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You are here: Home / Member News / Ruishan Lin Wins Statistical Significance Poster Competition

Ruishan Lin Wins Statistical Significance Poster Competition

October 1, 2025 Leave a Comment

Chaitra H. Nagaraja, ASA Scientific and Public Affairs Advisory Committee

The winner of this year’s Statistical Significance contest at the 2025 Joint Statistical Meetings is Ruishan Lin for her presentation, “Detection of multiple change points for Nonstationary Network Autoregressive Models.” She developed a time series technique to model electroencephalogram data, which detects changes in brain activity such as a seizure. Lin is a PhD student in statistics at George Mason University with interests in network analysis, change point detection, and science communication.

Kimberly Greco received an honorable mention for her poster, “Weakly Supervised Transformer for Rare Disease Phenotyping.” This work uses deep learning to comb through patient histories to discern patterns in rare disease detection and treatment. Greco is a PhD student in biostatistics at Harvard University and is interested in integrating multi-modal biomedical data into predictive models.

There were 40 participants who submitted a one-page piece explaining the contribution their research makes to society and subsequently presented their poster at the conference. The ASA’s Scientific and Public Affairs Advisory Committee has sponsored this competition since 2009.

An Asian woman with shoulder-length black hair, wearing a gray blazer and a dark shirt, stands near a poster presentation
Ruishan Lin wins the 2025 Statistical Significance competition.
A white woman with mid-length dark hair, wearing a white blouse and khaki pants, stands near a poster presentation
Kimberly Greco receives honorable mention in the 2025 Statistical Significance competition.

Filed Under: Member News, People News Tagged With: 2025 Joint Statistical Meetings, brain activity, change point detection, data, electroencephalogram data, JSM, JSM 2025, Kimberly Greco, multi-modal biomedical data, network analysis, Nonstationary Network Autoregressive Models, Rare Disease Phenotyping, Ruishan Lin, science communication, seizures, Statistical Significance, statistician, statisticians, statistics

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