Dabin Jeong

Dabin Jeong

AI for Life Science Researcher

About me

I am an AI for Life Science researcher at the Lotfollahi Lab, Wellcome Sanger Institute, working at the intersection of machine learning and biomedical science.

I see myself as a problem finder as much as a problem solver. My background in bioinformatics and biochemistry leads me to look for gaps where existing computational approaches fall short. Rather than simply applying established AI frameworks to biology, I focus on identifying under-explored problems in the domain and developing novel solutions to address the problems. I translate the complexity of biological and medical questions into well-defined computational problems, then build the models needed to solve them.

💡 Research Interest

  • Multi-modal learning of biomedical images and spatial transcriptomics
  • Regulatory DNA sequence generation
  • Temporal cell dynamics modeling

🚩 Research Vision

“What I cannot create, I do not understand” — Richard Feynman

I was drawn to computational biology by the idea that DNA is the ultimate code: a sequence in which both code and the codebook are deeply entangled. My research ambition is to build a program that can simulate biological systems from DNA sequence.

🔥 News

  • 🎉 Sep 2026 · LocoGen, a locality-aware generative model for designing regulatory DNA, was accepted to NeurIPS 2026.
  • 🎉 Jul 2026 · Selected as one of 500 participants from over 6,000 applicants in the Anthropic × Gladstone Claude Life Sciences Hackathon, where I built a generative model that infers missing cell states across time points (LinkedIn post).
  • 🎉 Jun 2026 · SIGMMA was accepted to the ICML FM4LS/SD4H workshop; a journal version is in preparation (manuscript).
  • 🎉 Oct 2024 · Joined the Wellcome Sanger Institute.

💼 Professional Experience

Senior Data Scientist · Wellcome Sanger Institute, UK · Oct 2024 – present

  • Led SIGMMA, a hierarchical contrastive learning framework aligning H&E histopathology with spatial transcriptomics (ICML 2026 workshop paper; journal version in preparation)
  • Led LocoGen, a generative framework for de novo regulatory DNA design that uses property-guided generation for out-of-distribution objectives in a lab-in-the-loop cycle (NeurIPS 2026)

Research Intern · LG AI Research, Korea · Jan – Apr 2024

  • Contributed to ExaonePath, a self-supervised foundation model for H&E histopathology images (press)
  • Built a scalable, reproducible multi-omics preprocessing pipeline

🧬 Projects

🔬 Spatial transcriptomics × histopathology

  • SIGMMA · Lead · 2025 – present
    Hierarchical contrastive learning aligning H&E histopathology with spatial transcriptomics.
    ICML 2026 workshop · journal version in preparation
  • ExaonePath · Contributor · 2024, LG AI Research
    Self-supervised foundation model for H&E histopathology images.
    press

🧬 Regulatory DNA generation

  • LocoGen · Lead · 2025 – present
    Generative framework for de novo regulatory DNA design that uses property-guided generation for out-of-distribution objectives in a lab-in-the-loop cycle.
    NeurIPS 2026

⏱️ Temporal cell dynamics

  • Cell-state inference across time · 2026, Anthropic × Gladstone Hackathon
    A generative model that infers missing cell states across time points.
    LinkedIn post

🧪 Multi-omics biomarker discovery

  • GOAT · Lead · PhD
    Graph neural network for multi-omics biomarker discovery in eosinophilic asthma.
    Bioinformatics, 2023
  • VWA5A as a biomarker in breast cancer · Co-author · PhD
    Machine-learning-based protein prioritization identifying VWA5A as a prognostic marker for breast-cancer metastasis.
    Scientific Reports, 2024
  • Patent (Korea) · Co-inventor
    Multi-omics biomarker discovery method, registered through the Korean Intellectual Property Office.
    KIPRIS

🎓 Education

Ph.D., Interdisciplinary Program of Bioinformatics · Seoul National University, Korea · Sep 2018 – Aug 2024

  • Thesis: Computational modeling of molecular interactions for biomarker discovery using single/multi-omics data
  • 3 first-authored and 10 co-authored papers
  • Developed GOAT, a graph neural network for multi-omics biomarker discovery in eosinophilic asthma (Bioinformatics, 2023)
  • Developed RNA-seq quantification and normalization pipelines for ICGC-ARGO

B.S., Biochemistry · Yonsei University, Korea · Mar 2013 – Aug 2018

Technical Skills

  • Programming · Python, R, Shell, LaTeX
  • ML / Deep Learning · PyTorch, PyTorch Geometric, DGL, scikit-learn
  • Workflow & Reproducibility · Nextflow, Snakemake, Docker, Git

Awards & Achievements

  • 🏆 Invited talk (2023) · National Heart & Lung Institute, Imperial College London (host: Prof. Kian Fan Chung) — Deep graph attention for multi-omics network biomarkers in eosinophilic asthma
  • 🏆 National Excellence Scholarship (2016) · Natural Sciences and Engineering; sole recipient in the department

📷 Photo

Photos coming soon.