School of Computing & Innovative Technologies
School of Computing & Innovative Technologies
Dr. Dang Quang Vinh
Dr. Quang-Vinh Dang is a Senior Lecturer at British University Vietnam (BUV), specialising in Artificial Intelligence, Machine Learning, Mathematical Optimization, Graph Learning, and Data Science. He joined BUV as a Lecturer in 2024 and was promoted to Senior Lecturer in 2026.
He has authored or co-authored more than 80 publications indexed in Scopus and/or SCI-E, including numerous articles published in Q1 journals. He currently has an h-index of 18 and has received more than 1,000 citations. His recent research covers AI security, large language models, vision-language models, federated learning, conformal prediction and uncertainty quantification, graph neural networks, retrieval-augmented generation, cybersecurity, mathematical optimization, and AI applications in finance, education, and decision-making.
Alongside his academic career, Dr. Dang has nearly 20 years of experience in the software, data, and AI industries. He has held senior technical and leadership positions at major technology companies and financial institutions, including roles as Head of Data Science and Head of Data/AI in large corporations, digital platforms, fintech companies, and banks. His work has involved building and leading multidisciplinary Data Science and AI teams, developing enterprise AI strategies, and delivering large-scale production systems.
He is currently a Senior Data & AI Consultant at SmartOSC, where he leads and advises enterprise Data and AI initiatives across banking, financial services, manufacturing, retail, and other sectors. His industry expertise spans Generative AI, Agentic AI, large language models, recommender systems, dynamic pricing, mathematical optimization, fraud and risk analytics, enterprise AI architecture, knowledge systems, and large-scale machine learning solutions.
- Artificial Intelligence and Machine Learning
- Mathematical Foundations of AI and Machine Learning
- Mathematical Optimization and Operations Research
- Trustworthy, Explainable and Responsible AI
- Large Language Models and Agentic AI
- Multimodal AI and Vision-Language Models
- Graph Neural Networks and Graph Learning
- Conformal Prediction and Uncertainty Quantification
- Retrieval-Augmented Generation and Knowledge Graphs
- Federated and Privacy-Preserving Machine Learning
- AI Security and Cybersecurity
- AI for Finance and Decision-Making
- AI in Higher Education
- Dang, Q.-V., Vu, H.-V., Nguyen, N.-S.-A., Dinh, M.N., & Le, D. (2026).
“CAPS: Compositional Attack Path Scoring for LLM Deployment Stacks.”
Artificial Intelligence and Applications.
DOI: 10.47852/bonviewAIA620210609.
Q1; published in a journal ranked within the top 3% of its Scopus category. - Nguyen, T.-H.-H., & Dang, Q.-V. (2026).
“Work-integrated learning in Vietnamese higher education: governance models, institutional diversity and graduate employability in the Asian century.”
Higher Education, Skills and Work-Based Learning.
DOI: 10.1108/HESWBL-05-2026-0390.
Q1. - Dang, Q.-V. et al. (2026).
“Real-time hallucination correction in vision-language models using dynamic knowledge graph verification.”
Discover Artificial Intelligence.
Q1. - Dang, Q.-V., Nguyen, N.-S.-A., & Vo, T.-B.-D. (2026).
“CONFIDE: CONformal Free Inference for Distribution-Free Estimation in Causal Competing Risks.”
Mathematics, 14(2), 383.
Q1. - Dang, Q.-V. et al. (2026).
“FORTRESS-FL: Byzantine-Robust and Privacy-Preserving Federated Learning.”
Array.
Q1.
- Dang, Q.V., 2019, December. Reinforcement learning in stock trading. In International conference on computer science, applied mathematics and applications (pp. 311-322). Cham: Springer International Publishing.
- Dang, Q.V., 2020, October. Active learning for intrusion detection systems. In 2020 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 1-3). IEEE.
- Dang, Q.V. and Vo, T.H., 2022. Reinforcement learning for the problem of detecting intrusion in a computer system. In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 2 (pp. 755-762). Springer Singapore.
- Dang, Q.V. and Ignat, C.L., 2016, August. Computational trust model for repeated trust games. In 2016 IEEE Trustcom/BigDataSE/ISPA (pp. 34-41). IEEE.
- Dang, Q.V., 2021. Right to be forgotten in the age of machine learning. In Advances in Digital Science: ICADS 2021 (pp. 403-411). Springer International Publishing.
- Principal Investigator – Research Laboratory, British University Vietnam
Leading a research laboratory focusing on mathematical problems in artificial intelligence and machine learning, including mathematical optimization, graph learning, uncertainty quantification, trustworthy AI, and related theoretical and applied research. - Google Computing Grant – 2026
USD 5,000 in computing support. - Google Computing Grant – 2025
USD 5,000 in computing support. - IUH Research Grant – 2022
Intrusion Detection Using LLM - IUH Research Grant – 2021
Intrusion Detection
- PhD in Computer Science (2014–2018)
Université de Lorraine, Nancy, France
Thesis: Trust Assessment in Large-Scale Collaborative Systems - Post-Master in Software Technology (2013–2014)
Eindhoven University of Technology, the Netherlands - MSc in Computer Science (2009–2012)
Vietnam National University – Hanoi, Vietnam - MSc in Financial Engineering (2019–2021)
WorldQuant University, USA - BSc in Computer Science – Talented Programme (2005–2009)
Vietnam National University – Hanoi, Vietnam - Bachelor of Law (2010–2013)
Hanoi Law University, Vietnam
- British University Vietnam
Senior Lecturer, 2026–Present
Lecturer, 2024–2026 - RMIT University Vietnam
Lecturer, 2023–2024 - Industrial University of Ho Chi Minh City
Lecturer, 2019–2023
