David Roh

David Roh is a Machine Learning Researcher at Texas A&M University, where contributions include designing an ML model for non-invasive glucose monitoring, enhancing prediction accuracy from 72% to 89%, and processing over 4,000 hours of ECG/PPG data. As a Computer Vision/Software Production Engineer at ASML, David automated contamination labeling using PyTorch CV, optimizing processes for substantial cost savings. In prior roles at Meta and JPMorgan Chase, David focused on distributed network infrastructure and job orchestration, respectively. David also served as a Research Fellow at Samsung Semiconductor, developing tools to enhance hardware security and improve analysis efficiency. David holds a Bachelor of Science in Computer Science from Texas A&M University.