Publications

To investigate the changing characteristics of SARS-CoV-2-related pediatric hospital admissions over time. STUDY DESIGN: This was a national, observational cohort study from July 1, 2020, to August …

The rapidly evolving regulatory landscape in AI presents significant challenges to establishing and maintaining trust. AI practitioners face a substantial burden in understanding and operationalizing …

Research suggests that large-scale human cooperation is driven by shared narratives that encode common beliefs and values. This study explores whether such narratives can similarly nudge LLM agents …

Population health studies increasingly collect longitudinal, patient-reported symptom data via mobile devices, offering unique insights into experiences outside clinical settings, such as pain, …

Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI …

Large language models (LLMs) have been extensively studied for their ability to generate convincing natural language sequences; however, their utility for quantitative information retrieval is less …

Machine learning models for multi-omics data often trade off predictive accuracy against biological interpretability. An emerging class of deep learning architectures structurally encode biological …