Jiaheng Wei
Assistant Professor at The Hong Kong University of Science and Technology
Guangzhou City, Guangdong Province, China
Summary
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Rockstar🎓
Top SchoolJiaheng 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
4 years of employment as a software developer
Master's degree Data Science, Master's degree Data Science at Brown UniversityBachelor of Science - BS Honors Science Program(Mathematics/Qian xuesen College) & Gifted Young Program, Bachelor of Science - BS Honors Science Program(Mathematics/Qian xuesen College) & Gifted Young Program at Xi'an Jiaotong UniversityUniversity of California Santa CruzNon-Exchange Programme MOU with XJTU Zhufeng Programme Mathematics, Non-Exchange Programme MOU with XJTU Zhufeng Programme Mathematics at National University of Singapore
Github Skills (15)
deep-learning10
weakly-supervised-learning10
paper10
robustness10
deep-neural-networks9
noise8
rlhf5
language-model5
pytorch4
mnist4
generative3
generative-adversarial-network2
tensorflow1
cyclegan1
dcgan1
Programming languages (1)
Github contributions (5)
UCSC-REAL/cifar-10-100n
Oct 2021 - Jul 2022
Contributions:17 commits, 18 pushes, 7 comments in 9 months
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
deep-learningdeep-neural-networksinternational-conferencelabel-noiselearning-with-label-noise
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