Robert is the Director of Statistical Methods at the Ohio Colleges of Medicine Government Resource Center (GRC). He has over 10 years of experience in statistical research and consulting across a broad range of topics and applications including survey methods and design, statistical disclosure avoidance, dashboard programming, data linkage, and causal inference. He re-joined GRC in 2025 after working at NORC at the University of Chicago as a Senior Statistician II. Prior to NORC, Robert was an Assistant Director of Applied Research and Analysis at GRC, leading the Informatics section. Earlier in his career, he worked as a Research Mathematical Statistician at the U.S. Census Bureau.
Dr. Ashmead has contributed to a variety of GRC survey, quality improvement, claims analysis, and evaluation projects. In addition to providing technical expertise, Robert has served as the principal or co-principal investigator on several projects. In addition, Robert is an expert in developing R-Shiny dashboards and has taught several classes and seminars on the topic.
Robert’s work at NORC included developing tools and workflows for quality assurance and quality control for the National Center for Science and Engineering Statistics’ (NCSES) information products, assisting with the startup of NORC’s disclosure review board, and the survey design and implementation of the USPS Household Mail Survey. In his work at the U.S. Census Bureau, Robert assisted with the design and implementation of the data privacy methods for the 2020 Decennial Census, developing small area estimation models for the Voting Rights Act Section 203 Determinations, and evaluating respondent contact burden in the American Community Survey.
Selected Publications:
Abowd, J. M., Adams, T., Ashmead, R., Darais, D., Dey, S., Garfinkel, S., Goldschlag, N., Hawes, M. B., Kifer, D., Leclerc, P., Lew, E., Moore, S., Rodríguez, R. A., Tadros, R. N., & Vilhuber, L. (2025). A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census. Harvard Data Science Review, 7(3).
Nattino, G., Ashmead, R., & Lu, B. (2024). Causal Inference with Complex Surveys: A Unified Perspective on Sample Selection and Exposure Selection. The American Statistician, 79(2), 173–183.
Frey, H.A., Ashmead, R, PhD; Farmer, A., Kim, Y.H, Shellhaas, C., Oza-Frank, R., Jackson, R.D., Costantine, M.M., Lynch, C.D. (2023) A Prediction Model for Severe Maternal Morbidity and Mortality After Delivery Hospitalization. Obstetrics & Gynecology 142(3):p 585-593, September 2023.
Abowd, J. M., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., & Zhuravlev, P. (2022). The 2020 Census Disclosure Avoidance System TopDown Algorithm. Harvard Data Science Review, (Special Issue 2).
Frey H.A., Ashmead R., Farmer A., et al. (2022) Association of Prepregnancy Body Mass Index With Risk of Severe Maternal Morbidity and Mortality Among Medicaid Beneficiaries. JAMA Network Open. 2022 Jun 1;5(6).