Jonathan Gordon

Machine Learning PhD Student

University of Cambridge

I am a Ph.D. candidate with the Computational and Biological Learning group at the University of Cambridge, supervised by Dr José Miguel Hernández-Lobato and advised by Dr Richard Turner. My research focuses on developing probabilistic models (typically parameterized by deep neural networks) and associated scalable approximate inference procedures.

I am particularly interested in developing deep probabilistic models that capture structure existing in specific data modalities, and exhibit nice behaviours such as sample and parameter efficiency, generalization, and calibrated uncertainties. Much of my recent work has focused on meta-learning, which enables fast few-shot generalization. Another theme in my research is modelling symmetries in the data, such as by incorporating translation or permutation equivariance.


  • Probabilistic machine learning
  • Meta-learning and few-shot learning
  • Probabilistic Modelling and Inference
  • Symmetries and equivariance in machine learning


  • PhD in Probabilistic Machine Learning, Present

    University of Cambridge

  • MPhil in Machine Learning, 2017

    University of Cambridge

  • MSc. in Applied Statistics and Engineering, 2016

    Ben-Gurion University of the Negev

  • BSc. in Engineering, 2014

    Ben-Gurion University of the Negev