Junxian He
Assistant Professor at The Hong Kong University of Science and Technology
Hong Kong, China
Summary
🤩
Rockstar🎓
Top SchoolJunxian He is an Assistant Professor at HKUST specializing in natural language processing and statistical machine learning, with a decade of experience spanning academia and industry. He earned his PhD in NLP from Carnegie Mellon University and has held research internships at Facebook AI Research and Salesforce Research, informing a pragmatic, research-driven approach to problems. Junxian previously taught and conducted research at Shanghai Jiao Tong University's John Hopcroft Center, bridging foundational theory with applied NLP systems. His work blends deep learning and probabilistic modeling, and his academic-industrial trajectory suggests a focus on translational research that moves novel algorithms toward real-world deployment. Outside publications, his personal website documents projects that reveal an interest in reproducible research and open sharing of models and code.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD Natural Language Processing, Doctor of Philosophy - PhD Natural Language Processing at Carnegie Mellon UniversityBachelor’s Degree Infomation Engineering, Bachelor’s Degree Infomation Engineering at Shanghai Jiao Tong University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (64)
text-generation10
pytorch10
machine-translation10
casl10
python10
text-summarization10
variational-autoencoder10
machine-learning10
inference10
multimodal10
style-transfer10
llm10
audio10
deep-learning10
image-generation10
Programming languages (5)
Github contributions (5)
jxhe/efficient-knnlm
Aug 2021 - Jan 2022
Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)
Contributions:20 commits, 11 pushes, 1 branch in 5 months
language-modelnearest-neighbor-searchpytorch
jxhe/unify-parameter-efficient-tuning
Jun 2021 - Mar 2022
Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning" (ICLR 2022)
Contributions:141 commits, 11 pushes, 13 comments in 9 months
transfer-learning
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