Shicong M
Software Engineer at Zoox
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolShicong M is a software engineer based in San Diego with 8 years of experience building perception and pose-graph systems for autonomous vehicles and robotics. He has led end-to-end, ACID-designed pipelines at TuSimple—spanning UI, databases, K8s deployment, AWS S3, visualization, and graph-based multi-bag pose optimization—and now contributes to Zoox’s engineering efforts. His background blends academic SLAM and SfM work (GTSAM contributions and a master’s in computer vision/robot perception) with hands-on production skills in LiDAR/IMU/camera fusion, Bayesian ICP, and concurrent graph building. Notably, he refactored core GTSAM components to improve maintainability and replaced algorithms with more robust Karcher-mean approaches, showing attention to long-term code health as well as algorithmic rigor.
8 years of coding experience
6 years of employment as a software developer
Master's degree Computer Vision Robot Perception SLAM, Master's degree Computer Vision Robot Perception SLAM at Georgia Institute of TechnologyBachelor's degree Electrical and Electronics Engineering, Bachelor's degree Electrical and Electronics Engineering at China University of Geosciences
English, Chinese
Github Skills (10)
gtsam10
c-language10
cprogramming-language10
linear-algebra10
factors9
robotics9
bayesian-network9
graph9
algorithms8
algorithm8
Programming languages (1)
Github contributions (5)
borglab/gtsam
Aug 2020 - Sep 2020
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Role in this project:
Backend Developer
Backend DeveloperContributions:2 reviews, 36 commits, 3 PRs in 1 month
Contributions summary:Shicong primarily focused on refactoring and improving the `Similarity3` class within the GTSAM library. Their contributions involved changing `typedef` to `using`, correcting variable names, and refactoring code for improved readability. The user also implemented helper functions, added test cases, and replaced `rotAveraging` with `gtsam::FindKarcherMean` to improve code maintainability and functionality.
smoothingsparsec-plus-plushierarchicalpattern-recognition
Alexma3312/gtsam
Aug 2020 - Sep 2020
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Contributions:15 pushes, 3 branches in 1 month
sparse-matricesroboticsmatricessmoothingvision
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