Youna Hu
Director of AI at IRL - Do More Together- Claim this Profile
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Topline Score
Bio
Experience
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IRL - Social Messenger
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United States
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Consumer Services
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1 - 100 Employee
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Director of AI
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Nov 2021 - Present
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Amazon
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United States
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Software Development
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700 & Above Employee
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Sr Applied Scientist / Tech Lead
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Dec 2019 - Nov 2021
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A9.com
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United States
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Technology, Information and Internet
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100 - 200 Employee
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Applied Scientist / Tech Lead
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Sep 2015 - Nov 2019
* Leading a team to build pipelines to monitor ads response prediction model metrics * Led a team to model uncertainties of estimated click through rate of Amazon ads and evaluated the model in prdocution for reinforcement learning * Analyzed interactions between Amazon organic search and advertisement * Designed and conducted A/B experiments to evaluate models for Amazon ads * Leading a team to build pipelines to monitor ads response prediction model metrics * Led a team to model uncertainties of estimated click through rate of Amazon ads and evaluated the model in prdocution for reinforcement learning * Analyzed interactions between Amazon organic search and advertisement * Designed and conducted A/B experiments to evaluate models for Amazon ads
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23andMe
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United States
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Biotechnology Research
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400 - 500 Employee
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Scientist
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Jul 2013 - Sep 2015
* Analyzed 23andMe's large genetic database: millions of customers and genetic variables and thousands of self reported health variables for each customer * Provided statistical guidance for 23andMe's commerical and academic research collaboration studies such as the genetics of chronic pain, mosquito bite size, gestation length and etc. * Authored the discovery paper of morning person genetics at Nature Communications based on the analysis of 23andMe cohort (the discovery has been reported by many media sites)
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University of Michigan
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United States
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Higher Education
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700 & Above Employee
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Research Assistant
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Sep 2008 - Aug 2012
* Led the analysis of the genetic associations of lipids in a large scale NIH project: 6,000+ samples, each with >30 millions genetic variables * Developed a hidden Markov model based method and implemented it in C++ to infer the fine scale ancestries of African American * Developed a method and implemented it as a software to calcualte sample size for longitudinal data with binray outcomes and demonstrated sample size savings via a novel approach * Led the analysis of the genetic associations of lipids in a large scale NIH project: 6,000+ samples, each with >30 millions genetic variables * Developed a hidden Markov model based method and implemented it in C++ to infer the fine scale ancestries of African American * Developed a method and implemented it as a software to calcualte sample size for longitudinal data with binray outcomes and demonstrated sample size savings via a novel approach
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Education
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University of Michigan
PhD, Biostatistics -
University of Waterloo
Master of Mathematics, Applied Mathematics -
Nanchang University
Bachelor's degree, Mathematics