Rate My Professor Ryan Peterson

His research specializations include clinical trials, infectious disease epidemiology, machine learning, model selection and interpretability, statistical computation, best practices for statistical collaboration, and methods for missing data. Peterson's methodological interests focus on model selection under ranked sparsity involving interactions and polynomials, clinical trials with multiple complex endpoints, study design optimization, and statistical computation. His applied research covers pulmonary sciences, influenza forecasting, seasonality in infectious diseases, and progression prediction in Parkinson’s disease. He has earned the Milford E. Barnes Award from the University of Iowa (2019), Best Poster Award at the International Biometrics Conference (2020), Best Paper Award from the Journal of Applied Statistics, Early Career Award from the Association for Clinical and Translational Statisticians (2021), and Best Contributed Session Award from the American Statistical Association Statistical Consulting Section at the Joint Statistical Meetings (2023). Key publications are "Ordered quantile normalization: a semiparametric transformation built for the cross-validation era" (Journal of Applied Statistics, 2020), "Finding Optimal Normalizing Transformations via bestNormalize" (R Journal, 2021), "Asthma in COVID-19 hospitalizations: an overestimated risk factor?" (Annals of the American Thoracic Society, 2020), and "Application of multi-state models in cancer clinical trials" (Clinical Trials, 2018). Peterson's contributions have advanced statistical methods in biomedical research.