Hao Xiang - Research Scientist at Waymo

Hao Xiang

Research Scientist at Waymo

Los Angeles, California, United States

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Summary

🤩

Rockstar

🎓

Top School

Hao Xiang is a research scientist specializing in machine learning, computer vision, and cooperative perception for autonomous driving, with eight years of experience spanning academia and industry. He holds a PhD from UCLA and earned a spotless academic record while focusing on spatial-temporal analysis, relational learning, and semantic mapping. Hao transitioned from graduate research and internships at Waymo into a research scientist role there, bringing hands-on experience deploying and prototyping full-stack cooperative driving solutions. He contributes to open-source projects like OpenCDA, improving developer experience, planners, and cooperative behavior modules—showing a practical focus on code maintainability as well as algorithms. Based in Los Angeles, he blends deep theoretical rigor with applied system-level work that accelerates safe, multi-agent autonomy.

8 years of coding experience

5 years of employment as a software developer

University of California, San Diego

Bachelor's degree, Microelectronics, 3.6/4.0, Bachelor's degree, Microelectronics, 3.6/4.0 at Fudan University

Doctor of Philosophy - PhD, Autonomous Driving, 4.0/4.0, Doctor of Philosophy - PhD, Autonomous Driving, 4.0/4.0 at University of California, Los Angeles

Chinese, English

Github Skills (5)

python10

autonomous-driving9

refactoring8

simulation8

simulations8

Programming languages (3)

C++Jupyter NotebookPython

Github contributions (5)

ucla-mobility/OpenCDA

Jul 2021 - Apr 2022

A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO.

Role in this project:

Back-end Developer

Contributions:37 reviews, 51 commits, 15 PRs in 8 months

Contributions summary:Hao primarily contributed to bug fixes and code refactoring within the `opencda` repository, specifically addressing comment errors, search bugs, and code structure updates. They also added developer tutorials to the documentation and refactored local planner functionality, suggesting a focus on improving code clarity and maintainability. These modifications indicate a role in improving the project's codebase and developer experience. The commits also touch upon the behavior agent and platoon plugin, areas relevant to the cooperative driving automation focus of the project.

cooperative-driving-automationfull-stackprototypingsimulationautomated-driving-systems

XHwind/XHwind

Aug 2021 - Sep 2023

Contributions:15 pushes, 1 branch in 2 years 2 months

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