School of Computing

Staff Profiles

Dr Jaume Bacardit

Reader in Machine Learning


I am a Reader in Machine Learning, appointed in 2014. I am affiliated to the Interdisciplinary Computing and Complex BioSystems (ICOS) research group.

I received a BEng and MEng in Computer Engineering and a PhD in Computer Science from the Ramon Llull University in Barcelona, Spain in 1998, 2000 and 2004, respectively.

My PhD thesis involved the adaptation and application of a class of rule-based machine learning methods called Learning Classifier Systems to Data Mining tasks in terms of scalability, knowledge representations and generalisation capacity.

From 2005 to 2007 I was a postdoc at the University of Nottingham working on Protein Structure Prediction

From 2008 to 2013 I was a Lecturer in Bioinformatics at the University of Nottingham.


My research interests include the development of machine learning methods for large-scale problems and their application to challenging problems, mostly involving biological data.

I have published papers on algorithmic advances to improve the scalability of machine learning methods, tackling challenges such as large dimensionality spaces, large sets of records, postprocessing operators or the use of data-intensive computing technologies such as GPUs and MapReduce.

The main focus of my applied research on biological data is knowledge discovery: analyzing the structure of the data mining models to discover useful knowledge, such as (panels of) biomarkers or functional networks and in this way bring the data mining process closer to the domain experts. My methods have been applied to a variety of biological/biomedical domains: the proces of germination in plants, cancer in humans or osteoarthitis both in humans and model organisms and multiple data-generating biotechnologies: transcriptomics, proteomics, lipidomics, etc.

Currently I lead the data mining efforts of the D-BOARD FP7 project that has as objective the discovery of novel biomarkers for Osteoarthitis. This project generates data of many different types, and the data mining is central to integrate all this heterogeneous information and distill biomarkers with diagnostic and prognostic power.