Adam Foster
Senior Researcher at Microsoft
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAdam Foster is a Senior Researcher at Microsoft Research AI4Science with around 10 years of experience applying probabilistic machine learning to chemistry, especially scalable Quantum Monte Carlo methods and experimental design. He completed a DPhil in Statistical Machine Learning at Oxford and has a strong track record in Bayesian experimental design, active learning and causal ML from roles at Microsoft, Oxford and internships at Pyro/Uber and BenevolentAI. Adam is a notable contributor to the Pyro probabilistic programming ecosystem, having implemented expected information gain estimators and practical examples for optimal experimental design. He combines rigorous mathematical training (Cambridge MMath, Oxford DPhil) with hands-on engineering experience building tools for large-scale scientific problems, including code and documentation contributions that bridge research and practice. Based in London, he is motivated by deploying ML to understand and accelerate critical reactions like CO2 reduction—bringing both theoretical insight and practical tooling to AI-driven science.
10 years of coding experience
2 years of employment as a software developer
DPhil, Statistical machine learning, Passed with no corrections, DPhil, Statistical machine learning, Passed with no corrections at University of OxfordBachelor’s Degree, BA Mathematics, First (rank 14th), Bachelor’s Degree, BA Mathematics, First (rank 14th) at University of CambridgeHigh School, Mathematics and Sciences, 5 A*'s, High School, Mathematics and Sciences, 5 A*'s at Peter Symonds College
English
Github Skills (11)
pytorch10
machine-learning10
variational-inference10
probabilistic-programming10
python10
bayesian-inference10
pytest9
testing9
bayesian9
deep-learning7
tensorflow3
Programming languages (6)
Github contributions (5)
pyro-ppl/pyro
Jul 2018 - Apr 2020
Deep universal probabilistic programming with Python and PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:132 commits, 32 PRs, 128 pushes in 1 year 8 months
Contributions summary:Adam primarily contributed to the development and enhancement of probabilistic programming models within the Pyro framework, a deep universal probabilistic programming language built on PyTorch. Their work involved implementing and refining various methods for expected information gain (EIG) estimation, including variational inference and nested Monte Carlo approaches. They also added and refined examples, such as the A/B testing model, demonstrating the application of these techniques in optimal experimental design scenarios. Additionally, the user improved documentation and tests to support the implemented functionality.
pytorchpythondeep-learningbayesian-inferenceprobabilistic-modeling
ae-foster/rdbgenerate
Jul 2017 - Jun 2024
Utility for generating Redis dump (.rdb) files from Python native objects
Contributions:5 pushes, 1 branch, 3 tags in 6 years 11 months
databasesencodingrdbredis
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