Joseph Bloom

Model Transparency Lead at AI Security Institute

London, England, United Kingdom
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Summary

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Joseph Bloom is a Model Transparency Lead in London with five years' experience at the intersection of machine learning, mechanistic interpretability, and computational biology. He has led white-box evaluations of frontier AI systems at the AI Security Institute, co-founded and served as science lead at a research startup, and published proteomics work that produced a novel protein-inference solution. Equally comfortable writing production data pipelines and research code, he blends hands-on engineering (Python/R, Docker, AWS) with academic rigor from a Computational Biology and Mathematical Sciences background. An aspiring effective altruist, Joseph focuses on reducing extreme AI risks through interpretability techniques that probe models' internal states rather than just inputs and outputs. A less obvious thread through his career is a consistent drive to translate deep scientific work into practical, auditable tools and published outcomes.
code5 years of coding experience
job6 years of employment as a software developer
bookMasters of Business Analytics - Differed, Masters of Business Analytics - Differed at Melbourne Business School
bookVCE, VCE at Leibler Yavneh College
bookThe University of Melbourne
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Stackoverflow

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Github Skills (37)

grid-world10
language-model10
gymnasium10
reinforcement-learning10
autoencoder10
gpt10
padding9
bioinformatics9
python8
enums8
union8
keras7
rust7
hyperparameter-tuning6
data-analysis6

Programming languages (5)

RRustHTMLJupyter NotebookPython

Github contributions (5)

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A python package for protein inference in Mass Spectrometric data analysis.
Contributions:72 commits, 51 pushes, 1 branch in 1 year 6 months
data-analysisinferencepythonbioinformaticslc-msms
MassDynamics/lfq_processing

Mar 2021 - Jul 2022

Post-processing of MaxQuant Label Free Quantification results.
Contributions:31 releases, 100 commits, 56 pushes in 1 year 4 months
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