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My research interests include
causal mediation analysis of big data problem;
(generalized) regression for covariance matrix outcomes.
Mediation analysis with high-dimensional mediators
Mediation analysis with high-dimensional mediators
Causal diagram with multiple causally dependent mediators.
Causal diagram of proposed reduced model relaxing the ordering assumption in the mediators.
Identified brain pathways using Pathway Lasso in a classification learning task fMRI study with reaction time as the outcome of interest.
Granger mediation analysis for multiple time series
Granger mediation analysis for multiple time series
Mediation model with correlated errors for unmeasured confounding.
Integrate Granger causality to account for spatio-temporal dependency.
Functional mediation analysis
Functional mediation analysis
Treatment, mediator and outcome are all functional data.
Study dynamics in the causal effects.
Multiview Data Integration
Multiview Data Integration
Integrate data collected from two sources following a biological mechanistic assumption.
Treat data from different imaging modalities as two blocks of high-dimensional mediators.
Regression for covariance matrix outcomes
Regression for covariance matrix outcomes
Two-dimensional data contour plot. Covariance matrices vary as continuous X varies.
Integrate principal component analysis principle with generalized linear model.
In the projection space, data variation depends on covariates through a log-linear model.
Identify linear projection and model coefficient simultaneously.
Application in resting-state fMRI study.
Brain regions with high positive loadings.
Brain regions with high negative loadings.
Brain subnetwork (component) identified in a resting-state fMRI study shows significant treatment effect in semantic aphasia patients.
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