VinNLP

About Us

Who We Are

VinNLP is the Natural Language Processing (NLP) research group at VinUniversity, dedicated to advancing AI-driven language technologies with a strong focus on low-resource languages, machine learning, and applied NLP research.

We collaborate with academic institutions, industry partners, and government bodies to develop cutting-edge LLMs, text summarisation techniques, machine translation models, and more.

Our Mission

Our mission is to push the boundaries of NLP research by developing language technologies for underrepresented languages, improving multilingual AI, and contributing to the global AI research community.

Our Focus Areas

Low-resource language modelling
Text summarisation & information extraction
Machine translation & multilingual NLP
AI applications in healthcare, finance, and social sciences
Ethical AI and bias mitigation in NLP

We are part of VinUniversity’s College of Engineering and Computer Science (CECS) and collaborate with leading international research groups to create impactful AI-driven solutions.

VinNLP Leaders

Dr. Mo El-Haj

Director, VinNLP
Associate Professor (Reader) in NLP

Research focus: LLMs for low-resource languages, text summarisation & information extraction.

Prof. Wray Buntine

Professor in NLP
Director, Computer Science Program

Research focus: Probabilistic graphical models, deep learning, and NLP.

Dr. Le Duy Dung

Assistant Professor, Computer Science
Director of Data Science Program

Research focus: Data science, AI applications, and data-driven NLP research.

Researchers and PhD Students

Our group supports PhD students working on NLP-related research.

[Kieu Hai Dang] – Research focus: [Research Topic]
[Vo Diep Nhu] – Research focus: [Research Topic]
[Ta Quang Hieu] – Research focus: [Research Topic]
[Nguyen Tien Dong] – Research focus: [Research Topic]

Apply to join our research group as a PhD student Explore professional opportunities with our NLP team Explore professional opportunities at VinUniversity

Research & Projects

Low-resource language processing: Developing AI models for underrepresented languages.
Text summarisation: Creating models for extractive and abstractive summarisation.
Machine translation: Improving multilingual and domain-specific translations.
Sentiment analysis & opinion mining: Extracting emotions and opinions from texts.
Question answering & information retrieval: Advancing AI-driven search and knowledge retrieval.
Conversational AI & chatbots: Developing domain-specific virtual assistants.