Nguyen Ha Thanh, PhD
College of Engineering and Computer Science
Assistant Professor of AI/Machine Learning
Biography
Dr. Nguyen Ha Thanh received the B.E. degree in Information Technology and the LL.B. degree in Law from Vietnam National University (VNU), Vietnam, and the Ph.D. degree in Information Science from the Japan Advanced Institute of Science and Technology (JAIST), Japan, where he graduated as Valedictorian. He has research experience in Japan at the National Institute of Informatics (NII), including the Principles of Informatics Research Division and the Research and Development Center for Large Language Models, and at the Center for Juris-Informatics (ROIS-DS) in Tokyo. His research has been supported by competitive programs in Japan, including the MEXT Scholarship and research initiatives of the Japan Science and Technology Agency (JST) and the Research Organization of Information and Systems (ROIS).
His research focuses on neuro-symbolic artificial intelligence for institutional decision systems, combining large language models with knowledge representation and reasoning techniques, with applications in law and governance. He has published in international conferences and journals in Natural Language Processing and Artificial Intelligence and Law. Beyond his research publications, he has initiated and led international workshops and competitions in AI and Law, strengthening global collaboration within the AI and Juris-Informatics community. He also collaborates with industry on AI applications in legal technology and data governance. He mentors students and research teams to compete, publish, and develop AI systems at the international level, with a focus on building sustainable research capacity.
• Language Models for Reasoning and Decision Systems
• Knowledge Representation and Automated Reasoning
• AI for Institutional Decision-Making
• Explainable and Governance-Aware AI
• Natural Language Understanding for Complex Institutional Documents
• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• Knowledge Representation and Reasoning
• Responsible and Trustworthy AI
1. Nguyen, H. T., Fungwacharakorn, W., Zin, M. M., Goebel, R., Toni, F., Stathis, K., & Satoh, K. (2025). LLMs for legal reasoning: A unified framework and future perspectives. Computer Law & Security Review, 58, 106165. https://doi.org/10.1016/j.clsr.2025.106165
2. Breton, J., Billami, M. M., Chevalier, M., Nguyen, H.-T., Satoh, K., Trojahn, C., & Zin, M. M. (2025). Leveraging LLMs for legal terms extraction with limited annotated data. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-025-09448-8
3. Nguyen, H.-T., Phi, M.-K., Ngo, X.-B., Tran, V., Nguyen, L.-M., & Tu, M.-P. (2024). Attentive deep neural networks for legal document retrieval. Artificial Intelligence and Law, 32, 57–86. https://doi.org/10.1007/s10506-022-09341-8
4. Zin, M. M., Nguyen, H.-T., Satoh, K., Sugawara, S., & Nishino, F. (2023). Improving Translation of Case Descriptions into Logical Fact Formulas using LegalCaseNER. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (pp. 462–466). https://doi.org/10.1145/3594536.3595141
5. Vuong, Y. T.-H., Bui, Q. M., Nguyen, H.-T., Nguyen, T.-T.-T., Tran, V., Phan, X.-H., Satoh, K., & Nguyen, L.-M. (2023). SM-BERT-CR: a deep learning approach for case law retrieval with supporting model. Artificial Intelligence and Law, 31(4), 601–628. https://doi.org/10.1007/s10506-022-09319-6
6. Nguyen, H.-T., Wachara, F., Nishino, F., & Satoh, K. (2022). A Multi-Step Approach in Translating Natural Language into Logical Formula. In Legal Knowledge and Information Systems (pp. 103–112). IOS Press. https://doi.org/10.3233/FAIA220453
7. Nguyen, H.-T., Nguyen, M.-P., Vuong, T.-H.-Y., Bui, M.-Q., Nguyen, M.-C., Dang, T.-B., Tran, V., Nguyen, L.-M., & Satoh, K. (2022). Transformer-Based Approaches for Legal Text Processing. The Review of Socionetwork Strategies, 16(1), 135–155. https://doi.org/10.1007/s12626-022-00102-2
8. Kien, P. M., Nguyen, H.-T., Bach, N. X., Tran, V., Nguyen, M. L., & Phuong, T. M. (2020). Answering Legal Questions by Learning Neural Attentive Text Representation. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 988–998). Barcelona, Spain. International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.86
• 2022: Ph.D. in Information Science, Japan Advanced Institute of Science and Technology, Japan
• 2017: M.E. in Software Engineering, Vietnam National University, Vietnam
• 2017: LL.B. in Law, Vietnam National University, Vietnam
• 2015: B.E. in Information Technology, Vietnam National University, Vietnam
• 2022: Ph.D. Valedictorian, Japan Advanced Institute of Science and Technology (JAIST)
• 2018: MEXT Scholarship, Government of Japan
• 2016: ITPEC TopGun Representative, Information Technology Professional Examination Council
• 2015: Honda Y-E-S Award, Honda Foundation
• Reviewer for international journals and conferences in Artificial Intelligence, Natural Language Processing, and Artificial Intelligence and Law.
• Organizer and scientific committee member of workshops and research events in AI, logic, and knowledge representation, including workshops held in conjunction with major conferences such as KR and ICDM.
• Principal investigator and collaborator in competitive research projects and grants in Japan and international collaborations on artificial intelligence, large language models, and legal informatics.
• Member of the question design and review committee for the Information Technology Professional Examination Council (ITPEC), contributing to the development of international IT certification examinations.
• Advisor for AI and legal technology initiatives, contributing to the development of AI systems for legal analytics, data governance, and institutional decision support.
• Mentor and advisor for student teams participating in international AI competitions and research projects.
