Nolan Gormley — accepted AI-conference papers

2026

Reducing Alert Fatigue Through AI Ranking: A Deployed Public Health Data Monitoring System

Ananya Joshi, Nolan Gormley, Richa Gadgil, Catalina Vajiac, Tina Townes, Roni Rosenfeld +1

AAAI 2026technical

Public health experts need scalable methods to monitor large volumes of health data (e.g., human-reported cases, hospitalizations, deaths). These methods must identify individual data points that may indicate significant events, such as outbreaks, or reveal data quality issues. Identifying, triaging

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2024

Outlier Ranking for Large-Scale Public Health Data

Ananya Joshi, Tina Townes, Nolan Gormley, Luke Neureiter, Roni Rosenfeld, Bryan Wilder

AAAI 2024technical

Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers returned by univariate outlier detection methods a…

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