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| <h2 class="section-title">Experience</h2> |
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| <div class="timeline-item"> |
| <div class="timeline-date">August 2025 - January 2026</div> |
| <h3> |
| MedMAS: Multi-agent System for Pre-intake Clinical Note |
| Generation in Conversation |
| </h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| BioInfomatic Laboratory, Feng Chia University, Taiwan |
| </p> |
| <p class="timeline-content"> |
| Building a multi-agent system that extracts patient information |
| through conversations, generates targeted follow-up questions to |
| gather comprehensive patient data, and creates detailed |
| pre-visit clinical reports. This streamlines the examination |
| process, saving physician time and improving patient experience |
| during medical consultations. The system is evaluated across |
| three core tasks: Named Entity Recognition (NER), Question |
| Generation (QG), and Summarization, achieving state-of-the-art |
| results on both MTS-Dialog and CliniKnote benchmark datasets, |
| demonstrating the superiority of multi-agent architectures over |
| conventional approaches such as in-context learning and |
| instruction-tuning in the medical domain. |
| </p> |
| <p |
| style=" |
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| font-size: 0.9rem; |
| margin-top: 0.5rem; |
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| > |
| <strong>Advisor:</strong> Prof. Fang-Rong Hsu |
| </p> |
| </div> |
|
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| <div class="timeline-item"> |
| <div class="timeline-date">September 2024 - January 2026</div> |
| <h3>ViLegalLM: Language Models for Vietnamese Legal Text</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| NLU Laboratory, Hung Yen University of Technology and Education |
| </p> |
| <p class="timeline-content"> |
| ViLegalLM comprises one representation model (135M) and two |
| generation models (1.54B, 1.72B) specifically for Vietnamese |
| legal text through continual pretraining on newly 16GB of |
| high-quality legal documents. ViLegalLM achieves |
| state-of-the-art performance across 10 benchmarks spanning four |
| main tasks: Information Retrieval (IR), Question Answering (QA), |
| Natural Language Inference (NLI), and Syllogism Reasoning, |
| outperforming 7 state-of-the-art Vietnamese models and |
| establishing new strong baselines for Vietnamese legal text |
| processing. The project also contributes three large-scale |
| synthetic training datasets to address the shortage of |
| high-quality legal training data in Vietnam. |
| </p> |
| <p |
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| > |
| <strong>Advisor:</strong> Assoc. Prof. Minh-Tien Nguyen |
| </p> |
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| <div class="timeline-item"> |
| <div class="timeline-date">August 2025 - September 2025</div> |
| <h3>Adaptive Weighted Ensemble for Legal Text Processing</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| NLU Laboratory, Hung Yen University of Technology and Education, |
| Vietnam |
| </p> |
| <p class="timeline-content"> |
| Designed a framework combining multiple bi-encoders through |
| query-specific confidence calculation, advanced dynamic |
| weighting, and ensemble score fusion with cross-encoder |
| reranker. Achieved 3rd place in Legal Information Retrieval task |
| (F2-score: 0.8482, 7.51% improvement) and 2nd place in Legal |
| Question Answering (97.56% accuracy) in ALQAC 2025 Competition. |
| Paper accepted at 17th International Conference on Knowledge and |
| System Engineering (KSE 2025). |
| </p> |
| <p |
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| > |
| <strong>Advisor:</strong> Assoc. Prof. Minh-Tien Nguyen |
| </p> |
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| <div class="timeline-item"> |
| <div class="timeline-date">February 2024 - September 2025</div> |
| <h3> |
| IntelliChat - Question Answering System for Vietnam Legal |
| Documents |
| </h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| NLU Laboratory, Hung Yen University of Technology and Education, |
| Vietnam |
| </p> |
| <p class="timeline-content"> |
| Built a demo legal question-answering system for Vietnamese, |
| integrating information retrieval with answer |
| extraction/generation optimized for the legal domain. |
| IntelliChat outperforms GPT-3.5 and state-of-the-art open-source |
| LLMs (~7B parameters) in both automatic and human evaluations, |
| and is deployed online to enable Vietnamese citizens to |
| independently access and understand legal documents. |
| </p> |
| <p |
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| > |
| <strong>Advisor:</strong> Assoc. Prof. Minh-Tien Nguyen |
| </p> |
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|
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| <div class="timeline-item"> |
| <div class="timeline-date">February 2024 - June 2025</div> |
| <h3>QACTune - Advanced Legal Information Retrieval Framework</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| NLU Laboratory, Hung Yen University of Technology and Education, |
| Vietnam |
| </p> |
| <p class="timeline-content"> |
| Developed a novel fine-tuning framework leveraging |
| Question-Context-Answer relationships for enhancing legal |
| information retrieval in low-resource settings. Average |
| improvements of 3.9% and 4.8% in MAP@100. Published in |
| Engineering Applications of Artificial Intelligence (WoS-SCIE, |
| Q1, IF: 8.0). |
| </p> |
| <p |
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| > |
| <strong>Advisor:</strong> Assoc. Prof. Minh-Tien Nguyen |
| </p> |
| </div> |
|
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| <div class="timeline-item"> |
| <div class="timeline-date">July 2023 - January 2024</div> |
| <h3> |
| ViEduQAG - Vietnamese Question and Answer Generation in |
| Education |
| </h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| NLU Laboratory, Hung Yen University of Technology and Education, |
| Vietnam |
| </p> |
| <p class="timeline-content"> |
| Pioneered Vietnamese Question-Answer Generation research in |
| education domain by creating ViEduQA - the first comprehensive |
| Vietnamese educational QAG dataset with 12,618 QA pairs across |
| 319 lessons from 4 high school subjects. Published in SOICT 2024 |
| (Springer CCIS). |
| </p> |
| <p |
| style=" |
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| > |
| <strong>Advisor:</strong> Assoc. Prof. Minh-Tien Nguyen |
| </p> |
| </div> |
| </div> |
| </div> |
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| <div class="timeline-date">February 2025 – July 2025</div> |
| <h3> |
| NLP Fresher @ Artificial Intelligence Institute JSC, Hung Yen, |
| Vietnam |
| </h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| SmartChat - Smart AI Assistant for Vietnamese |
| </p> |
| <p class="timeline-content"> |
| Led comprehensive evaluation of large language models for |
| production chatbot system, focusing on optimizing performance |
| across multiple NLP tasks including document reranking, question |
| rewriting, and content generation. Conducted systematic |
| benchmarking of state-of-the-art models including GPT-4o-mini, |
| Amazon Nova Lite, Amazon Nova Micro, and Amazon Nova Pro. |
| </p> |
| <p |
| style="color: var(--accent); font-weight: 600; margin-top: 1rem" |
| > |
| AI Assistant for Vietnam Ministry of Agriculture |
| </p> |
| <p class="timeline-content"> |
| Developed a comprehensive domain-specific chatbot system |
| enabling intelligent question-answering capabilities over legal |
| document collections and regulatory text corpora. Built |
| innovative text-to-analytics functionality and designed a novel |
| document chunking mechanism combining Depth-First Search (DFS) |
| algorithms with advanced Regular Expression patterns. |
| </p> |
| </div> |
|
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| <div class="timeline-item"> |
| <div class="timeline-date">October 2024 – January 2025</div> |
| <h3>AI Intern @ Viettel Telecom Corporation, Hanoi, Vietnam</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| Per-Title Encoding |
| </p> |
| <p class="timeline-content"> |
| Developed and optimized per-title encoding algorithms for video |
| compression on the TV360 streaming platform. Implemented |
| advanced analysis techniques to assess video complexity and |
| dynamically adjust encoding parameters, achieving optimal |
| balance between visual quality and file size. |
| </p> |
| <p |
| style="color: var(--accent); font-weight: 600; margin-top: 1rem" |
| > |
| Video Frame Interpolation |
| </p> |
| <p class="timeline-content"> |
| Designed and implemented advanced video frame interpolation |
| systems to enhance motion smoothness for TV360 platform content |
| delivery. Developed sophisticated interpolation algorithms using |
| deep learning techniques to generate high-quality intermediate |
| frames. |
| </p> |
| <p |
| style="color: var(--accent); font-weight: 600; margin-top: 1rem" |
| > |
| Video Quality Assessment for User Generated Content |
| </p> |
| <p class="timeline-content"> |
| Built comprehensive video quality assessment frameworks for |
| evaluating and enhancing user-generated content on the TV360 |
| platform using computer vision and machine learning techniques. |
| </p> |
| </div> |
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| <div class="timeline-item"> |
| <div class="timeline-date">April 2024 – July 2025</div> |
| <h3>Natural Language Processing - Teaching Assistant</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| CS19TN, Hung Yen University of Technology and Education |
| </p> |
| <p class="timeline-content"> |
| Assisted in teaching Natural Language Processing course, guiding |
| students through fundamental and advanced NLP concepts, helping |
| with assignments and projects. |
| </p> |
| </div> |
|
|
| <div class="timeline-item"> |
| <div class="timeline-date">April 2024 – July 2025</div> |
| <h3>Natural Language Processing - Key Mentor</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| UTEHY-NLU Lab |
| </p> |
| <p class="timeline-content"> |
| Mentored students in NLP research projects, guiding them through |
| literature review, experimental design, and paper writing. |
| </p> |
| </div> |
|
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| <div class="timeline-item"> |
| <div class="timeline-date">December 2023 – July 2025</div> |
| <h3>Deep Learning - Key Mentor</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| UTEHY-NLU Lab |
| </p> |
| <p class="timeline-content"> |
| Guided students in deep learning fundamentals and applications, |
| covering neural networks, CNNs, RNNs, and Transformers. |
| </p> |
| </div> |
|
|
| <div class="timeline-item"> |
| <div class="timeline-date">June 2023 – July 2025</div> |
| <h3>Machine Learning - Key Mentor</h3> |
| <p style="color: var(--accent); font-weight: 600"> |
| UTEHY-NLU Lab |
| </p> |
| <p class="timeline-content"> |
| Mentored students in machine learning concepts and practical |
| applications, covering supervised and unsupervised learning |
| algorithms. |
| </p> |
| </div> |
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