Stone Tao
Summary
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RockstarStone Tao is a PhD student researcher at UC San Diego and experienced software engineer focused on simulation, robotics, and reinforcement learning, with eight years of applied research and engineering experience. He co-founded the Lux AI Challenge, driving the competition's engine and environment design that attracted 900+ GitHub stars and 1,400+ competitors, and contributed backend integration of Lux into Kaggle Environments. Stone has interned on embodied AI at NVIDIA and worked on production-grade ML systems and tooling—from converting brain.js to TypeScript to building high-precision function approximators at QuantCo and feature-flagging systems at LaunchDarkly. He blends deep research instincts with practical engineering, comfortable shipping backend infrastructure, simulation environments, and RL experiments at scale. Based in San Diego, he favors quick, open collaboration (reach him on X/Twitter) and often works at the intersection of contest design, open source, and embodied AI research.
8 years of coding experience
Github Skills (20)
javascript10
game-engine10
python10
game-dev10
typescript10
object-oriented-programming10
data-structure10
ai10
typescript-types10
neural-network10
data-structures10
state-management10
nodejs10
typescripts10
game-development10
Programming languages (14)
Github contributions (5)
Lux-AI-Challenge/Lux-Design-S1
Aug 2020 - Jul 2022
Home to the design and engine of the @Lux-AI-Challenge Season 1, hosted on @kaggle
Role in this project:
Back-end Developer
Back-end DeveloperContributions:30 reviews, 435 commits, 55 PRs in 1 year 10 months
Contributions summary:Stone appears to have focused on developing the core logic for the Lux AI Challenge engine and integrating the game rules within the engine's framework. They worked on implementing the game state, actions, and rule enforcement, including resource management, building, and unit actions. The code changes included the initial implementation of a design class, types and default configurations, and building core game mechanics, suggesting significant backend development.
ailux-aireinforcement-learning
Kaggle/kaggle-environments
Jul 2021 - Jan 2023
Role in this project:Back-end Developer
Back-end DeveloperContributions:53 reviews, 38 commits, 50 PRs in 1 year 6 months
Contributions summary:Stone primarily contributed to the backend implementation of the Lux AI 2021 environment within the Kaggle Environments repository. Their work focused on integrating the Lux AI Season 1 design, including changes to game mechanics and data structures, suggesting a focus on core game logic. The user also made changes to reward calculations, ensuring proper win/loss/tie conditions and introduced a warning if node is not installed, indicating involvement in environment setup. Furthermore, changes to the JS test suggests the individual's responsibility extended to the environment integration and testing.
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