Kaiwen Wu
Graduate Student Researcher
California, United States
Summary
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Rockstar🎓
Top SchoolKaiwen 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 DiegoBachelar 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 TelecommunicationsUniversitat Politècnica de València
English, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (41)
pytorch10
modular10
gaussian-processes10
bayesian10
stochastic-processes10
gaussian10
gpu-acceleration10
python9
machine-learning9
gpu9
optimization9
deep-learning8
image-synthesis7
high-resolution7
neural-network7
Programming languages (3)
Github contributions (5)
kaiwenwu96/stochastic-optimization-svd
Jul 2019 - Oct 2020
Contributions:5 commits, 10 pushes, 1 branch in 1 year 3 months
watml/fast-wasserstein-adversarial
Apr 2020 - Dec 2020
Implementation of Wasserstein adversarial attacks.
Contributions:26 commits, 5 pushes, 1 comment in 8 months
adversarial-attackspythonrobustnessadversarial-machine-learningadversarial
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