TO

Thein Htay Oo

Lead Machine Learning Engineer at Govini

Thein Htay Oo is currently working as a Lead Machine Learning Engineer at Govini since July 2022. Prior to that, they worked at UPMC, first as a Lead Data Scientist from July 2020 to July 2022, and then as a Senior Data Scientist from June 2019 to June 2020. Before joining UPMC, Thein Htay Oo had a brief stint at The Data Incubator as a Data Science intern in 2018. Thein Htay also worked as a Research Assistant at the University of Oregon from September 2010 to August 2017, where they created a blob detection web application and developed an interactive front end. Earlier in their career, they served as a Graduate Teaching Assistant at the University of Oregon from September 2009 to June 2010.

Thein Htay Oo has a strong educational background with a focus on physics and data science. They obtained a Doctor of Philosophy (Ph.D.) degree in Quantum Optics and Optomechanics from the University of Oregon in the period from 2011 to 2017. Prior to that, they earned a Master of Science (M.Sc.) degree in Physics from the University of Oregon from 2009 to 2011. In addition, they obtained a Master of Science (MSc) degree in Physics from Western Illinois University from 2007 to 2009. Their earlier education includes a Bachelor of Engineering (B.E.) degree in Electronics from Yangon Technological University from 2001 to 2003. Thein Htay Oo also holds a certification as a Program Graduate from The Data Incubator, which they obtained in September 2018.

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Pittsburgh, United States

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Govini

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Govini’s unique dataset and analytic platform are utilized at scale across the OSD, Joint Staff, military departments, and defense agencies. With Govini, DoD’s data scientists and analysts solve challenges pertaining to strategic portfolio analysis, resource analysis, industrial base assessments, science and technology surveillance, and supply chain analysis. These capabilities enable defense leaders to achieve superior innovation and modernization outcomes on shorter timelines and at a lower cost than traditional methods.


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