Julian Wolfson, PhD
My research lies at the intersection of causal inference and machine learning, particularly as applied to large, messy datasets. I have applied my 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.
My collaborative work spans multiple disciplines, including infectious disease (HIV/Ebola), cardiovascular health, nutrition and obesity, and pediatrics. I serve as lead statistician on a number of clinical trials, consult on small projects with a wide variety of investigators, and participate in interdisciplinary research teams pursuing longer-term research projects.
Awards and Recognition
- Leonard M. Schuman Award for Excellence in Teaching, School of Public Health, 2016
- Delta Omega National Honor Society, 2016
- American Statistical Association
- Institute of Mathematical Statistics
- International Biometric Society