Lifan Wu is an applied scientist with six years of experience translating advanced quantitative methods into production at Amazon and AWS after a Ph.D. in Operations Research from Cornell. He has a strong background in financial engineering and data science, having built state-space models and visualization tools for fixed income and CLO issuance during roles at Morgan Stanley and Swiss Re. Comfortable spanning research and engineering, he teaches complex stochastic and simulation concepts based on years of academic instruction experience. At Amazon he now applies that toolkit to large-scale ML problems, combining rigorous probabilistic modeling with practical product-focused deployment. A detail that sets him apart is his early career breadth—from actuarial reviews and bank client work to building web tools—giving him both domain depth and user-centered perspective.
6 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Operations Research, Doctor of Philosophy (Ph.D.) Operations Research at Cornell University
Bachelor of Science (BS) Mathematics and Statistics, Bachelor of Science (BS) Mathematics and Statistics at Purdue University
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