Kaiwen Wu is a graduate student researcher in California specializing in theoretical deep learning and its intersections with cognitive processes, with eight years of research experience spanning adversarial example analysis, class-agnostic feature extraction, and predictive coding models. Based at UC San Diego and collaborating with the San Diego Supercomputer Center, Kaiwen profiles applications and generates labeled datasets to support deep-learning performance prediction. His work blends rigorous MS-level study in Artificial Intelligence with hands-on lab experiments on layerwise vulnerability of networks and biologically inspired binocular predictive coding. Preparing for PhD study, he brings a strong engineering foundation from a high-achieving undergraduate degree in Internet of Things and a knack for connecting theoretical insights to practical profiling and labeling pipelines.
8 years of coding experience
University of California, San Diego
Bachelar of Engineering, The Internet of Things Engineering, Accumulative GPA 91.72/100, Bachelar of Engineering, The Internet of Things Engineering, Accumulative GPA 91.72/100 at Beijing University of Posts and Telecommunications
Contributions:5 commits, 10 pushes, 1 branch in 1 year 3 months
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