About

My name is Carl Henrik Ek and I am a Senior Lecturer in the Computer Laboratory at the University of Cambridge a visiting Professor at Karolinska Institute in Stockholm and a Docent in Machine Learning at the Royal Institute of Technology, Stockholm.

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.

Short Bio Before joining the Computer Lab in Cambridge I was a Senior Lecturer at the University of Bristol, prior to this I was an Assistant Professor in Machine Learning at the Royal Institute of Technology (KTH) in Stockholm. I did my postdoctoral research at University of California at Berkeley and my PhD is from Oxford Brookes University. I spent two years of my PhD at the University of Manchester where I was a research assistant in the Machine Learning and Optimisation group and a further six months at University of Sheffield as a visitor in the Machine Learning group. My supervisors during my PhD where Professor Neil Lawrence and Professor Phil Torr and during my post-doc I worked under Professor Trevor Darrell and Professor Raquel Urtasun. My undergraduate degree is a MEng degree in Vehicle Engineering from the Royal Institutie of Technology in Stockholm where I worked with Professor Stefan Carlsson for my dissertation.

PhD Applications 24/25 I am not looking to take on any PhD students starting the academic year 24/25.