Yang Xue
Graduate Teaching Assistant
Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolYang Xue is an ML-focused physicist and graduate teaching assistant with nine years of hands-on experience developing and refining rotated object detection systems in TensorFlow and the OpenMMLab ecosystem. His open-source contributions include core implementations and optimizations to R3Det and mmrotate, adding algorithmic support (CSL, ATSS, Stable KLD) and practical dataset integrations like DOTA1.5 and DIOR-R. Combining experimental lab automation experience from UC Berkeley with analytic work on topological responses at ShanghaiTech, he bridges theoretical physics and applied ML tooling. Based in Shanghai and pursuing a PhD at The Ohio State University, he brings a research-first mindset to engineering problems and routinely improves model training, configs, and test pipelines. Colleagues value his knack for turning nuanced algorithmic ideas into robust code and reproducible benchmarks.
8 years of coding experience
2 years of employment as a software developer
Bachelor of Applied Science - BASc, Physics, Bachelor of Applied Science - BASc, Physics at ShanghaiTech UniversityPhysics, Physics at University of California, BerkeleyDoctor of Philosophy, Physics, Doctor of Philosophy, Physics at The Ohio State University
Github Skills (22)
algorithm10
algorithms10
pytorch10
python10
machine-learning10
detectors10
data-structure10
detector10
rotation10
tensorflow10
trainings10
retinanet10
data-structures10
object-detection10
computer-vision10
Programming languages (3)
Github contributions (5)
DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow
May 2018 - Jan 2020
Rotational region detection based on Faster-RCNN.
Role in this project:
ML Engineer
ML EngineerContributions:62 commits, 1 PR, 54 pushes in 1 year 7 months
Contributions summary:Yang primarily contributed to the codebase by adding and modifying files related to object detection, specifically within the context of rotational region detection. They introduced features like face detection and made several updates to configuration files (`cfgs.py`) and demo scripts. Furthermore, the user addressed and fixed a bug related to `cv2.rotatedRectangleIntersection` and made updates to data processing scripts.
regionfaster-rcnnfasterrcnn
Thinklab-SJTU/R3Det_Tensorflow
Nov 2019 - Jun 2021
Code for AAAI 2021 paper: R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Role in this project:ML Engineer
ML EngineerContributions:63 commits, 2 PRs, 53 pushes in 1 year 7 months
Contributions summary:Yang primarily contributed to updating and training baseline weights, and adding core code related to R3Det, a refined single-stage detector for rotating objects. Their work involved modifications to configuration files, suggesting involvement in model training and optimization within the TensorFlow framework. These changes indicate a focus on improving object detection performance, likely through refinement of the model's architecture or training process. The commits also show the user's engagement in model testing by updating test scripts.
pytorchrotationrefinedrefinementrotating
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