Zach Maas
Boulder, Colorado, United States
Summary
👤
Senior🎓
Top SchoolZach Maas is an independent mechanistic interpretability researcher based in Boulder with nine years of experience bridging interpretable ML and domain science. He focuses on improving sparse autoencoder (SAE)-based tools and graphical methods to extract circuit-like structures from large language models. Zach completed a PhD applying interpretable ML, transformers, and MCMC normalization techniques to genomic and transcriptional sequencing problems, bringing rigorous experimental design to model introspection. His undergraduate training in chemistry and mathematics informs a quantitative, systems-first approach to mechanistic questions. Now freelancing, he translates research-grade interpretability methods into practical analysis workflows for LLMs. He combines deep domain expertise from genomics with active contributions to ML interpretability techniques that emphasize visual, circuit-centered explanations.
9 years of coding experience
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Colorado Boulder
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (14)
factor9
transcription8
emacs6
pipeline5
owl3
rna-seq3
linked-open-data3
spacemacs3
nextflow2
ontologies2
semantic-web2
seq1
python1
knowledge-graph1
Programming languages (4)
Github contributions (5)
zmaas/.emacs.d
Apr 2017 - Jan 2022
My Emacs configuration. Based around Evil, and constantly changing.
Contributions:279 commits, 2 PRs, 63 pushes in 4 years 10 months
emacs
zmaas/evil-unimpaired
Oct 2017 - Jan 2020
Port of evil-unimpaired code from spacemacs, for general evil use.
Contributions:11 commits, 3 PRs, 7 pushes in 2 years 3 months
spacemacs
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