Abstract. Researcher with 3+ years of experience developing production-ready AI solutions, specializing in Large Language Models and MLOps systems. Demonstrated research excellence through multiple publications on A* conferences. Strong mathematical foundation combined with hands-on expertise in building end-to-end ML pipelines, retrieval-augmented generation systems, and scalable infrastructure. Focused on advancing research in AI Safety, Efficient Learning, and Speech Processing.
1 Recent Activity
[1] Traveled to San Diego, USA to present two accepted papers at ACL 2026. July 2, 2026.
[2] Became a peer reviewer for NeurIPS 2026, a top-tier ML conference, for the first time. May 7, 2026.
[3] Had two papers accepted at ACL 2026 (1 Findings, 1 Workshop). April 6, 2026.
2 Recent Publications
[1] Quy-Anh Dang, Chris Ngo. Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection. Findings of the Association for Computational Linguistics: ACL 2026. [pdf]
[2] Quy-Anh Dang, Chris Ngo. Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR. Proceedings of Multilinguality in the Era of Large Language Models @ ACL 2026. [pdf]
[3] Quy-Anh Dang, Chris Ngo, Truong-Son Hy. RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models. Principled Design for Trustworthy AI: Interpretability, Robustness, and Safety across Modalities @ ICLR 2026. [pdf]
3 Recent Work
[1] BlogLocal MLOps Mastery: Your Complete Guide to Building ML Systems on Your Machine. A practical 8-week roadmap for mastering the full MLOps lifecycle—data versioning, experiment tracking, model APIs, monitoring, and CI/CD pipelines—using only free, local, open-source tools. June 3, 2025.
[2] BlogUnderstanding AUROC: A Mathematical and Practical Perspective. Dive into the AUROC metric—its mathematical foundation, interpretation, and practical pros and cons in evaluating binary classification models. May 3, 2025.
[3] BlogBias-Variance Tradeoff in Machine Learning. A rigorous mathematical decomposition of prediction error into bias, variance, and irreducible noise—with practical intuitions on how to balance them for better-generalizing models. May 3, 2025.