Alexis is a PhD candidate in statistics at the University of Massachusetts Amherst whose research focuses on the statistical evaluation of biomarkers and medical prediction models, with particular emphasis on the area under the receiver operating characteristic curve (AUC). Her methodological work includes nonparametric estimation and inference for the AUC when risk scores are estimated from the data, as well as time-dependent AUC estimation under verification bias and censoring. Her research addresses challenges common in clinical and outcomes data, including missingness, correlated data, and measurement error, drawing on mixed-effects modeling, survival analysis, resampling-based inference, and inverse-probability weighting. She has also conducted applied research on the reliability of clinical assessments for aphasia and has delivered invited instruction on measurement, reliability, and generalizability theory at the Massachusetts General Hospital Institute of Health Professions, in addition to designing and teaching a university seminar on clinical trials and evidence evaluation. Alexis has consulted across academia and industry in the health and life sciences, including projects on medical device evaluation, survival and outcomes modeling, and estimating patient numbers and treatment utilization from pharmaceutical shipment data. She has also built cloud-based data pipelines for live, large-scale real-world data. She holds an MA in economics from the University of Massachusetts Amherst and previously worked as a health economist.
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