Contact Information
605 E. Springfield Ave. #152CAB
Champaign, IL 61820
Biography
Douglas G. Simpson is a professor in the Department of Statistics at the University of Illinois Urbana-Champaign and an affiliate professor in the Beckman Institute for Advanced Science and Technology. His research interests include applied and computational statistics, quantitative image analysis, machine learning and functional data, and the general theory of robust and semiparametric statistical methods. He has served as Associate Editor of the Journal of the American Statistical Association (1996–1999), Biometrics (2000–2006) and Chemometrics and Intelligent Laboratory Systems (1999–2006), as a regular member of the Biostatistical Research and Design (BMRD) Study Section of the National Institutes of Health (2006–2010), as Chair-elect, Chair, and Past-Chair of the American Statistical Association Caucus of Academic Representatives (2007–2010). He served several terms as Chair of the Department of Statistics at the University of Illinois between 2000 and 2019 and as Associate Director of the Institute for Mathematical and Statistical Innovation (2020-2022). Dr. Simpson is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and a Fellow of the American Association for the Advancement of Science.
Research Interests
Applied and computational statistics
Biostatistics and bioinformatics
Robust and semiparametric statistical methods
Functional data
Quantitative image analysis
Education
PhD, Statistics, University of North Carolina at Chapel Hill, 1985
MS, Statistics, University of North Carolina at Chapel Hill, 1983
BA, Mathematics, Carleton College, 1980
Additional Campus Affiliations
Professor, Statistics
Professor, Beckman Institute for Advanced Science and Technology
Director, External and Corporate Relations, Statistics
External Links
Recent Publications
McFarlin, B. L., Villegas-Downs, M., Mohammadi, M., Han, A., Simpson, D. G., & O'Brien, W. D. (2024). Enhanced identification of women at risk for preterm birth via quantitative ultrasound: a prospective cohort study. American Journal of Obstetrics and Gynecology MFM, 6(5), Article 101250. https://doi.org/10.1016/j.ajogmf.2023.101250
Villegas-Downs, M., Mohammadi, M., Han, A., O'Brien, W. D., Simpson, D. G., Peters, T. A., Schlaeger, J. M., & McFarlin, B. L. (2024). Trajectory of Postpartum Cervical Remodeling in Women Delivering Full-Term and Spontaneous Preterm: Sensitivity to Quantitative Ultrasound Biomarkers. Ultrasound in Medicine and Biology, 50(12), 1777-1784. https://doi.org/10.1016/j.ultrasmedbio.2024.06.015
Zuo, J., Simpson, D. G., O'Brien, W. D., McFarlin, B. L., & Han, A. (2024). Automated Field of Interest Determination for Quantitative Ultrasound Analyses of Cervical Tissues: Toward Real-time Clinical Translation in Spontaneous Preterm Birth Risk Assessment. Ultrasound in Medicine and Biology, 50(12), 1861-1867. https://doi.org/10.1016/j.ultrasmedbio.2024.08.011
Li, B., & Simpson, D. (2023). Reflections on the IDEA Forum—Statistics, Climate Change, and Sustainability. CHANCE, 36(1), 25-30. https://doi.org/10.1080/09332480.2023.2179273
McFarlin, B. L., Liu, Y., Villegas-Downs, M., Mohammadi, M., Simpson, D. G., Han, A., & O'Brien, W. D. (2023). Predicting Spontaneous Pre-term Birth Risk Is Improved When Quantitative Ultrasound Data Are Included With Historical Clinical Data. Ultrasound in Medicine and Biology, 49(5), 1145-1152. https://doi.org/10.1016/j.ultrasmedbio.2022.12.018