Dr. Julian Wolfson develops novel techniques for identifying important predictors of clinical outcomes from large and complex data. The techniques he uses merge traditional statistical methods with machine learning approaches to make most efficient use of the data and account for challenges such as missing data, measurement error, and selection bias. He has applied his methods to problems such as finding surrogate endpoints in clinical trials, identifying relevant explanatory variables in the presence of correlation and measurement error, predicting the risk of heart attacks using electronic health record data, and understanding human behavior patterns using smartphone sensor data. Dr. Wolfson also serves as Associate Editor for Reproducibility at the Journal of the American Statistical Association, where he co-developed a comprehensive reproducibility review process for submitted papers.
Associate Professor, Division of Biostatistics, University of Minnesota School of Public Health