Yongbin Sun
Quantitative Researcher
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolYongbin Sun is a quantitative researcher based in Cambridge, MA with 11 years of experience at the intersection of machine learning, computational engineering, and finance. He currently conducts alpha research for global equities and mid-frequency strategies at Citadel, leveraging a deep academic foundation from MIT where he completed MS and PhD work in mechanical and computational engineering. His background includes hands-on roles in industry research and product engineering—stints at Google Ads and Google Research focused on view synthesis and light fields—and contributions to ML projects such as point-cloud part segmentation. Comfortable moving between research, model architecture, and production training pipelines, he combines rigorous quantitative methods with practical engineering to deploy models that drive trading decisions. Known for meticulous academic results (top grades during graduate study) and practical impact, he brings both theoretical depth and production experience to data-driven strategy development.
11 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Mechanical and computational engineering, 5.0/5.0, Doctor of Philosophy (Ph.D.), Mechanical and computational engineering, 5.0/5.0 at MITMaster of Science - MS, 4.8/5.0, Master of Science - MS, 4.8/5.0 at Massachusetts Institute of TechnologyBachelor of Science (B.S.), Mechanical Engineering, Bachelor of Science (B.S.), Mechanical Engineering at Shanghai Jiao Tong University
English, Chinese
Github Skills (10)
machine-learning10
deep-learning10
trainings10
tensorflow10
point-cloud10
python10
modeling10
evaluation9
eval9
semantic-segmentation9
Programming languages (1)
Github contributions (5)
WangYueFt/dgcnn
Feb 2018 - Dec 2018
Role in this project:
ML Engineer
ML EngineerContributions:16 commits, 12 pushes, 9 comments in 10 months
Contributions summary:Yongbin contributed to the development of a part segmentation model, likely for point cloud data. Their commits involved modifying the model architecture within `part_seg/part_seg_model.py` and training scripts (`part_seg/train_multi_gpu.py`), indicating a focus on model design and training processes. Further modifications in `part_seg/test.py` suggest involvement in model evaluation and testing. The commits also set up the initial semantic segmentation and provided minor updates to the data download and collection scripts.
syb7573330/PointGrow
Oct 2018 - Mar 2020
An autoregressive model for point cloud generation augmented with self-attention
Contributions:25 commits, 23 pushes, 1 branch in 1 year 5 months
point-cloudself-attention
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