Introduction
Welcome to my personal website!
About Me
I am Rui Dong, currently working as an Instructor in Neurological Sciences (in Neurology and in the Gertrude H. Sergievsky Center) at Columbia University Irving Medical Center. With a solid foundation in mathematics and statistics, my academic journey has been dedicated to unraveling the genetic underpinnings of complex traits. My career encompasses a blend of methodological advancements and practical applications in statistical genetics, driven by a passion for advancing our understanding of genetics and its relation to disease etiology.
I hold a Ph.D. in Mathematics from Tsinghua University (advisor: Prof. Stephen S.-T. Yau), where our major research interest is in evolutionary analysis. My Ph.D. thesis is on genome-scale analysis of populational genetics using alignment-free approaches based on the 1000 Genomes Project. My academic background also includes a Bachelor of Science in Mathematics from Sun Yat-sen University. Later on I did my first postdoc (mentor: Prof. Donald Rubin) at Yau Mathematical Sciences Center, Tsinghua University on causal inference in medical design and we also collaborated on a research project to study the SARS-CoV-2 variants (with Prof. Andrew Zhou from Peking University). My second postdoc is under the supervision of Prof. Suzanne M. Leal and Prof. Gao T. Wang at Columbia University Irving Medical Center.
Research Interests
My research focuses on developing and applying statistical methodologies to understand the genetic architecture of complex traits. Currently, I am working on projects involving gene-environment interactions, heritability, and genetic correlations of traits such as Alzheimer’s disease (AD) and age-related hearing impairment (ARHI). By leveraging data from large-scale cohorts like the UK Biobank, my goal is to uncover significant genetic associations and improve our understanding of disease mechanisms.
In addition, I am exploring the use of omics data to investigate the underlying mechanisms of complex traits. This involves examining proteomic data to identify genetic associations with various phenotypes and employing advanced statistical methods to analyze these relationships.
Through my research, I aim to contribute to the field of statistical genetics by providing insights that can inform disease prevention, diagnosis, and treatment strategies.
