Bradley Gram-hansen
Senior Applied Scientist at Apple
Seattle, Washington, United States
Summary
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Senior🎓
Top SchoolBradley Gram-hansen is a Senior Applied Scientist in Apple's AIML organization with nine years of experience building probabilistic and deep learning systems that turn noisy, large-scale data into actionable signals. He earned a DPhil in Machine Learning from Oxford and has published at NeurIPS, ICML, AISTATS and AAAI, blending deep generative models, Bayesian statistics, and simulation in both research and production. Before Apple he spent nearly four years at Dataminr developing and deploying models for early detection of high-impact events and co-founded Intelligent Networks to create probabilistic physics simulators for utility infrastructure. Comfortable moving models from prototype to MLOps at scale, he has a track record of working directly with customers and partners—including presenting applied research to stakeholders like UNICEF—while still describing his GitHub bio simply as “Tinkering,” a hint at a pragmatic, hands-on curiosity.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (DPhil) Machine Learning, Doctor of Philosophy (DPhil) Machine Learning at University of OxfordMaster's degree with honours in Mathematics Mathematics, Master's degree with honours in Mathematics Mathematics at University of Nottingham
French, Italian, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (44)
org-mode9
simulation9
markdown9
graph9
probabilistic9
knowledge-graph9
clojurescript8
trello8
clojure8
probabilistic-models8
system-programming8
probabilistic-programming7
graphical-models7
machine-learning7
knowledge-management7
Programming languages (5)
Github contributions (5)
bayesianbrad/Machine-Learning-and-Python-for-Beginners
Nov 2019 - Dec 2019
Contributions:31 commits, 27 pushes, 1 branch in 1 month
bayesianbrad/pyfo
Nov 2017 - Sep 2018
FOPPL-HMC interface for performing inference in directed graphical models
Contributions:642 commits, 2 PRs, 30 pushes in 10 months
inferencegraphical-modelsmachine-learninggraphicaldirected
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