Binghong Chen
Quantitative Researcher
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolBinghong Chen is a Ph.D. machine learning researcher and quantitative researcher at Citadel Securities with a decade of experience bridging deep learning research and real-world applications. Trained at Tsinghua and Georgia Tech under Le Song and Chao Zhang, he develops novel neural methods for discrete-structured problems—from code optimization and theorem proving to drug design, retrosynthesis, and path planning. His internships at Google, Amazon, and JPMorgan reflect a track record of shipping Transformer- and VAE-based models for code, large-scale text pretraining, and order-flow modeling. Notably, he combines neural-guided search and neural-symbolic approaches, and has applied contrastive and pretraining techniques across text and graph modalities. Based in Chicago, he blends strong theoretical grounding with practical systems experience, often implementing models in JAX, TensorFlow, and production settings.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, 4.0/4.0, Doctor of Philosophy - PhD, Machine Learning, 4.0/4.0 at Georgia Institute of TechnologyBachelor of Engineering - BE, Computer Science, 93/100, Rank 3/127, Bachelor of Engineering - BE, Computer Science, 93/100, Rank 3/127 at Tsinghua University
Chinese, English
Github Skills (8)
planning9
computer-vision7
parallel7
pytorch6
deep-learning6
machine-learning6
molecule3
generative1
Programming languages (3)
Github contributions (5)
binghong-ml/retro_star
Jun 2020 - Jul 2022
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Contributions:6 commits, 1 PR, 4 pushes in 2 years
pytorchretrodeep-learningcomputer-visionguided
cbh13/ucore-lab-doc
Mar 2016 - May 2016
Contributions:55 pushes, 1 branch in 2 months
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