernando Otero

Fernando Otero

Senior Lecturer
Deputy Head of School


I have extensive research experience in machine learning and data mining, including the evaluation and interpretation of classification models. My research interests involve the design of new machine learning algorithms that create interpretable models and data pre-processing methods for feature selection and construction. My  recent work has focused on machine learning techniques that incorporate domain knowledge to extract knowledge that is both accurate and useful for the user. I also have expertise in optimising the creation of machine learning models using computational intelligence techniques.

I received my 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. I 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 my BSc in Computer Science from Pontificia Universidade Catolica do Parana (Brazil) in 2002.

Research interests

I belong to the following research groups:

My main research interests include:

  • Data Mining and Knowledge Discovery, in particular classification and regression – focusing on the creation of interpretable models – and more recently clustering
  • Bio-inspired algorithms, mainly ant colony optimization and genetic programming
  • Application of data mining algorithms in bioinformatics (e.g., protein function prediction) and financial forecasting
  • Large-scale data mining ("Big Data")

I am 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.


Current Modules

I teach on (or am otherwise involved with) the following modules:

Past Modules


Fellow of the Higher Education Academy

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