Regulatory genomics
Understanding how biological sequences shape gene regulation and cellular function.
AI × Genomics
Seongjun Yang
PhD Student at Cold Spring Harbor Laboratory
I am a PhD student at Cold Spring Harbor Laboratory, where I am fortunate to be advised by Prof. Peter Koo. I develop AI methods that move beyond black-box prediction to uncover interpretable, mechanistic insights from genomic data.
My research spans regulatory genomics, genomic foundation models, model robustness, and evidence-grounded evaluation—with an emphasis on generating testable biological hypotheses.
Research focus
From prediction to mechanism.
I am interested in models that are not only accurate, but also scientifically useful, robust, and grounded in biological evidence.
Genomic foundation models
Building and studying general-purpose representations of genomic and transcriptomic data.
Robust & interpretable AI
Making model behavior reliable, transparent, and useful under real biological variation.
Evidence-grounded evaluation
Evaluating whether model insights are supported by evidence and lead to testable hypotheses.
Updates
Recent news
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Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling was accepted to the ICML 2026 AI for Science Workshop.
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Assessing Patent Value with LLMs: DRAM Dataset was accepted to PACIS 2025.
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Predictive Pipelined Decoding was accepted to TMLR.
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Towards the Practical Utility of Federated Learning in the Medical Domain was accepted to CHIL 2023.
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Joined KRAFTON AI as an NLP Research Engineer.
Background
Along the way
Before starting my PhD, I lived a little like a greedy search algorithm: at each step, I followed the most interesting question in front of me.
From December 2025 to May 2026, I worked at Gruve AI, a U.S.-based AI company, where I developed AI agents in collaboration with a genetic testing company.
Before Gruve AI, I was an NLP Research Engineer at KRAFTON AI, where I worked on LLM post-training, efficient inference, and AI-agent applications for games, including Smart Zoi for inZOI ↗.
I received my M.S. from the Graduate School of AI at KAIST, advised by Prof. Edward Choi. My work explored trustworthy machine learning across healthcare, distribution shifts, and natural language processing.
I earned my B.S. in Computer Science from Yonsei University, where I first became interested in extracting useful structure from language and complex data.