Qiming Huang
About Qiming Huang
Qiming Huang is a Senior Research Scientist with a PhD in Statistics from Purdue University. He specializes in statistical machine learning methods for advertising and has experience in text classification and confidence modeling.
Work at Convoy
Qiming Huang serves as a Senior Research Scientist at Convoy Inc since 2022. In this role, he applies his expertise in statistical machine learning to enhance the company's research initiatives. His work focuses on developing innovative solutions that leverage statistical methods to optimize processes within the organization.
Education and Expertise
Qiming Huang holds a Doctor of Philosophy (PhD) in Statistics from Purdue University, where he studied from 2011 to 2016. He also earned a Master's degree in Statistics from the same institution in 2011. His educational background includes a Bachelor of Science in Statistics from Beijing Institute of Technology, completed in 2010. Huang has specialized knowledge in statistical machine learning, particularly in advertising applications, and possesses expertise in dependence measures, variable selection, and dimension reduction.
Background
Qiming Huang's academic journey began at Beijing Normal University and Tsinghua University, where he participated in summer programs focused on statistics in 2009. He later worked as a Teaching Assistant at Beijing Institute of Technology for four months in 2009-2010. His professional experience includes a role as a Research Scientist Intern at Amazon in 2015 and a full-time Research Scientist position at Amazon from 2016 to 2017 in the Greater Seattle Area.
Research Focus
Qiming Huang's research primarily concentrates on statistical applications in text classification and confidence modeling. He employs statistical machine learning methods to address complex problems in various fields, including bioinformatics, environmental science, image analysis, and psychometrics. His work aims to advance the understanding and application of statistical techniques in real-world scenarios.