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.

• 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
• INFORMS

• NIH/NHLBI, "Targeting Anticoagulant to Reduce Inflammation in Treated HIV Disease," C- Investigator
• NIH/NIA, "Treatment to Reduce Inflammation and Improve Immune Recovery Among Older HIV Patients," Co-Investigator
• NIH/NIDDK, "Weight Tracking and Weight Loss Outcomes: Establishing the Standard of Care," Co-investigator
• Minnesota Metropolitan Council, "Smartphone-Based Interventions for Sustainable Travel Behavior," Co-Investigator
• National Science Foundation, "SRN: Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy and Livable Cities." 

SmarTrAC: A smartphone solution for travel and activity capturing.

• Inventors: Fan Y, Wolfson J and Adomavicius G
• Provisional patent filed August 2014
• Full patent application filed August 2015

See a full list of my publications on Google Scholar

Selected Publications

• Wolfson J, Vock DM, Bandyopadhyay S, Kottke T, Vazquez Benitez G, Johnson P, Adomavicius G, and O'Connor PJ. "Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data," To appear, Journal of the American Heart Association, 2017.
• Brown, B., Miller, C.J., and Wolfson, J. "ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction Via Estimating Equations," Journal of Computational and Graphical Statistics, March 2017.
• Wolfson J, Bandyopadhyay S, Elidrisi M, Vazquez-Benitez G, Vock DM, Musgrove D, Adomavicius G, Johnson PE, O'Connor PJ. "A Naive Bayes Machine Learning Approach to Risk Prediction Using Censored, Time-to-Event Data," Statistics in Medicine, September 2015