Baoxiang Wang
Assistant Professor at The Chinese University of Hong Kong, Shenzhen
Hong Kong, China
Summary
🤩
Rockstar🎓
Top SchoolBaoxiang Wang is an Assistant Professor at The Chinese University of Hong Kong, Shenzhen with 11 years of experience bridging machine learning research and applied engineering. He holds a Ph.D. in Computer Science and Engineering and began his career with research roles and internships at Siemens Corporate Research, Cubist Systematic Strategies, and RBC Institute of Research, bringing academic rigor to industry problems. His hands-on work includes integrating and optimizing BERT-based text classification models in PyTorch, covering data preprocessing, tokenization, and model architecture tweaks for real-world performance. Based in Hong Kong, he combines deep technical expertise in information security and NLP with a track record of moving research advances into reproducible code and benchmarks. An intriguing detail: he balances academic leadership with practical ML engineering, maintaining open benchmarks that help standardize text-classification evaluation.
11 years of coding experience
1 year of employment as a software developer
The Chinese University of Hong Kong (CUHK)Bachelor of Engineering (B.E.), Information Security, Bachelor of Engineering (B.E.), Information Security at Shanghai Jiao Tong University
Github Skills (7)
pytorch10
machine-learning10
nlp10
text-classification10
bert10
python9
data-preprocessing9
Programming languages (3)
Github contributions (5)
FreedomIntelligence/TextClassificationBenchmark
Dec 2017 - Aug 2019
A Benchmark of Text Classification in PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:42 commits, 6 PRs, 172 pushes in 1 year 8 months
Contributions summary:Baoxiang focused on integrating and modifying a BERT model for text classification within the PyTorch framework. They implemented a BERT-based model, and also experimented with different layers. The commits showcase modifications to data loading, model architecture, and configurations to support and optimize the BERT model's performance. Furthermore, the user worked on data preprocessing and tokenization.
pytorchnlpbertword-embeddingsdeep-learning
FreedomIntelligence/qnn
Jul 2018 - Apr 2019
Contributions:276 commits, 1 PR, 215 pushes in 8 months
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