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publications
Exploration of Plane Geometry
Published in Vietnam National University Press, 2022
A comprehensive introduction to plane geometry that integrates foundational principles with advanced problem-solving techniques, offering readers a deep and structured understanding of geometric reasoning.
Recommended citation: Le Xuan Hoang, Duc-Anh Nguyen. (2022). Exploration of Plane Geometry. Vietnam National University Press. 2022.
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Determinants of Credit Risk under the Basel II Accord: A Case Study of the Vietnamese Banking Sector
Published in International Conference on Emerging Challenges in Economics and Business (ICECH 2023), 2023
This paper investigates key factors influencing credit risk in Vietnam’s banking sector within the Basel II regulatory framework, providing empirical evidence and policy implications for risk management practices.
Recommended citation: Ngo Thu Giang, Duc-Anh Nguyen, Vu Thi Thao Chi, Nguyen Bao Anh, Nguyen Tai Quang Dinh. (2023). "Determinants of Credit Risk under the Basel II Accord: A Case Study of the Vietnamese Banking Sector." International Conference on Emerging Challenges in Economics and Business (ICECH 2023). 2023.
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Adaptive Conflict-Averse Multi-gradient Descent for Multi-objective Learning
Published in International Conference on Intelligence of Things (ICIT 2023), 2023
This work introduces an adaptive conflict-averse multi-gradient descent algorithm that effectively handles multiple objectives in machine learning tasks, addressing the challenges of conflicting gradients in multi-objective optimization.
Recommended citation: Dinh Van Tuan, Tran Anh Tuan, Duc-Anh Nguyen, Bui Khuong Duy, Tran Ngoc Thang. (2023). "Adaptive Conflict-Averse Multi-gradient Descent for Multi-objective Learning." International Conference on Intelligence of Things (ICIT 2023). 2023.
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Selective Sinkhorn Routing for Improved Sparse Mixture of Experts
Published in Approved for Filing U.S. Patent, Qualcomm, 2025
This work introduces Selective Sinkhorn Routing (SSR), a novel routing mechanism for sparse Mixture-of-Experts (SMoE) models. By formulating token-to-expert assignment as an optimal transport problem with balancing constraints, SSR derives gating assignments directly from a transport map - eliminating the need for auxiliary balancing losses or additional trainable noise. The method promotes balanced expert utilization while preserving flexibility, resulting in faster training, improved accuracy, and greater robustness across language modeling and image-classification tasks. This work introduces a new family of balancing strategies for efficient SMoE training.
Recommended citation: Duc-Anh Nguyen*, Huu Binh Ta*, Nhuan Le Duc, Tan Minh Nguyen, Toan Tran. (2025). "Selective Sinkhorn Routing for Improved Sparse Mixture of Experts." U.S. Patent Application (Filed), Qualcomm. 2025.
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Selected Beautiful Math Problems from International Competitions (Geometry)
Published in Vietnam National University Press, 2025
This book presents a curated collection of challenging and elegant geometry problems drawn from international mathematics competitions across nine renowned countries. Each problem is accompanied by a detailed solution and enriched with supplementary theoretical insights, making the book a valuable resource for students, educators, and enthusiasts seeking to deepen their understanding of geometric problem-solving.
Recommended citation: Nguyen Duy Khuong, Phan Quang Tri, Tran Quoc Dung, Nguyen Duong Minh, Pham Nguyen Phuc Long, Duc-Anh Nguyen. (2025). Selected Beautiful Math Problems from International Competitions (Geometry). Vietnam National University Press. 2025.
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Artificial Intelligence in Metal Additive Manufacturing: Current Status, Challenges, and Future Developments
Published in Journal of Intelligent Manufacturing, 2025
Artificial Intelligence (AI) is emerging as a pivotal technology for advancing Metal Additive Manufacturing (MAM), a process that enables the fabrication of geometrically complex and customized metallic components. In the context of Industry 4.0, AI has become a transformative force, offering powerful tools for data-driven optimization, defect detection, process control, and property prediction in MAM. While numerous studies and review papers have explored AI applications in specific MAM techniques—such as Laser Powder Bed Fusion (LPBF) or Wire Arc Additive Manufacturing (WAAM)—few have provided a comprehensive and balanced evaluation across the broader MAM landscape. Additionally, Existing reviews often focus narrowly on individual processes or AI methods, lacking a unifying perspective to assess the field holistically. To address this gap, we introduce a novel, end-to-end AI-enhanced MAM lifecycle framework (Design → Build → Post-processing → End-of-life) as the foundation for a systematic analysis. Using this framework, we move beyond descriptive summaries to deliver a critical synthesis of current progress, highlighting persistent challenges, including data quality and availability, model interpretability, generalizability across materials and processes, and integration of domain knowledge. For each challenge, we highlight potential solutions, such as the use of physics-informed learning models and adaptive control frameworks. Finally, we propose a roadmap for future developments toward autonomous, intelligent, scalable and real-time MAM systems. This work serves as a foundation for researchers seeking to advance the integration of AI into MAM.
Recommended citation: Duc-Anh Nguyen*, Bui Truong Giang Le*, Van Anh Nguyen, Minh Tuan Vu, Manh Ha Bui, Minh Thanh Le, Trong Duc Nguyen, Tien Dung Hoang, Xuan Hai Le. (2025). "Artificial Intelligence in Metal Additive Manufacturing: Current Status, Challenges, and Future Developments." Journal of Intelligent Manufacturing Published: Talk in session with discussion on the steepest descent method for solving nonconvex minimax problems and its applications to multi-task learning. Presented at the Vietnam Institute for Advanced Study in Mathematics (VIASM) Summer School. Published: Talk in session with discussion on the minimax approach for solving non-convex multiobjective optimization problems and their applications in machine learning and optimization. Undergraduate course, University 1, Department, 2014 This is a description of a teaching experience. You can use markdown like any other post. Workshop, University 1, Department, 2015 This is a description of a teaching experience. You can use markdown like any other post.
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The Steepest Descent Method for Solving Nonconvex Minimax Problem and Applications to Multi-Task Learning
The Minimax Approach for solving non-convex multiobjective optimization and Applications
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