Renhe Huang (Edward Huang) | AI Engineer | Official Website
AI Engineering Intern at Taiwan Mobile, M.S. student in Information Management at National Central University. Specializing in NLP, Deep Learning, and Traditional Chinese AI models. TANet Best Paper Award & NSTC GenAI Stars winner. Explore my projects, articles, and open-source AI models.
Frequently Asked Questions
Who is Renhe Huang (Edward Huang)?
Renhe Huang (黃仁和), also known as Edward Huang, is an AI Engineer and Researcher. He currently works as an AI Engineering Intern at Taiwan Mobile and is pursuing a Master's degree at National Central University in Information Management.
He specializes in Natural Language Processing (NLP), Deep Learning, Traditional Chinese AI model development, and Vision-Language Models (VLMs). He is passionate about turning academic research into real-world applications and actively contributes to Taiwan's AI open-source ecosystem.
What projects has Renhe Huang worked on?
Renhe Huang has led and contributed to numerous AI projects, including:
- [object Object] — BERT-based classifier for Traditional Chinese variants (Taiwan vs Mainland). Achieves 87.71% accuracy with long-text support, Focal Loss, and MC Dropout voting.
- [object Object] — Established the 'Formosa Vision Dialogue Corpus' using images from National Cultural Memory Bank 2.0 to enhance VLM's understanding of Taiwan's landmarks, history, and culture.
- [object Object] — A resume matching system balancing performance and fairness, combining LoRA fine-tuning, adversarial debiasing, and XAI for transparent decision-making.
- [object Object] — A Chinese SMS classification system based on multi-stage clustering and contrastive learning, using SimCSE-style methods to fine-tune Sentence Encoder.
See more on the projects page. Open-source models and datasets are available on the AI models page.
What are Renhe Huang's research interests?
His core research interests include:
- Natural Language Processing (NLP) — Text classification, semantic understanding, language model fine-tuning
- Deep Learning — Model architecture design, training optimization, LoRA fine-tuning
- Traditional Chinese AI — Development and open-sourcing of Traditional Chinese corpora and models
- Vision-Language Models — Multimodal research combining vision and language
- CTR Prediction — Click-through rate prediction for ad recommendation systems
Read his technical insights on the articles page.
How can I contact Renhe Huang?
You can reach out to Renhe Huang through the following channels:
- 📧 Email: renhehuang0723@gmail.com
- 💼 LinkedIn: linkedin.com/in/edwarddata
- 🐙 GitHub: github.com/Edwarddev0723
- 🤗 Hugging Face: huggingface.co/renhehuang
- ✍️ Medium: medium.com/@renhehuang0723
Or visit the contact page to send a message directly.