Jiaheng Wei
Sunnyvale, California, United States
Summary
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RockstarJiaheng Wei is an assistant professor and researcher with seven years' experience at the intersection of weakly-supervised learning, trustworthy AI, and applied ML systems. He completed a PhD in Computer Science at UC Santa Cruz after graduate work at Brown and earlier maths training at Xi'an Jiaotong University and NUS, and has held research roles at Google, ByteDance, and Accenture where he transitioned briefly into senior research management. His work emphasizes robust learning from human-generated data and LLM evaluation without gold-standard labels, blending theoretical rigor with practical evaluations in industry settings. Based in Guangzhou, he bridges academia and industry, moving rapidly from doctoral research to faculty leadership while maintaining hands-on contributions to applied AI problems. An uncommon thread in his career is frequent short-term industry research stints during his PhD, signaling an ability to translate novel methods into real-world evaluation pipelines.
7 years of coding experience
Github Skills (18)
deep-learning10
deep-neural-networks10
paper10
robustness10
weakly-supervised-learning10
cifar1009
language-model9
classifiers6
rlhf6
cgan5
pytorch5
stargan4
arxiv4
generative-adversarial-network4
dcgan3
Programming languages (2)
Github contributions (5)
UCSC-REAL/cifar-10-100n
Oct 2021 - Jul 2022
Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at http://www.noisylabels.com/.
Contributions:17 commits, 18 pushes, 7 comments in 9 months
cifar100label-noise
weijiaheng/Advances-in-Label-Noise-Learning
Apr 2021 - Dec 2022
A curated (most recent) list of resources for Learning with Noisy Labels
Contributions:120 commits, 1 PR, 118 pushes in 1 year 8 months
noisy-labelsrobustnesslabel-noisenoisy-label-learningdeep-neural-networks
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