Aaron Brown
Senior Software Engineer at MBRDNA
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAaron Brown is a Senior Software Engineer in Mountain View with a decade of experience building perception and control systems for self-driving cars. He combines an electrical engineering background and advanced CS training with hands-on ML and backend work—contributing UKF tracking, CNN feature-visualization, lidar point-cloud processing, and simulator integration to high-profile Udacity autonomous-driving projects. At MBRDNA he focuses on production-grade vehicle and navigation features, often bridging visualization, sensor fusion, and simulator control. Fluent in both low-level algorithmic tweaks and full-stack delivery, he brings practical results-oriented engineering to safety-critical systems. An uncommon strength is his habit of enhancing interpretability—adding visualization and diagnostic outputs that make complex ML and filtering behavior easier to understand.
9 years of coding experience
Bachelor of Science in Electrical Engineering, Integrated Circuit Design, Bachelor of Science in Electrical Engineering, Integrated Circuit Design at Boise State UniversityMaster's degree, Computer Science, Master's degree, Computer Science at Georgia Institute of TechnologyNanodegree, Machine Learning Engineer, Nanodegree, Machine Learning Engineer at UdacityMinor, Japanese Language, Minor, Japanese Language at Hosei University
English, Japanese
Github Skills (30)
data-visualizations10
simulation10
pcl10
visualization10
c-language10
python10
waypoint10
simulations10
machine-learning10
point-cloud-library10
data-visualisation10
mask-rcnn10
tensorflow10
sensor-fusion10
3d10
Programming languages (7)
Github contributions (5)
udacity/SFND_Unscented_Kalman_Filter
Apr 2019 - Apr 2019
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 9 pushes, 1 branch in 2 days
Contributions summary:Aaron implemented features for tracking cars using the Unscented Kalman Filter (UKF), a core component of the project. They added a selector to choose which cars to track and integrated the UKF into the highway simulation. Furthermore, the user enhanced the visualization by including projected future paths for the tracked cars, aiding in understanding the UKF's predictions. The user also added functionality to visualize the Lidar and Radar measurement data from the simulation.
udacity/CarND-Kidnapped-Vehicle-Project
Apr 2017 - May 2017
Role in this project:Back-end Developer
Back-end DeveloperContributions:14 commits, 6 PRs, 16 pushes in 1 month
Contributions summary:Aaron contributed to the particle filter project by adding functionalities to generate and output best particle data to a text file for visualization. The user also updated the `particle_filter.cpp` file to correct the map's y-axis orientation. Furthermore, the user updated the simulator build instructions and the `run.sh` file. These changes indicate contributions to the core functionality and setup of the project.
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