Seungmo Lee is a Computer Science PhD student at UCLA with nine years of hands-on experience building ML-driven software and computational biology tools. As a Graduate Student Researcher in ZarLab and former AI Scientist at LG Uplus, he has applied foundation-model techniques to multivariate time-series anomaly detection and led projects that improved structural-variant calling accuracy by 20% using ensemble methods. His background blends academic research and industry practice, including impactful internship work that produced a gold-standard SV caller for the research community. Based in Los Angeles, he brings a pragmatic focus on translating novel ML methods into reliable software and reproducible research pipelines. Notably, he began his UCLA journey as an undergraduate in CS before continuing into doctoral research, reflecting a deep institutional continuity in his training.
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