Matthew Hausknecht
Principal AI Researcher at DataRobot
Houston, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolMatthew Hausknecht is a Principal AI Researcher with 14 years of experience advancing reinforcement learning and applied AI across industry leaders including DataRobot, Microsoft, Argo AI, and startups like Latitude AI. He holds a PhD in Computer Science from UT Austin and blends deep research expertise with practical engineering—evident from code and QA contributions to influential open-source environments such as the Arcade Learning Environment and Microsoft’s TextWorld. His work spans core algorithmic research, robust testing automation, and production-ready systems for complex simulation and autonomy stacks. Based in Houston, he’s known for diagnosing subtle game-mechanic bugs and strengthening test suites—skills that translate into shipping reliable, reproducible ML research and products.
14 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at AustinBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Emory University
Github Skills (10)
text-based10
c-language10
game-development10
cprogramming-language10
python10
test-automation10
testing10
sdl9
makefile9
reinforcement-learning5
Programming languages (6)
Github contributions (5)
Farama-Foundation/Arcade-Learning-Environment
Oct 2012 - Feb 2016
The Arcade Learning Environment (ALE) -- a platform for AI research.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:112 commits, 33 PRs, 34 pushes in 3 years 4 months
Contributions summary:Matthew primarily focused on fixing bugs and improving the core functionality of the Arcade Learning Environment (ALE) through code modifications. They fixed a critical bug in the Boxing termination detection, ensuring games ended correctly after a knockout. Additional contributions included simplifying the enabling/disabling of SDL and updating action sets within game-specific files, contributing to the overall robustness and maintainability of the project. The user demonstrated a clear understanding of the codebase by addressing issues related to game mechanics and configuration.
arcade-learning-environmentarcadedeep-learningreinforcement-learningmachine-learning
microsoft/TextWorld
Jul 2018 - Aug 2018
TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:10 commits, 8 comments in 1 month
Contributions summary:Matthew primarily focused on enhancing the testing framework for the `microsoft/textworld` repository. Their contributions included updates to existing test scripts, specifically for various text-based games within the project, by modifying walkthroughs and expected behavior. These changes demonstrate an understanding of the game logic and the ability to write effective test assertions. Furthermore, the user made adjustments to the testing environment itself, such as integrating new actions or fixing scores, further showcasing their involvement in quality assurance.
game-aigamesrl-agentsreinforcement-learningevaluation
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