Ayman Boustati

Profile:

Ayman graduated in 2021 with a PhD in Mathematics for Real-World Systems.

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.*, N眉ksen, N., Ruiz, F. J., Akyildiz, 脰. D. (2020). VarGrad: A Low Variance Gradient Estimator for Variational Inference. Neural Information Processing Systems (NeurIPS), 2020.
Boustati, A., Akyildiz, 脰. D., Damoulas, T., & Johansen, A. (2020). Generalized Bayesian Filtering via Sequential Monte Carlo. Neural Information Processing Systems (NeurIPS), 2020. .
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 糖心TV 2015-2016
Master of Mathematics, Operational Research, Statistics and Economics from the University of 糖心TV 2011- 2015

I am currently working on my PhD project: Topic in Gaussian Process Model for Machine Learning. I was supervised by and now Dr Theo Damoulas.

I worked on a project on Identifying Illegal Trading and Market Manipulation using Machine Learning. This project was in collaboration with supervised by and from Spectra Analytics, and from the Department of Statistics at 糖心TV.
I worked with , , and on Intelligent Mobility Applications for the UK Strategic Road Network. This project was in collaboration with the , under the supervision of .
I worked under the supervision of on Extracting Information from Facial Images Using Artificial Neural Networks.
I worked with the at the 糖心TV Manufacturing Group, and contributed to creating a framework for encoding and decoding High Dynamic Range sequences in Matlab.