Katelyn Gadd is a Senior Software Engineer in Seattle with 16 years of experience bridging game design, engine/tools development, and low-level runtime engineering. She has deep expertise in .NET and WebAssembly—contributing to the dotnet/runtime and as a founder of WebAssembly work—and created widely used projects like the Jiterpreter and JSIL for compiling .NET to JavaScript. Equally comfortable shipping game code and building backend systems, she’s improved performance from SQL queries to JIT codegen and implemented platform-specific fixes for projects like MonoGame and FNA. Known for fixing broken processes and shipping pragmatic tooling, she pairs creative product instincts (game design and writing) with rigorous systems-level debugging. She’s a persistent collaborator who pushes for better developer and customer experiences, and has a track record of turning research/prototypes into production-quality runtime features.
FNA - Accuracy-focused XNA4 reimplementation for open platforms
Role in this project:
Back-end Developer
Contributions:4 reviews, 9 commits, 23 PRs in 3 years 6 months
Contributions summary:Katelyn primarily focused on fixing bugs and improving the accuracy of the XNA4 reimplementation. They addressed issues with struct uniforms, implemented pixel alignment adjustments, and resolved errors in the color conversion process. Additionally, the user refactored sampler modification tracking, exposing text editing events and adding support for BC7 texture formats.
.NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps.
Role in this project:
Back-end Developer
Contributions:1506 reviews, 83 commits, 477 PRs in 5 years 10 months
Contributions summary:Katelyn contributed significantly to the .NET runtime, particularly focusing on the WASM build. Their work involved deep dives into the low-level aspects of the runtime, including optimizing the jiterpreter, introducing specialized features like SIMD support and exception handling within the jiterpreter, and refining the code generation process for better performance. These optimizations included improvements to memory management with custom mmap and code generation strategies.
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