My name is Carl Henrik Ek and I am a Associate Professor in the Computer Laboratory at the University of Cambridge, UK. and a Docent in Machine Learning at the Royal Institute of Technology, Sweden.

Learning is the task of associating a new phenomena to previous knowledge. Knowledge is the capability of providing structure to the environment. In the field of machine learning we try to build methods that are capable of learning from data. The fundamental aspect of learning is assumptions, being the realisation of knowledge, the science of machine learning is concerned with how to formulate assumptions into mathematics (modelling) and how to related them to observed data (inference). My research focus spans both these areas, in specific I am interested in how we can specify data efficient and interpretable assumptions that allows us to learn from small amounts of data. Most of my work is focused on Bayesian non-parametric methods and in specific Gaussian processes.