Dean Thomasson is a software engineer based in Austin, Texas with 11 years of experience building reliable infrastructure and deployment tooling. He currently contributes to GenAI-focused development and has hands-on expertise in DevOps for complex open-source projects, notably helping maintain IBM's Fully Homomorphic Encryption toolkit for Linux. His work on that toolkit included cross-architecture scripting (x86_64/amd64 and s390x), build automation, and packaging updates—showing a knack for bridging research-grade crypto demos into reproducible developer environments. A Texas State University-trained computer engineer, Dean pairs systems-level thinking with practical scripting and containerization skills. Colleagues rely on him to simplify heavyweight technical stacks into maintainable pipelines that enable secure, privacy-preserving ML experiments.
11 years of coding experience
B.S., Computer Engineering, B.S., Computer Engineering at Texas State University-San Marcos
IBM Fully Homomorphic Encryption Toolkit For Linux. This toolkit is a Linux based Docker container that demonstrates computing on encrypted data without decrypting it! The toolkit ships with two demos including a fully encrypted Machine Learning inference with a Neural Network and a Privacy-Preserving key-value search.
Role in this project:
DevOps Engineer
Contributions:13 reviews, 16 commits, 16 PRs in 5 months
Contributions summary:Dean's contributions primarily focused on building and maintaining the build and deployment infrastructure for the IBM FHE toolkit. They added build arguments, implemented a root/sudo check, and updated the NTL version. Furthermore, the user implemented scripts to fetch, start, and stop HELayers labs, encompassing both x86_64/amd64 and s390x architectures. Finally, the user switched the available lab types to cpp for selection.
IBM Fully Homomorphic Encryption Toolkit For Linux. This toolkit is a Linux based Docker container that demonstrates computing on encrypted data without decrypting it! The toolkit ships with two demos including a fully encrypted Machine Learning inference with a Neural Network and a Privacy-Preserving key-value search.
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