Fan Dong is a Senior Machine Learning Engineer with a decade of experience building large-scale, production ML systems across advertising, finance, and research settings. Currently at iQIYI in Shanghai, he has driven multi-point online revenue growth by improving CTR/CVR ranking through cross-feature networks, sequential modeling, and multi-domain learning while solving engineering challenges like ID hash collisions and dynamic embeddings. His research background from the University of Calgary includes federated AI for financial crime detection (NIST/NSF-supported work that placed in the U.S. PETs Challenge) and publications on privacy and heterogeneity in distributed learning. Equally comfortable in academia and industry, he combines hands-on deployment skills—feature stores, A/B experimentation, RTB calibration—with up-to-date exploration of generative and multimodal recommendation models. Fan’s profile reflects a rare blend of rigorous research output and measurable business impact, with a knack for translating advanced ML ideas into production-ready systems.
11 years of coding experience
7 years of employment as a software developer
Master's degree, Economics (Econometrics), Master's degree, Economics (Econometrics) at Central University of Finance and Economics
Master's degree, Software Engineering (AI), Master's degree, Software Engineering (AI) at University of Calgary
Contributions:12 releases, 24 commits, 19 pushes in 8 months
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