Yongjin Park

Assistant Professor

Research Interests

single-cell genomics
Computational Biology
Causal inference
Bayesian machine learning

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.

Research Methodology

bioinformatics tool development
Bayesian modelling
Causal inference
probabilistic programming


Doctoral students
Any time / year round

Single-cell profiling over millions of cells (transcriptomics, epigenomics, proteomics) provides an unprecedented opportunity to dissect fundamental mechanisms of human biology and disease etiology. However, single-cell data's quality (missing values) and sample size (#subjects) pose a significant technical challenge, and state-of-the-art methodology tends to remain in a traditional biostatistical framework. As data sets are shared via a public database/repository, we have no shortage of data and computation power but suffer from the lack of creativity. Therefore, I am looking for a biology-oriented dry lab scientist willing to create and tackle high-risk, high-return projects in close collaboration with computational/statistical people. Here are potential directions, but not limited to:

  • Joint analysis of single-cell measurements with bulk (tissue-level) data, such as GTEx and ENCODE projects
  • Single-cell cell-cell network community detection incorporating gene-gene, protein-protein interactions
  • Ascertainment of the interplay between germline and somatic mutations within cancers and across different complex disorders
  • Deep learning of pleiotropic map to reveal novel contributions of cell types and states in the UK Biobank data
  • Dynamic expression QTL analysis incorporating cell-cell communications in Brain and Cancer
  • Single-cell dissection of polygenic risk score matrix to reveal a map of pleiotropy and polygenicity
  • Identifying the effects of trans-factors (e.g., metabolic changes, hormonal perturbation, sex chromosomes) and their interplay with cis-regulatory mechanisms in complex disorders


  • Strong education backgrounds in fundamental biology, genetics, and systems biology
  • A proficient writer/speaker who loves to communicate with other students and collaborators
  • Literacy in genomics technology and experience in a sequencing analysis pipeline
  • Fluent in data analysis language, e.g., R and Python (beyond R Studio and Jupyter notebook)
  • Working knowledge of a UNIX-like environment (e.g., Linux, Mac OS)
  • Fluent in script languages, e.g., AWK, BASH, Makefile, Snakemake, etc.


I am interested in supervising students to conduct interdisciplinary research.

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