Matthew Yang is a software engineer and UCLA master's student with seven years of experience spanning machine learning, web development, and cloud-native systems. He combines academic research—co-authoring a diffusion-based foundational model for tabular data generation—with production experience at Google and fast-moving internships at Zoox and Poppin where he built dataset registries, autotuning pipelines, and high-traffic frontends. Certified in machine learning and AWS architecture, he designs scalable, decoupled microservices and automated deployment workflows that have materially improved training efficiency and ML lifecycle velocity. Matthew has led small teams to deliver evaluation frameworks for synthetic data privacy and utility, and built preprocessing pipelines capable of handling up to 500GB using distributed techniques. Comfortable moving between client-facing roles and core product development, he brings both rigorous research instincts and pragmatic engineering to shipping reliable systems. Based in Los Angeles, he also has a background spanning CMU and UWaterloo collaborations hinted at in his GitHub bio, reflecting broad academic and industry ties.
7 years of coding experience
3 years of employment as a software developer
Primary and Secondary Education, Primary and Secondary Education at SMIC Private School
Bachelor of Science - BS, Computer Science and Engineering, Bachelor of Science - BS, Computer Science and Engineering at University of California, Los Angeles
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