Young Jung is a machine learning engineer with 8 years of experience blending research and production-grade systems, currently advancing data-centric AI for multimodal foundation models and agentic systems at Apple. He has a PhD from KAIST and a track record of translating ML research into deployable software across domains including genomics, video, and high-performance distributed systems. At Inocras he led genomic foundation models and an ultra-sensitive MRD detection pipeline (JCO 2025), while also cutting cloud pipeline costs by 80% through HPC/cloud re-architecting. His KAIST work produced BWA-MEME, a widely deployed, ML-accelerated read alignment tool that reduced compute and memory overheads substantially, and LiveNAS, an online super-resolution system for live video (SIGCOMM 2020). Young combines domain depth in genomics with systems-level optimization skills, making robustness and generalization arise from smarter data and architecture choices rather than just larger models. Based in San Diego, he favors pragmatic, cost-aware solutions that scale from lab validation to production.
8 years of coding experience
Doctor of Philosophy - PhD, Electrical Engineering, Doctor of Philosophy - PhD, Electrical Engineering at 한국과학기술원(KAIST)
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