Matt Richardson
Sr. Principal Researcher at Microsoft
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolMatt Richardson is a Sr. Principal Researcher at Microsoft with over nine years of professional experience and a long tenure at the company dating back to 2004. He holds a PhD in Computer Science from the University of Washington and a BS from Caltech, combining deep academic training with extensive applied research. Matt focuses on systems and backend engineering for machine learning tooling, contributing significant cross-platform and CUDA support work to the well-known DyNet dynamic neural network toolkit. His strengths lie in making complex research codebases robust and performant across platforms—evidenced by solving Windows compilation and GPU compatibility challenges. Based in Seattle, he blends academic rigor with pragmatic engineering to move prototypes toward production-ready infrastructure.
9 years of coding experience
11 years of employment as a software developer
PhD Computer Science, PhD Computer Science at University of WashingtonCalifornia Institute of Technology
Stackoverflow
11reputation
324reached
1answer
2questions
Github Skills (19)
c-language10
gpu-programming10
eigen10
cuda10
cprogramming-language10
msvcrt10
multiplatform9
cross-platform9
machine-learning8
linear-algebra8
html6
react-router6
react6
vscode-extension6
visual-studio-code6
Programming languages (5)
Github contributions (5)
clab/dynet
Sep 2016 - Feb 2018
DyNet: The Dynamic Neural Network Toolkit
Role in this project:
Back-end Developer & System Architect
Back-end Developer & System ArchitectContributions:91 commits, 28 PRs, 22 pushes in 1 year 4 months
Contributions summary:Matt primarily focused on enhancing the codebase to support cross-platform compilation and CUDA support. Their contributions included modifying files to allow compilation in Microsoft Visual Studio 2015 and adding conditional compilation directives for Windows environments. They also addressed issues related to CUDA support, including fixing compilation errors and optimizing performance. Additionally, they refactored the code to improve efficiency and compatibility with different systems.
dynetdynamic-neural-networkdeep-learningneural-networksmachine-learning
mattr1/dynet
Sep 2016 - May 2017
Contributions:4 PRs, 53 pushes, 44 branches in 7 months
neural-networkcpp
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.