Khoa Huynh

I am a machine learning researcher and engineer with a strong technical foundation in Generative AI, efficient deep learning architectures, and multi-modal retrieval systems. I have published in Q1 journals and top-tier conferences, reflecting both research depth and practical impact. I am motivated to collaborate with cross-functional teams to research, prototype, and deploy clean, scalable, and testable ML solutions that address real-world client challenges.

I have hands-on experience designing and optimizing deep learning models, including Generative AI systems and Retrieval-Augmented Generation (RAG) pipelines. My work emphasizes model efficiency, system optimization, and reproducible experimentation, bridging the gap between research and production. I have contributed to end-to-end ML workflows—from problem formulation and model development to evaluation and integration into larger systems.

My academic background has provided me with a solid grounding in machine learning theory, deep learning, and advanced AI systems. Through research-driven coursework and publications, I have developed strong analytical skills and the ability to conduct independent, high-quality research, particularly in Generative AI and multi-modal learning.