Shangjie Xue
PHD Student In CS at Georgia Institute of Technology
Atlanta, Georgia, United States
Summary
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Rockstar🎓
Top SchoolShangjie Xue is a PhD student in Computer Science at Georgia Tech with seven years of research and engineering experience bridging probabilistic robotics, vision, and optimization. He holds dual MS degrees from MIT with perfect GPAs and a BS in Physics from Peking University, bringing strong theoretical instincts to practical system building. His open-source contributions to the widely used GTSAM library focused on hybrid optimization and HybridValues, demonstrating deep expertise in Bayesian smoothing and hybrid Gaussian Bayes nets. Past research internships at Uber and NVIDIA reflect experience applying advanced algorithms in industry-scale settings. Colleagues describe him as a meticulous back-end developer who pairs rigorous proofs with production-minded testing to integrate new probabilistic features into mature codebases.
7 years of coding experience
Master of Science - MS, EECS, 5.0/5.0, Master of Science - MS, EECS, 5.0/5.0 at Massachusetts Institute of TechnologyBachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Peking University
Github Skills (9)
bayesian-network10
graph10
factors10
c-language10
gtsam10
cprogramming-language10
linear-algebra9
algorithm9
algorithms9
Programming languages (2)
Github contributions (5)
borglab/gtsam
Nov 2021 - Aug 2022
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:
Back-end Developer
Back-end DeveloperContributions:19 reviews, 18 commits, 3 PRs in 8 months
Contributions summary:Shangjie primarily contributed to the development and testing of hybrid optimization methods within the GTSAM library. Their work involved implementing and testing new methods related to `HybridValues` and the `optimize()` method within the context of hybrid Gaussian Bayes nets, demonstrating a focus on probabilistic robotics and vision algorithms. The user also worked on the `HybridLookupDAG` component, integrating it with the `HybridValues` to perform argmax operations and demonstrating proficiency in integrating new features within a larger framework. This involved modifying existing code and writing tests to ensure the correctness and efficiency of the implemented algorithms.
smoothingsparsec-plus-plushierarchicalpattern-recognition
xsj01/xsj01.github.io
Jan 2021 - Apr 2023
Contributions:25 pushes, 2 branches in 2 years 3 months
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