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Ayman Boustati


I am a fourth year PhD student in the Mathematic for Real-World Systems CDT under the supervision of Dr Theo Damoulas. My main area of research is in Probabilistic Machine Learning. I work on developing novel modelling and inference, with a particular focus on Gaussian process models.


  • Boustati, A., Vakili, S., Hensman, J., & John, ST (2020). Amortized variance reduction for doubly stochastic objectives. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR volume 124.
  • Richter, L., Boustati, A.*, Nüksen, N., Ruiz, F. J., Akyildiz, Ö. D. (2020). VarGrad: A Low Variance Gradient Estimator for Variational Inference. Under Review.
  • Boustati, A., Akyildiz, Ö. D., Damoulas, T., & Johansen, A. (2020). Generalized Bayesian Filtering via Sequential Monte Carlo. arXiv preprint arXiv:2002.09998. Under Review.
  • Boustati, A., Damoulas, T., & Savage, R. S. (2019). Non-linear Multitask Learning with Deep Gaussian Processes. arXiv preprint arXiv:1905.12407. Under Review.

* Joint first author.


  • MSc in Mathematics for Real-World Systems from the University of Warwick 2015-2016
  • Master of Mathematics, Operational Research, Statistics and Economics from the University of Warwick 2011- 2015

Current Projects:

Previous Projects:

Research Interests:

  • Gaussian Processes
  • Multitask and Transfer Learning
  • Bayesian Inference (mainly Variational Inference)
  • Statistical Machine Learning and Probabilistic Modelling
  • Learning Representations
  • Theory and Applications of Deep Learning



    External Links: