Rutgers University-New Brunswick · Mathematics & StatisticsCardiovascular Data Science Lab
Liangyuan Hu · New Brunswick, New Jersey
Research Focus
Dr. Liangyuan Hu's lab focuses on developing advanced statistical methods for analyzing complex data from cardiovascular health studies. By utilizing Bayesian machine learning techniques, the lab aims to address issues such as missing data and causal inference in longitudinal cohort studies. This research is vital for improving our understanding of cardiovascular diseases and informing public health strategies.
Bayesian machine learningcausal inferencelongitudinal datacardiovascular diseasesmissing data analysissensitivity analysisR programminghealth policy
Research Aims
5AIM 01Develop methods to handle missing longitudinal covariates in cardiovascular studies.
AIM 02Create a scalable Bayesian framework for causal inference in complex data.
AIM 03Design a continuous-time model for analyzing survival outcomes in health data.
AIM 04Assess the impact of unmeasured confounding on causal effect estimates.
AIM 05Produce R software packages to make new analytical methods accessible to researchers.
Skills the Lab Values
Bayesian analysismachine learninglongitudinal data analysisstatistical modelingR programmingsurvival analysisdata imputationsensitivity analysissoftware development
Output against the department
Publications115bottom 18% of dept.108th of 131
this lab
most publishedleast published
Citations1.8kbottom 14% of dept.113th of 131
this lab
most citedleast cited
Career totals from OpenAlex, compared against labs that share a department. A department spans everything from one paper to tens of thousands, so bar heights are on a log scale — otherwise the largest lab would flatten every other bar to a line.