I am an AI for Science researcher at the Lotfollahi Lab, Wellcome Sanger Institute, working at the intersection of machine learning and biomedical science.
My background in bioinformatics and biochemistry shapes how I approach this space — not by fitting biological data into existing AI frameworks, but by asking what a problem actually requires before reaching for a method. I focus on translating the complexity of biological and medical questions into well-defined computational problems, and building the models to solve them.
“What I cannot create, I do not understand”
- Richard Feynman
Academic CV
Current research interest
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Multi-modal learning of spatial gene expression and histopathology image
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Uncertainty-controlled de novo regulatory sequence design
Work experience
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2024.10 – PresentSenior Data Scientist at Sanger Institute
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2024.1 – 2024.4
Research internship at LG AI researchEducation
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2018.9 – 2024.8
Ph.D in Interdisciplinary Program in Bioinformatics at Seoul National University•
2013.3 – 2018.8
BS in Biochemistry at Yonsei University◦
Selected as Fellow of the National Excellence Scholarship (Natural Sciences and Engineering)
Publication
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2026, ECCB (Under revision) “MIL2Het: Learning Patient Phenotypes from Single-Cell Heterogeneity with Multi-view Prior Knowledge-informed Graph Learning”
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2024, BMC genomics (Under revision) “ALPACA: A Visual Data Mining System for Subcellular Location-specific Knowledge Mining from Multi-Omics Data in Cancer”
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Co-lead 2025, BioData Mining “Identification of Severity Related Mutation Hotspots in SARS-CoV-2 Using a Density-Based Clustering Approach”
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Co-lead 2024, Scientific Reports “Identification of VWA5A as a Novel Biomarker for Inhibiting Metastasis in Breast Cancer by Machine-learning Based Protein Prioritization”
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2023, Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics “Deep learning-based survival prediction using DNA methylation-derived 3D genomic information”
Presented by Dabin Jeong at ACM-BCB 2023
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2023, Journal of Korean Medical Science “Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure”
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2022, International Journal of Molecular Science “A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective”
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Lead 2021, Frontiers in Genetics “Construction of Condition-Specific Gene Regulatory Network using Kernel Canonical Correlation Analysis”
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Co-lead 2020, Recent Advances in Biological network Analysis, “Network Propagation for the Analysis of Multi-omics Data”
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Presented by Dabin Jeong at Genome Informatics Workshop (GIW) 2019
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2019, Frontiers in Plant Science “PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation”
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2018, Nucleic Acids Research “TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions”
cited 1,831 times (25.9.11)
Portfolio
Skills
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Python
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R
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Docker
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Nextflow
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Snakemake
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Git
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Shell script
Languages
Korean – native English – advancedSpanish – intermediate