Manuel Drehwald
PHD Student at University of Toronto
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolManuel Drehwald is a PhD student at the University of Toronto with six years of experience at the intersection of machine learning, automatic differentiation, and high-performance computing. He has contributed core autodiff functionality to the Rust compiler and to the high-profile Enzyme project, integrating LLVM-based AD into production toolchains and bringing GPU support to Rust during internships at Lawrence Livermore. Manuel combines rigorous math and CS training from KIT and Göttingen with practical systems work—optimizing numerical kernels, building Rust tooling, and automating lab workflows with PyTorch. Notably, his work spans both algorithmic AD research and low-level compiler integration, giving him rare expertise that bridges models, hardware, and compiler toolchains.
6 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of TorontoComputer Science, Computer Science at Georg-August-Universität GöttingenBachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at Karlsruher Institut für Technologie (KIT)
English, Spanish, German
Github Skills (24)
c-language10
llvm10
compiler-development10
ndarray10
numerics10
automatic-differentiation10
compiler-compiler10
compiler10
rust10
enzyme10
cprogramming-language10
numeric10
build-time9
buildx9
build-engine9
Programming languages (15)
Github contributions (5)
EnzymeAD/Enzyme
Sep 2021 - Jan 2023
High-performance automatic differentiation of LLVM and MLIR.
Role in this project:
Back-end Developer & Algorithm Optimization Engineer
Back-end Developer & Algorithm Optimization EngineerContributions:200 reviews, 181 commits, 240 PRs in 1 year 4 months
Contributions summary:Manuel primarily contributed to the Enzyme project, focusing on automatic differentiation of LLVM and MLIR. They addressed bugs, optimized code, and improved the handling of various input/output operations within the Enzyme framework. The user also integrated a Rust build system, including dependency management and testing procedures. Furthermore, the user refactored code related to constant expressions and improved the overall code quality and robustness of Enzyme.
clangautomatic-differentiationsimulationcompilertensorflow
rust-lang/rust
Jun 2021 - Jul 2021
Empowering everyone to build reliable and efficient software.
Role in this project:Back-end Developer
Back-end DeveloperContributions:135 reviews, 6 commits, 43 PRs in 1 month
Contributions summary:Manuel's commits primarily focused on implementing and integrating LLVM Enzyme, a tool for automatic differentiation, into the Rust compiler. Their contributions included adding support for Enzyme to the bootstrap process, modifying the configuration and build steps to include Enzyme, and integrating Enzyme into the test infrastructure. Furthermore, they introduced the core autodiff functionality by modifying the `rustc_codegen_llvm` and `rustc_middle` crates.
crategarbage-collectionrustreliablecompiler
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