Alexander G. de G. Matthews
I am a postdoctoral Research Associate in the University of Cambridge Machine Learning Group. I work with Zoubin Ghahramani with whom I also completed my PhD. I have done work on Gaussian processes. In the area of approximate Bayesian inference, I have worked on variational methods and Markov chain Monte Carlo methods. Most recently, I have worked on probabilistic deep learning.
A full list of my publications can be found on my Google Scholar page.
I am one of the founding developers on GPflow a Gaussian process library built on TensorFlow. I have also contributed C++ linear algebra ops to TensorFlow itself, for which I won a Google open source software award.
My doctoral thesis can be found here.
As an undergraduate I studied Natural Sciences at the University of Cambridge, specialising in theoretical physics. My fourth year project, which was later published, studied scattering in the fractional quantum Hall effect with Nigel Cooper. After that I worked in industry for Navetas Energy Management, a University of Oxford spin-out company which applies machine learning to the problem of home energy disaggregation.