Ashley  Naimi

I am Associate Professor in Epidemiology with expertise in causal inference, machine learning, and artificial intelligence methods. Substantively, I leverage this expertise to answer questions related to reproductive and perinatal epidemiology, nutritional epidemiology, social determinants of health. I am currently an Associate Editor at the American Journal of Epidemiology and Epidemiology, and am to co-lead editor of the AJE Classroom, a section devoted to methods education. My recent contributions to science include: developing a better understanding of the role that daily low-dose aspirin can play in mitigating the risk of adverse pregnancy outcomes among women at high risk of pregnancy loss; identifying potential problems that arise when using machine learning methods to estimate causal exposure effects; exploring the role that machine learning methods can play in quantifying the reproductive effects of different dietary patterns; and the use of mediation analysis methods to better understand factors that explain health disparities. These contributions can be found here: 

Naimi AI, Perkins NJ, Mumford SL, Sjaarda LA, Platt RW, Silver RM, Schisterman EF. The per protocol effect of preconception-initiated low-dose aspirin on conception, pregnancy loss, and live birth. Ann Int Med. Published Online Ahead of Print: Jan 25, 2021.

Naimi AI, Mishler A, Kennedy EH. Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms. Am J Epidemiol. Accepted 29 Oct 2020 (PrePrint Available Here: https://arxiv.org/abs/1711.07137)

Bodnar LM, Cartus AR, Kirkpatrick SI, Himes KP, Kennedy EH, Simhan HN, Grobman WA, Duffy JY, Silver RM, Parry S, Naimi AI. Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes. Am J Clin Nutr.111(6): 1235-1243. PMID: 32108865 PMCID: PMC7266693.

Naimi AI, Schnitzer ME, Moodie EE, Bodnar LM. Mediation Analysis for Health Disparities Research. Am J Epidemiol. 2016; 184(4): 315-24. PMID: 27489089.

More information on my work and my team can be found here: https://ainaimi.github.io