Lang Liu

Lang Liu has a diverse and extensive work experience. They started their career in 2015 as a Research Assistant at Tsinghua University, where they proposed a scalable density divergence based test for the K-sample test problem by segmentation. In 2016, they worked as a Research Intern at the University of Washington, focusing on Bayesian structure learning for stationary time series. In 2017, Lang Liu became a Research and Teaching Assistant at the University of Washington, where they worked until 2022. During this time, they also had an internship at Google in 2020 as a Data Scientist Intern, where they formalized a meta-learning framework and developed a distribution embedding network. In 2019, Lang Liu worked as an Applied Scientist Intern at Amazon, where they developed a track-query joint embedding model for the Music ML Team. Currently, they work at Citadel Securities as a Quantitative Researcher.

Lang Liu attended Tsinghua University from 2013 to 2017, where they obtained a Bachelor of Science (BS) degree in Mathematics and Statistics. From 2017 to 2022, they pursued a Doctor of Philosophy (PhD) degree in Statistics at the University of Washington.