About Yunhak Oh
I’m Yunhak (pronounced “Yoon-hahk”), a Ph.D. candidate in the Graduate School of Data Science (GSDS) at KAIST, advised by Prof. Chanyoung Park. My research explores how machine learning can connect cellular measurements to an understanding of disease and the differences between patients.
My current work focuses on learning representations of cellular states and responses from transcriptomic data, with an emphasis on capturing biologically meaningful variation. I am increasingly interested in how these representations capture disease-related changes and help us understand differences across individuals. My long-term goal is to connect this understanding with therapeutic discovery and design, with a particular interest in RNA therapeutics.
My research began with graph and representation learning to model complex relationships and contexts. Through spatial transcriptomics, I began exploring the biological organization of cells and tissues, later broadening my work to transcriptomics and cellular modeling. Before graduate school, I studied industrial engineering and worked as a Data Scientist and Manager at NielsenIQ. That experience in solving practical problems continues to shape how I connect methodological advances with useful knowledge and real-world value.
Ultimately, my research is motivated by the people living with disease and those who support them. I hope to deepen our understanding of biology and disease, and contribute knowledge and tools that can make a meaningful difference in our lives.
Research interests
- Cellular Representation Learning & Transcriptomics
- Disease Modeling & Patient Heterogeneity
- Therapeutic Discovery & RNA Therapeutics