CV
Curriculum Vitae of Truong-Binh Duong - Computer Science graduate and AI researcher focusing on multi-modal systems.
Basics
| Name | Truong-Binh Duong |
| Label | AI Researcher |
| duongtruongbinh2003@gmail.com | |
| Phone | +(84) 936212865 |
| Url | https://duongtruongbinh.github.io |
| Summary | Recent Computer Science graduate from VNU-HCM, University of Science specializing in multi-modal AI and Vision-Language Models. Focused on rigorous experimentation, benchmarking, and deploying deep learning systems with PyTorch and Transformers. |
Work
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2024.09 - Present Teaching Assistant
AI VIETNAM
Developing curriculum and teaching materials for the AIO program, covering fundamental Machine Learning, Deep Learning, and advanced topics such as AI Agents and LLM Reasoning.
- Created comprehensive educational content for AI/ML courses
- Designed exam questions for Linear Algebra, Probability, Statistics, ML, and DL
- Teaching materials available at: https://drive.google.com/drive/folders/1JTLMNDsnOKD4Yo1e2x3CZVMUXkbP8KD2
Education
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2021.09 - 2025.08 Ho Chi Minh City, Vietnam
B.Sc. in Computer Science
VNU-HCM, University of Science
Computer Science (High-Quality Program)
- Multi-modal AI
- Deep Learning
- Machine Learning
- Data Science Programming
Awards
- 2021.2024
Dean's List Honoree (5 Semesters)
VNU-HCM, University of Science
Recognition for academic excellence with GPA 3.98/4.0
Publications
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2026.01.01 An Automated Pipeline for Constructing a Vietnamese VQA-NLE Dataset
Proceedings of the Fifth International Conference on Intelligent Systems and Networks (Lecture Notes in Networks and Systems, Springer Nature Singapore)
Officially published ViVQA-X dataset paper detailing an automated, multi-LLM pipeline that constructs the first Vietnamese Visual Question Answering dataset with natural language explanations.
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2025.08.01 Describe Anything Model for Visual Question Answering on Text-rich Images
ICCV Workshop 2025
Multi-modal VQA approach demonstrating strong performance across six benchmark datasets through comprehensive evaluation of Vision-Language Models.
Skills
| Programming | |
| Python | |
| C/C++ | |
| SQL |
| Data Science | |
| Pandas | |
| NumPy | |
| Matplotlib | |
| Seaborn | |
| Scikit-learn | |
| XGBoost |
| Deep Learning | |
| PyTorch | |
| Hugging Face | |
| Transformers | |
| CNN | |
| RNN/LSTM | |
| Vision-Language Models |
| MLOps & Tools | |
| Docker | |
| MLflow | |
| Weights & Biases | |
| FastAPI | |
| Streamlit | |
| Google Cloud (Vertex AI) |
Languages
| Vietnamese | |
| Native speaker |
| English | |
| Professional Working Proficiency (VSTEP B2) |
Interests
| Artificial Intelligence | |
| Machine Learning Research | |
| Computer Vision | |
| Natural Language Processing |
| Technology | |
| Open Source | |
| DevOps |
| Sports & Entertainment | |
| Football | |
| Gaming | |
| Anime | |
| Traveling |
Projects
- 2025.01 - 2025.08
Counterfactual Reasoning for Robust Visual Question Answering
Graduation thesis proposing counterfactual training strategies, batch-contrastive losses, and curriculum learning to mitigate language bias in VQA.
- Three-stage curriculum for stable optimization
- Batch-contrastive loss with counterfactual samples
- State-of-the-art performance on VQA-CP v2 (annotation-free methods)
- 2024.07 - 2024.07
Heineken Image Analysis Tool
Backend and model engineering for a multi-model AI pipeline that automates brand compliance and safety analysis from images.
- FastAPI inference service
- Integrated YOLOv10, Owlv2, PaddleOCR, and CLIP
- Delivered actionable reports for on-site audits
- 2023.04 - 2023.05
AI-Powered Math Solving Assistant
Multi-modal chatbot that solves math problems through Gemini API fine-tuned on MetaMathQA-40K with OCR support for handwritten inputs.
- Vertex AI fine-tuning workflow
- OCR integration for image inputs
- Web interface for interactive tutoring
- 2022.01 - 2022.12
Manga Popularity Prediction
End-to-end data science pipeline for predicting manga scores using scraped metadata, exploratory analysis, and ensemble regression.
- Automated web scraping and preprocessing
- Compared Linear Regression, Random Forest, and XGBoost
- Bayesian Optimization for hyperparameters