Huynh Ly Tan Khoa received his bachelor’s degree in Information Technology from Ho Chi Minh City University of Foreign Languages and Information Technology (HUFLIT) in 2026. During his undergraduate studies, he developed a strong interest in applying computational methods to language-related problems. His research focuses primarily on natural language processing and artificial intelligence, with particular attention to text classification, sentiment analysis, large language models, and intelligent language-based applications. He is also interested in exploring machine learning and deep learning techniques to develop accurate, efficient, and practical solutions that can support research and address real-world challenges in language understanding and communication.
Huynh Ly Tan Khoa
Journal Papers
[1]
Nguyen Thi Thuy A, Huynh Ly Tan Khoa, Nguyen Minh Y, and Tieu Phung Mai Suong, “A MULTI-METHOD STUDY ON INTENT DETECTION IN HUMAN-MACHINE INTERACTION,” HUFLIT Journal of Science, vol. 10, no. 3, pp. 37, 2026, doi: 10.71091/2354-113X/385.
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[2]
Le Hong Quang, Huynh Ly Tan Khoa, and Thanh Le, “Evaluating GPT-OSS-20B Model for Hate Speech Detection: Advances in Parameter-Efficient Adaptation,” HUFLIT Journal of Science, vol. 10, no. 3, pp. 11, 2026, doi: 10.71091/2354-113X/372.
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Conference Papers
[1]
Nguyen Phuoc Dai, Huynh Ly Tan Khoa, Luu Van Nhat Hao, Y. Nguyen Minh, Bay Vo, and Thien Khai Tran, “Benchmarking Conventional, Deep, and Large Language Models on Vietnamese Clickbait Detection,” in Proc. Intelligent Systems and Data Science: ISDS 2025, Communications in Computer and Information Science, vol. 2714, pp. 489-496, 2026, doi: 10.1007/978-981-95-3358-9_35.
[2]
Thanh-Long Nguyen, Phan Hoang Minh Phuoc, Huynh Ly Tan Khoa, Y. Nguyen Minh, and Luu Van Nhat Hao, “Integrating Compositional and Contextual Protein Representations for Improved Allergenicity Assessment,” in Proc. Computational Intelligence in Engineering Science: ICCIES 2026, Communications in Computer and Information Science, vol. 2944, pp. 318-328, 2026, doi: 10.1007/978-3-032-21628-1_23.