The Young Laboratory at KAIST draws upon ideas from data science, applied statistics, and machine learning to tackle fundamental questions in quantitative biology and biomedical engineering. Our algorithms are optimized according to a specific data generation process which means we often ask for copies of experimental protocols and lab notes from our collaborators. In particular, we are interested in (1) decoding the human genome by developing probabilistic models at single-nucleotide resolution and (2) encoding those molecular insights within the context of large biological networks.
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