Dionysis Manousakas
Applied Scientist II at Amazon Web Services (AWS)
New York, New York, United States
Summary
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Senior🎓
Top SchoolDionysis Manousakas is an applied scientist with a decade of experience specializing in scalable inference for deep probabilistic models, currently advancing large-scale ML systems at AWS. He holds a PhD in Computer Science from Cambridge and a Distinction MSc in Machine Learning from UCL, blending rigorous research on automated Bayesian inference, privacy, and robustness with production-minded deployment. Previously he translated academic advances into practice at Meta and contributed to temporal and spatiotemporal modeling during internships at Max Planck and Nokia Bell Labs. Comfortable mentoring and teaching ML courses, he pairs hands-on probabilistic modelling expertise with a track record of shipping inference methods that scale to real-world, privacy-sensitive applications.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of CambridgeDipl.-Ing., Electrical & Computer Engineering, Dipl.-Ing., Electrical & Computer Engineering at National Technical University of AthensUniversity College London
English, German, Spanish, Greek
Github Skills (150)
python10
pot10
lightning10
artificial-intelligence10
pytorch10
lifecycle10
statistics9
ai9
probabilistic-programming9
deep-learning9
infrastructure9
bayesian-methods9
transport9
pattern-recognition9
scaling9
Programming languages (4)
Github contributions (5)
dionman/thesis
Aug 2020 - Sep 2021
Contributions:214 commits, 132 pushes in 1 year
dionman/beta-cores
Jun 2020 - Dec 2021
Coresets for scalable robust pseudo-Bayesian inference
Contributions:120 commits, 7 PRs, 28 pushes in 1 year 6 months
scalableoutlier-removalbayesian-inferenceinferencecoreset
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