I am a Senior Machine Learning Scientist at Amazon Research Cambridge and a Visitor Researcher of the department of Mathematics and Statistics of Lancaster University. My research focuses on the development of probabilistic machine learning methods for uncertainty quantification and data-efficient sequential decision making. I work on the challenges arising when uncertainty of different types (the loss of precision induced by numerical calculations, data errors, model miss-calibration, etc.) need to be be propagated, controlled and reduced in complex pipelines. As fields of application of my research I am interested in computational biology, health, robotics and environmental sciences.

You may be interested on:

  • The Special Issue in Bayesian optimization that together with With Roberto Calandra, Frank Hutter, Bobak Bobak Shahriari and Roman Garnett we are editing for the Journal of Machine Learning Research.
  • Emukit, a Python platform for emulation and decision making under uncertainty. Try the Emukit-playground!
  • GPyOpt, a Python framework for Bayesian optimization.
  • The series on Gaussian process summer schools that I have helped to organize in Sheffield.