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Deep Learning Methods for Public Health Prediction

Alexander Rodríguez, University of Michigan
E18-304

Abstract: Epidemic prediction is an essential tool for public health decision-making and strategic planning. Despite its importance, our ability to model the spread of epidemics remains limited, largely due to the complexity of social and pathogen dynamics. With the increasing availability of real-time multimodal data and advances in deep learning, a new opportunity has emerged to capture and exploit previously unobservable facets of the spatiotemporal dynamics of epidemics. Toward realizing the potential of AI in public health, my work addresses multiple challenges in this domain,…

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Inference for ATE & GLM’s when p/n→δ∈(0,∞)

Rajarshi Mukherjee, Harvard University
E18-304

Abstract In this talk we will discuss statistical inference of average treatment effect in measured confounder settings as well as parallel questions of inferring linear and quadratic functionals in generalized linear models under high dimensional proportional asymptotic settings i.e. when p/n→δ∈(0,∞) where p, n denote the dimension of the covariates and the sample size respectively . The results rely on the knowledge of the variance covariance matrix Σ of the covariates under study and we show that whereas √n-consistent asymptotically…

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Massachusetts Institute of Technology
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