About

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.

In my previous studies I examined applications of machine learning to specific domains such as healthcare and manufacturing. Specifically, I examined how machine learning might be useful in prognosis and prediction of ALS progression based on clinical trial data. I also worked on a project to develop optimal sampling strategies for quality assurance processes in semi-conductor manufacturing environments.

In my spare time I like to cook, run, listen and play music, watch movies etc. I also love spending time with the best part of me: my beautiful and perfect wife R.C.

Education

  • Present - PhD in Machine learning – University of Cambridge
  • 2017 - MPhil in Machine Learning, Speech and Language Technology – University of Cambridge (distinction).
  • 2016 - MSc. in Applied Statistics and Engineering – Ben-Gurion University (magna cum laude).
  • 2014 - BSc. in Engineering – Ben-Gurion University (magna cum laude).

Publications

  • Conditional Convolutional Neural Processes

    J. Gordon, W. P. Bruinsma, A. Y. K. Foong, J. Requeima, Y. Dubois, and R. E. Turner, arXiv:1910.13556


  • Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

    J. R. Requima, J. Gordon, J. F. Bronskill, S. Nowozin, and R. E. Turner, NeurIPS 2019 (spotlight) arXiv:1906.07697


  • Bayesian Batch Active Learning as Sparse Subset Approximation

    R. Pinsler, J. Gordon, E. Nalisnick, J. M. H. Lobato, NeurIPS 2019 arXiv:1908.02144


  • Probabilistic Neural Architecture Search

    F. P. Casale, J. Gordon, N. Fusi, arXiv:1902.05116


  • Meta-Learning Probabilistic Inference for Prediction

    J. Gordon, J. F. Bronskill, M. Bauer, S. Nowozin, and R. E. Turner, ICLR 2019 arXiv:1805.09921


  • Consolidating the Meta-Learning Zoo: a Unifying Perspective as Posterior Predictive Inference

    J. Gordon, J. F. Bronskill, M. Bauer, S. Nowozin, and R. E. Turner, Meta-Learning Workshop, NeurIPS 2018



  • Cambridge MPhil thesis

    MPhil thesis on Bayesian adaptations of VAEs and VAE-BNN hybrids (Download)


  • Bayesian Semi-Supervised and Active Learning with Deep Generative Models

    J. Gordon and J. M. H. Lobato, ICML Workshop on Principled Approaches to Deep Learning, arXiv:1706.09751


  • Exposing and Modeling Underlying Mechanisms in ALS with Machine Learning

    J. Gordon and B. Lerner, International Conference on Pattern Recognition, 2016


  • Disease State Prediction, Knowledge Representation, and Heterogeneity Decomposition for ALS

    J. Gordon and B. Lerner, UAI Workshop on Machine Learning in Healthcare, 2016


Awards and Scholarships

  • Research Grant and Studenship

    Funding for my Ph.D. research at the University of Cambridge, generously provided by Samsung ltd., the University of Cambridge, and the CBL.


  • AJA Karten Trust Scholarhip

    Yearly scholarship awarded for outstanding research, generously provided by the AJA foundation for two years.




Contact me

jg801@cam.ac.uk