- March 31, 2025
Associate Professor Kenneth Strazzeri's innovative teaching methods and curriculum tweaks significantly improved student engagement and success in George Mason University's STAT 250 course, resulting in an impressive 80% attendance rate across its sections and a strong foundation in statistical concepts for nearly one thousand students each semester.
- March 21, 2025
Nicholas Rios and Abolfazl Safikhani revamped a Department of Statistics student seminar series to better prepare students for professional success, focusing on such topics as public speaking, resume building, and searching for internships.
- March 19, 2025
Professor William F. Rosenberger's publication in the Journal of Royal Statistical Science illuminates the history of randomization.
- March 11, 2025
George Mason's annual cherry blossom prediction contest is underway.
- March 10, 2025
Department of Statistics Annual Cherry Blossom Prediction Competition featured in Fairfax Times.
- March 6, 2025
By developing a new machine learning algorithm to apply variational inference to spatial statistics, Jin Hyung Lee has significantly improved high-dimensional data analysis efficiency and accuracy. The PhD candidate in the Department of Statistics received the 2024 Korean International Statistical Society Outstanding Student Paper Award for his work.
- November 8, 2024
CEC Department of Statistics continues to network with Washington, D.C., universities George Washington and Georgetown, leading to ongoing collaborations.
- October 14, 2024
While working full-time in Northern Virginia as a consultant in technology, management, and marketing analysis for over a decade, Tigran Markaryan has also been teaching statistics at George Mason University as an adjunct professor.
- October 11, 2024
Department of Statistics graduate students gathered with faculty in the Nguyen Building Atrium for the first student-organized welcome mixer.
- September 17, 2024
The Department of Statistics’ Lily Wang and George Washington University’s Huixia Judy Wang are developing scalable, distributed computing methods to analyze large-scale spatiotemporal datasets. The collaborative research project is funded by the National Science Foundation.