Jan. 12, 2021

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Speaker: Ron Sarafian


Iterative learning algorithm for exposure assessment used in generalized linear epidemiological models.​


Studying exposure effects on health is a key area in environmental epidemiology. As direct measurement is impossible, the estimation requires exposure assessments. Spatio-temporal data are combined with ground-true measurements to build an \emph{exposure model}. The exposure model's predictions at the residences of the subjects of the epidemiological study are then used as covariates in an \emph{epidemiological model}, where parameter estimation is the goal. This process is sometimes called a two-stage study. We call upon ideas from the Domain Adaptation and Experimental Design literature to suggest an iterative importance-weighted algorithm that may replace the two stage process, to achieve more accurate estimates in an epidemiological generalized linear model (GLM). We show the optimality of our algorithm In simulation analysis.​ ​

Ron Sarafian


Ron Sarafian is a Ph.D student in the Department of Industrial Engineering, Ben Gurion University of the Negev. His advisors are Dr. Jonathan Rosenblatt and Prof. Itai Kloog. His research interests include spatio-temporal machine learning, statistics and deep learning, with applications in remote-sensing, geoscience, and environmental studies.