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Wang unlocking complex heterogeneity in large spatial-temporal data with scalable quantile learning

2024-09-30
(Press-News.org)

Lily Wang, Professor, Statistics, College of Engineering and Computing (CEC), received funding for the project: “Collaborative Research: Unlocking Complex Heterogeneity in Large Spatial-Temporal Data with Scalable Quantile Learning.”

Wang and her collaborator, Huixia Judy Wang, Department Chair and Professor of Statistics at The George Washington University, are developing scalable and efficient quantile learning techniques and theories to address challenges in analyzing large-scale heterogeneous spatial and temporal data. These new analytical techniques will have wide-ranging applications, revolutionizing scientists’ understanding of spatial and temporal variations in critical areas. By making these advanced techniques widely accessible through public software releases, the project will empower researchers and policymakers to leverage vast amounts of spatial-temporal data and address pressing societal issues more effectively. The project will also contribute to STEM education by engaging both undergraduate and graduate students in hands-on learning and incorporating research findings into course development.

Wang received $248,742 from the National Science Foundation for this project. Funding began in Sept. 2024 and will end in late Aug. 2027.

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ABOUT GEORGE MASON UNIVERSITY

George Mason University is Virginia’s largest public research university. Located near Washington, D.C., Mason enrolls more than 40,000 students from 130 countries and all 50 states. Mason has grown rapidly over the past half-century and is recognized for its innovation and entrepreneurship, remarkable diversity, and commitment to accessibility. In 2023, the university launched Mason Now: Power the Possible, a one-billion-dollar comprehensive campaign to support student success, research, innovation, community, and stewardship.Learn more at gmu.edu.

 

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[Press-News.org] Wang unlocking complex heterogeneity in large spatial-temporal data with scalable quantile learning