Tin Nguyen is a quantitative researcher with six years of experience translating advanced Bayesian inference and statistical algorithms into systematic trading strategies and applied ML tools. He holds a PhD from MIT where his work produced inference algorithms and sensitivity-detection methods published at NeurIPS, AISTATS and Bayesian Analysis, and he has repeatedly demonstrated orders-of-magnitude runtime improvements in validation and inference tasks. At Quantbot he quickly moved from intern to full researcher, applying rigorous statistical thinking to live trading systems, and prior internships at IBM exposed him to robust testing of learned features under domain shift. Tin combines deep theoretical training with practical engineering instincts—he designs methods with provable guarantees that are engineered to run efficiently in production. Based in New York, he brings a rare blend of academic rigor and product-minded execution to quantitative research problems.
6 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Massachusetts Institute of Technology
Bachelor of Science in Engineering Operations Research and Financial Engineering, Bachelor of Science in Engineering Operations Research and Financial Engineering at Princeton University
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