Nicos Angelopoulos

I have conducted research at prestigious UK and Dutch universities and institutes. My area of expertise lies in the intersection of machine learning, artificial intelligence and computational biology. I am interested in theories and the engineering of concrete computational artifacts that can model uncertainty and have strong foundations in probability theory along with computational systems that can reason with and learn such complex models from large data sets.

Furthermore, I am committed to the application of probabilistic artificial intelligence (AI) methods to data analytics of biology, medicine and other applications areas.

I have worked closely with a number of researchers in the biological sciences and have developed both personal contacts and the skills required for conversing with cross-disciplinary colleagues. The constant in my research has been the application of probabilistic AI, with main emphasis on Bayesian machine learning and applied bioinformatics.

I'm a senior computational biologist and AI researcher with over two decades of experience across academia, research institutes, and collaborative biomedical projects. My work brings together probabilistic programming, Bayesian networks, and machine learning, with applications in cancer, immunology, and systems biology. I've led research teams, developed data infrastructure, and contributed to high-impact publications and international collaborations, always with a focus on integrating statistical reasoning with biological insight.

PhD in Computer Science, City University London, UK, 2001

MSc in Advanced Computer science, (AI specialization), Imperial College, London UK, 1993

BSc in Computer Science and Statistics, University of Keele, UK, 1992