I am a Principal Researcher at Microsoft Research Cambridge. 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. I am also interested on how causal inference can be used to leverage decision making methods and to improve the understanding of complex systems and processes. As fields of application of my research I am interested in computational biology, health 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.