Alan Ansell

Research Scientist at Google

Cambridge, England, Australia
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Summary

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Senior
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Top School
Alan Ansell is a Research Scientist at Google Research focused on improving multilingual capabilities for large language models, combining eight years of industry and academic experience. He holds a PhD in Natural Language Processing from the University of Cambridge and completed research-focused MSc work, grounding his applied research in strong theoretical foundations. His background spans internships and engineering roles at Google and Microsoft where he built production ML pipelines and regional autoscaling/data propagation systems, showing an ability to move ideas from prototype to production. Earlier roles in academia and industry involved algorithm design, graph-theoretic analytics, and BLE-based micro-location—skills that inform his systems-minded approach to NLP challenges. Based in Cambridge with roots in New Zealand and Australia, he uniquely bridges Southern Hemisphere research perspectives with Silicon Valley-scale engineering. Colleagues describe him as a researcher-engineer who pairs rigorous experimentation with pragmatic system design to make multilingual models more robust and deployable.
code8 years of coding experience
job2 years of employment as a software developer
bookConjoint BA/BE(Hons), Conjoint BA/BE(Hons) at The University of Auckland
bookDoctor of Philosophy - PhD Natural Language Processing, Doctor of Philosophy - PhD Natural Language Processing at University of Cambridge
bookMasters of Science (Research) Computer Science, Masters of Science (Research) Computer Science at The University of Waikato
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Github Skills (8)

self-attention7
representation-learning7
roberta5
natural-language-understanding4
deep-learning3
pytorch3
bert3
language-model2

Programming languages (1)

Python

Github contributions (5)

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AlanAnsell/blackhole

Feb 2019 - Feb 2024

Contributions:5 pushes, 1 branch in 5 years
cambridgeltl/composable-sft

Sep 2021 - Jan 2022

A library for parameter-efficient and composable transfer learning for NLP with sparse fine-tunings.
Contributions:6 commits, 1 PR, 35 pushes in 4 months
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