Frank Laub
Co-founder & CTO at RISC Zero
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolFrank Laub is a seasoned software leader and co-founder/CTO with 11+ years focused on deep learning compilers, ML frameworks, and verifiable computing, based in Seattle. He led technical work at RISC Zero building zero-knowledge verifiable compute infrastructure and previously drove deep learning compiler and inference software at Intel Movidius and Intel Labs. Frank’s hands-on contributions to open source include improving PlaidML’s ComplexConv2d test and integrating an experimental PyTorch-PlaidML bridge, plus build-system and pure-Rust verifier work for the widely noted RISC Zero project. He excels at bridging low-level build and serialization engineering with high-level ML system design, shipping reproducible, performant tooling for production workloads. Colleagues would describe him as an engineer who moves between compiler internals, build engineering, and ML model execution with rare breadth and practical focus. Trained in computer software engineering, he combines startup grit with enterprise-grade delivery across security-sensitive and performance-critical domains.
11 years of coding experience
24 years of employment as a software developer
Computer Software Engineering, Computer Software Engineering at Oregon Institute of Technology
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
machine-learning10
verification10
build-system10
c-language10
rust10
cargo10
cprogramming-language10
risc-v9
deep-learning9
deep-q-learning8
pytorch8
Programming languages (18)
Github contributions (5)
risc0/risc0
Feb 2022 - Jan 2023
RISC Zero is a zero-knowledge verifiable general computing platform based on zk-STARKs and the RISC-V microarchitecture.
Role in this project:
Back-end Developer & Build Engineer
Back-end Developer & Build EngineerContributions:42 releases, 2099 reviews, 136 commits in 11 months
Contributions summary:Frank contributed primarily to the build process and core functionality of the RISC Zero project. Their commits include adding support for building with cargo, including Rust guest binaries, implementing C++ serialization, and improving the method ID generation process. They also incorporated a pure-rust implementation of the verifier. The changes indicate a focus on build system integration and improving the performance and reliability of the verification process.
risc-vcryptographyverifiablestarkszero
plaidml/plaidml
Oct 2017 - Feb 2022
PlaidML is a framework for making deep learning work everywhere.
Role in this project:ML Engineer
ML EngineerContributions:1 release, 721 reviews, 833 commits in 4 years 4 months
Contributions summary:Frank contributed to the PlaidML deep learning framework by implementing a complex convolution test. The changes involved creating a new test case for the `ComplexConv2d` function, which is likely used for deep learning operations. Furthermore, they replaced `tile_ret_` with a `tile_error*` out parameter and incorporated a new experimental PyTorch-PlaidML bridge.
pytorchtvmdeep-learningmachine-learningcompiler
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