Ayman Boustati
Senior Applied Scientist at Microsoft
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolAyman Boustati is a Machine Learning Scientist at Amazon with 11 years of experience bridging probabilistic research and applied ML. He earned a PhD in probabilistic machine learning from the University of Warwick and translated that expertise into product-focused roles at causaLens and Amazon, working on causal and predictive systems. His background includes research stints at The Alan Turing Institute and industry collaborations (PROWLER.io), reflecting a strong track record of moving novel probabilistic methods into real-world deployments. He combines deep probabilistic modeling, operational research and statistics with practical engineering to deliver interpretable, production-ready ML. Based in London, he has a habit of pairing academic rigor with startup-speed iteration, making him equally comfortable publishing papers and shipping features.
11 years of coding experience
10 years of employment as a software developer
Summer Programme, Summer Programme at University of California, BerkeleyHigher Education Foundation Programme Science and Engineering, Higher Education Foundation Programme Science and Engineering at Warwickshire CollegeHigh School Diploma, High School Diploma at International School of Choueifat, DamascusDoctor of Philosophy (Ph.D.) Mathematics for Real-World Systems, Doctor of Philosophy (Ph.D.) Mathematics for Real-World Systems at University of Warwick
English, Arabic, French
Github Skills (13)
bayesian-statistics10
variational-inference10
stochastic-processes10
hierarchical10
gpflow10
gaussian-processes10
tensorflow9
machine-learning9
deep-learning9
ml8
jekyll5
reproducible-research3
slide1
Programming languages (3)
Github contributions (5)
aboustati/robust-smc
Oct 2019 - Jun 2020
Contributions:27 commits in 7 months
aboustati/dgplib
Nov 2017 - Sep 2019
Library for Deep Gaussian Processes based on GPflow
Contributions:156 commits, 18 PRs, 96 pushes in 1 year 9 months
gaussian-processesgpflowdeep-gaussian-processeshierarchical
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