Hiram Ring
Director at Encoding AI
Singapore
Summary
👤
Senior🎓
Top SchoolHiram Ring is a Director and computational linguist based in Singapore with a decade of experience applying machine learning to natural language tasks and a parallel academic career in Austroasiatic descriptive and historical linguistics. He builds practical NLP tools—contributing Electra support to the popular fast-bert library—and leads applied research at Encoding AI while lecturing at Nanyang Technological University. His work spans text classification, sentiment analysis, information extraction, and semi-automated methods for grammatical reconstruction using word vectors. A published field linguist and former postdoc, he combines deep field experience documenting minority languages with hands-on ML engineering, a rare bridge between theoretical linguistics and production NLP. Outside tech, he runs an independent record label and brings audio engineering, production, and event management skills to multidisciplinary projects.
10 years of coding experience
Doctor of Philosophy (Ph.D.), Linguistics, Doctor of Philosophy (Ph.D.), Linguistics at Nanyang Technological UniversityBA, Intercultural Studies, Linguistics, Art, BA, Intercultural Studies, Linguistics, Art at Houghton University
English, French, Spanish, Persian, pnar, Swahili
Github Skills (9)
transformers10
pytorch10
nlp10
electra10
bert10
natural-language-processing10
python9
machine-learning9
fastai8
Programming languages (9)
Github contributions (5)
appvision-ai/fast-bert
Jun 2020 - Jun 2020
Super easy library for BERT based NLP models
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 8 PRs, 20 comments in 1 day
Contributions summary:Hiram primarily contributed to the `fast-bert` library by adding support for the Electra model. Their commits involved integrating Electra into the existing codebase, including updates to the model definitions, data loading, and learner classes. They modified the `fast_bert/learner_cls.py`, `fast_bert/modeling.py`, `fast_bert/data_lm.py`, and `fast_bert/learner_lm.py` files to incorporate Electra for classification and language modeling tasks. Additionally, the user made a minor change to the scheduler within `fast_bert/learner_util.py`.
bertnatural-language-processingfast-bertfastaitransformers
lingdoc/trs2txt
Nov 2015 - Jan 2019
Contributions:3 releases, 23 commits, 8 PRs in 3 years 2 months
toolboxtranscriberfieldworkconverter
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