Brett Lopez - Assistant Professor at University of California, Los Angeles

Brett Lopez

Assistant Professor at University of California, Los Angeles

Los Angeles, California, United States

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Summary

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Senior

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Brett Lopez is an assistant professor at UCLA specializing in nonlinear control, estimation, and trajectory optimization for autonomous systems. With nine years of experience spanning academia and high-profile robotics programs, he led drone teams for NASA JPL's CoSTAR in the DARPA Subterranean Challenge and contributed to low-latency collision avoidance in unknown environments. His work advances robust model predictive control under uncertainty and integrates perception, localization, and planning to enable safe, real-time autonomous flight. He is an active open-source contributor, notably implementing a 3D reactive planner for unknown environments in the mit-acl/faster project and integrating sensor streams from Vicon and LaserScan for collision avoidance and goal generation. Based in Los Angeles, he earned his PhD in Aerospace Engineering from MIT and a BS from UCLA, collaborating across MIT, JPL, and university labs to translate theory into deployable robotics systems.

9 years of coding experience

7 years of employment as a software developer

Massachusetts Institute of Technology

Bachelor of Science (BS), Aerospace, Aeronautical and Astronautical Engineering, Bachelor of Science (BS), Aerospace, Aeronautical and Astronautical Engineering at University of California, Los Angeles

Github Skills (9)

trajectory-optimization10

pathfinding10

c-language10

ros10

c-programming-language10

path-finding10

algorithm9

algorithms9

point-clouds7

Programming languages (3)

OpenEdge ABLC++SCSS

Github contributions (5)

mit-acl/faster

May 2016 - Oct 2017

3D Trajectory Planner in Unknown Environments

Role in this project:

Back-end Developer

Contributions:234 commits in 1 year 4 months

Contributions summary:Brett's commits primarily focused on the implementation of a reactive planner for 3D trajectory planning. The code changes involve integrating sensor data from Vicon and LaserScan sources. These changes involve modifying the code to allow for the vehicle to follow the new trajectory, and they reflect work on refining the planning logic with a focus on collision avoidance and generating intermediate goals. The work highlights development of core back-end features.

path-planningunknownuavtrajectory-optimizationplanner

btlopez/btlopez.github.io

Jul 2023 - Jan 2025

Contributions:14 pushes, 1 branch in 1 year 6 months

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