My research includes:

1. Inferring Causal Relationships in Genetics: Using Multivariable Mendelian Randomization (MVMR) to estimate causal effects between gene expression and clinical traits like coronary artery disease, leveraging genetic instruments with pleiotropic effects.
2. Bias Analysis of Causal Inference Methods: Comparing Instrumental Variable (IV) and Proximal Causal Learning (PCL) methods under violated assumptions, demonstrating PCL's robustness.
3. Negative Controls and Breast Cancer Genes: Developing methods to identify valid negative controls and infer causal genes for breast cancer using proximal causal learning.

1. Genome-Scale Algorithms: Lectured on Matrix eQTL and its applications using R.
2. Statistical Inference: Taught false discovery rate estimation based on the paper "Statistical significance for genome-wide studies."
3. Causal Inference in Genetics: Introduced causal inference concepts using seminal papers, including "Critical reasoning on causal inference in genome-wide linkage and association studies."
4. Workshop: Delivered a 90-minute workshop on causal inference for PhD students at the NORA Research School Annual Conference 2022, Oslo, Norway.

1. Prediction of Causal Genes at GWAS Loci: Demonstrated how MVMR with correlated instruments can predict causal genes at loci with pleiotropic effects, applied to coronary artery disease loci.

1. Multivariable Mendelian Randomization: Predicting causal genes for Coronary Artery Disease.
2. Proximal Causal Learning vs. Instrumental Variables: Systematic evaluation of biases in causal inference methods.
3. Negative Control Identification for Breast Cancer Genes: Employing proximal causal learning to identify valid negative controls and causal genes.