AndreasĀ Damianou
Senior Research Manager & Lab Lead at Spotify
Greater Cambridge Area United Kingdom
Summary
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Top SchoolAndreas Damianou is a Senior Research Manager and Lab Lead in Greater Cambridge with 15 years of experience building and scaling ML-driven search and recommendation systems. He leads a lab at Spotify focused on representation learning that blends Graph ML and LLMs to improve discovery for talk audio and explore generative listening experiences. His background spans industry research roles at Amazon and Spotify, a PhD in Machine Learning from Sheffield, and hands-on startup experience as a co-founder, giving him both deep research expertise and product-facing delivery instincts. An active contributor to Gaussian process tooling (notably improvements to the popular GPy framework), he combines Bayesian modelling strengths with modern deep representation techniques. Colleagues know him for translating complex probabilistic ideas into scalable systems and for mentoring cross-disciplinary teams at the intersection of ML research and product.
15 years of coding experience
10 years of employment as a software developer
MSc in Informatics Artificial Intelligence Machine Learning, MSc in Informatics Artificial Intelligence Machine Learning at The University of EdinburghBSc Informatics and Telecommunications, BSc Informatics and Telecommunications at Ethnikon kai Kapodistriakon Panepistimion AthinonPhD Machine Learning, PhD Machine Learning at The University of Sheffield
Stackoverflow
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Github Skills (6)
gpy10
machine-learning10
python10
numpy10
gaussian-processes10
dimensionality-reduction9
Programming languages (4)
Github contributions (5)
SheffieldML/GPy
Jul 2013 - Oct 2015
Gaussian processes framework in python
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:21 commits, 1 push, 16 comments in 2 years 2 months
Contributions summary:Andreas contributed to the GPy framework, primarily focusing on kernel implementations and improvements. Their work included debugging and enhancing the RBF inverse kernel, as well as integrating it into demonstrations like the BGPLVM stick demo. The commits also show changes related to regression tasks and the SVIGP model, indicating a focus on Bayesian and sparse Gaussian process models.
gaussiangaussian-processespython
adamian/adamian.github.io
Jan 2016 - Dec 2022
Contributions:144 commits, 144 pushes, 1 branch in 6 years 11 months
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