Yutaka Shimizu - Research Scientist at University of California, Berkeley

Yutaka Shimizu

Research Scientist at University of California, Berkeley

Berkeley, California, United States

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Summary

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Rockstar

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Top School

Yutaka Shimizu is an autonomous vehicle research scientist and software engineer based in Berkeley with seven years’ experience in planning and control for self-driving cars. He holds a Master's from the University of Tokyo and is pursuing a PhD in Mechanical Engineering at UC Berkeley, combining strong academic publication credentials with production contributions to Autoware’s map-based prediction and multi-object tracking modules. His work blends C++ and Python expertise with practical systems engineering—improving obstacle planning, time-delay compensation, and velocity-aware tracking in open-source autonomy stacks. Having transitioned from industry roles at Tier IV and a short ML stint at Zoox, he is comfortable moving research into real-time vehicle software and published novel constrained-optimization approaches for velocity and belief-space planning. A less obvious strength is his dual fluency in deep algorithmic research and hands-on refactoring/architecture work that helps teams ship robust, maintainable autonomy code.

7 years of coding experience

5 years of employment as a software developer

Doctor of Philosophy - PhD Mechanical Engineering, Doctor of Philosophy - PhD Mechanical Engineering at University of California, Berkeley

University of Tokyo

学士機械工学, 学士機械工学 at Waseda University

Chinese, Chinese, Japanese

Github Skills (8)

object-tracking10

c-language10

cprogramming-language10

ros10

autonomous-driving10

algorithm9

algorithms9

performance-optimization8

Programming languages (6)

DockerfileJavaC++CCMakePython

Github contributions (5)

autowarefoundation/autoware_universe

Mar 2022 - Jan 2023

Role in this project:

Back-end & System Architect

Contributions:345 reviews, 117 commits, 514 PRs in 10 months

Contributions summary:Yutaka made significant contributions to the map-based prediction module, involving refactoring, refactoring, and code cleanup, and also integrating utility functions. The user demonstrated expertise in the object tracking within the multi-object tracker by adding a pass-through tracker, enabling velocity observation, and adjusting measurement dimensions. Furthermore, the user improved the obstacle cruise planner through parameter adjustments, time delay compensation, and the handling of stop conditions.

autowareros23d-mapcalibrationros

purewater0901/HMKF

Sep 2022 - Mar 2023

Contributions:86 commits, 7 pushes in 5 months

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