I am a recent Ph.D. graduate in Electrical and Computer Engineering, Seoul National University (SNU). With hands-on experience in dry/wet lab and knowledge in deep learning techniques and the biomedical field, adept at performing interdisciplinary research that requires generating, processing, and interpreting biomedical data. Proficient in developing deep learning models from scratch (using Pytorch); setting up bioinformatics pipeline to analyze molecular data including DNA, and RNA; and designing biomolecular experiments including cell culture and single-cell DNA/RNA-seq.

Academic Bio

I received a Ph.D. in Electrical and Computer Engineering from the Seoul National University (SNU). I was very fortunate to be advised by:

My diploma thesis focused on graph neural network model that is optimized to detect the contextual prognostic pathology biomarkers from gigapixel whole slide images.

Research Focus

Interested in knowledge-infusion learning, causal inference, graph neural network, computational pathology, bioinformatics, single-cell, spatial omics, tumor microenvironment. Especially, I thought the biomedical field has an enormous amount of accumulated human knowledge, which is rarely incorporated into the deep learning method. Thus, I’m interested in the knowledge-infused learning and knowledge-infused interpretation of the deep learning model trained for systematic analysis of given biomedical datasets.