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.
I use Emacs every day. I rarely notice it. But when I do, it usually brings me joy.
– Norman Walsh
Seeing Emacs as an editor is like seeing a car as a seating-accomodation
– Karl Voit
For as long as humanity doesn’t collapse and probably even after it does
org mode and
emacs will be used (there is going to be some nerd somewhere using
org mode and ledger cli to meticulously track how many smoked rats and cockroach kebobs they have left to eat before they have to leave their bunker), there is just such an intense critical mass of utility under an open source license.
- Started as a visiting Professor in Machine Learning at Karolinska Institute, Stockholm, Sweden
- Started as a Senior Lecturer in Machine Learning in the Computer Laboratory of the University of Cambridge.
- Organising the workshop Uncertainty Propagation in Composite Models together with Markus Kaiser and Neill Campbell
- Webinar titled Free lunch? What allows us to learn from data?
- Gave a lecture on Unsupervised Learning using Gaussian Processes at the Gaussian Process Summer School The video of the talk can be found here
- Invited talk on Composite Bayesian Modelling at University of Exeter
- Tutorial on Bayesian modelling at Siemens, Munich
- Lecture and Panel debate on AI, Media and our obsession with Data
- Gave a tutorial title Bayesian Non-parametrics and Priors over Functions at Vicarious, Bay Area.
- Gave a tutorial titled Bayesian Non-parametrics and Priors over Functions at Imperial College London.
- Gave a lecture on Intelligent Machines for Unionen a Swedish union for executives.
- Gave a lecture on Unsupervised Learning using Gaussian Processes at the Gaussian Process and Uncertainty Quantification Summer School The video of the talk can be found here
- Gave 3 lectures on Machine Learning at the Estonian Summer School on Computer and Systems Science titled Assumptions, Models and Inference.
- Gave a 1 day course on machine learning for developers for Peltarion in Stockholm
- Gave seminar on AI for WiseIT in Stockholm
- Gave a seminar for Partsrådet titled Digitalisation, Learning and the value of information
- Awarded Teacher of the year in Computer Science at University of Bristol
- Gave open lecture on Artificial Intelligence and the Future of Employment
- Gave a workshop on Thinking Machines in a Digital World for Partsrådet
- Organising Workshop on Learning Representations at Intelligent Vehicles in Gothenburg with Trevor Darrell and Erik Rodner. I am also a Associate Workshop Editor for the conference.
- New paper titled Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis available on arXiv
- Awarded the degree of Docent in Machine Learning at Royal Institute of Technology, KTH, Stockholm, Sweden
- Acted as opponent for Ekaterina Kolycheva defending her thesis Grasp planning under uncertainty.
- I gave a TEDx talk titled “Why I do not fear Artificial Intelligence” you can find the Video of the talk here.
- Interview (in Swedish) for Campi magazine URL
- Awarded Teacher of the year at Royal Institute of Technology, Sweden motivation
- Awarded Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology
- Interview (in Swedish) about teaching in national Swedish student magazine Shortcut URL
- Awarded Teacher of the year in Computer Science at Royal Institute of Technology