Bryan Wilie
PhD Student at The Hong Kong University of Science and Technology
Hong Kong Island, Hong Kong, China
Summary
👤
Senior🎓
Top SchoolBryan Wilie is a PhD student and NLP researcher at HKUST with eight years of industry and research experience focused on reasoning and knowledge-intensive NLP. He has held senior data science and principal roles at JULO and contributed as a deep learning researcher and engineer at institutions including ITB and Coursera, bridging research rigor with product-focused deployment. His open-source work includes enhancing the widely used NL-Augmenter toolkit, where he robustly implemented number-to-word transformations covering dates, currencies, fractions, and phone numbers—improving real-world text perturbation for robustness testing. Based in Hong Kong, he combines academic inquiry with practical engineering, often refactoring and hardening code to move prototypes toward production. Bryan’s background in electrical/drive systems and PLC programming early in his career gives him an uncommon systems perspective for an NLP specialist. He is active in regional NLP communities (HLTCHKUST, IndoNLP) and pursues research that connects symbolic knowledge and modern language models.
8 years of coding experience
6 years of employment as a software developer
Bachelor of Engineering (B.Eng.), Bachelor of Engineering (B.Eng.) at Institut Teknologi Bandung (ITB)SMAK 1 PENABUR JakartaHong Kong University of Science and Technology (HKUST)
English, Indonesian, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
text-manipulation10
regular-expression10
nlp10
strings10
python10
natural-language-processing10
testing8
Programming languages (3)
Github contributions (5)
GEM-benchmark/NL-Augmenter
Jun 2021 - Aug 2021
NL-Augmenter 🦎 → 🐍 A Collaborative Repository of Natural Language Transformations
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 reviews, 60 commits, 1 PR in 1 month
Contributions summary:Bryan primarily contributed to the `numeric_to_word` transformation within the `nl-augmenter` repository. Their work focused on implementing number-to-word conversion logic, including handling date formats and currency symbols. The user added features for fractional numbers and incorporated logic for recognizing and transforming phone numbers. They also restructured and refactored existing code, improving the overall functionality of the number transformation component.
nlpinformation-theorysentencedeep-learningmachine-learning
SamuelCahyawijaya/greenformer-demo
Sep 2021 - Sep 2021
Contributions:21 commits, 19 pushes in 5 days
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