Causal simulation of physiological systems
Interdisciplinary Areas: | Engineering and Healthcare/Medicine/Biology, Data/Information/Computation |
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Project Description
This project aims to study the causal model of physical systems, such as fluid dynamics models using structure causal models (SCM). Structure causal model requires the problem domain represented by a graphical representation, where nodes represent variables and directional edges represent cause-effect relationship. More specifically, the project will study simulation in causal inference with networks-based structural equation models [1]. One particular area of the study will be reviewing the simcausal package in R [2], and establish protocols and graphical models for generating data using fluid dynamics models of heart. The goal is to better understand the mechanism of heart and generate data from the graphical model similar to the distribution of the data generating mechanism.
Start Date
Summer/Fall 2019
Postdoc Qualifications
PhD in Computer Science or Statistics or related quantitative field
Co-advisors
Paul Griffin, Professor, Industrial Engineering