Huynh Thanh Trung, PhD
College of Engineering and Computer Science
Assistant Professor - Computer Science
Biography
Dr. Huynh Thanh Trung received his B.E. and M.E. degrees in Information Systems from the Hanoi University of Science and Technology, Vietnam, in 2016 and 2017, respectively. He obtained his Ph.D. in Artificial Intelligence from Griffith University, Australia, in June 2022, where his research primarily focused on advanced machine learning and data mining techniques.
From 2022 to 2025, he worked as a Postdoctoral Researcher at the Swiss Federal Institute of Technology Lausanne (EPFL), contributing to cutting-edge research in AI and data-driven systems. Dr. Trung’s research interests encompass machine learning, deep neural networks, graph mining, computer vision, social network analysis, fake news detection, and large language models. He has authored and co-authored over 20 research papers in leading international journals and conference proceedings, which have collectively attracted more than 1,400 citations and earned him an h-index of 16.
He actively contributes to the research community as a reviewer for top-tier venues including WWW, WSDM, TKDE, ICDE, VLDB, and IJCAI. His work bridges theoretical AI research and real-world applications, particularly in analyzing complex data, detecting misinformation, and leveraging large language models for knowledge extraction and reasoning.
• Applied Machine Learning Techniques
• Deep neural networks
• Graph mining
• Computer vision
• Social network analysis
• Fake news detection
• Large language models
• Quantitative Financial Analysis
• Applied Machine Learning and Data Mining
• Deep Neural Networks and Modern AI Architectures
• Graph Mining and Network Analysis
• Computer Vision and Visual Intelligence
• Social Network Analysis and Information Diffusion
• Misinformation and Fake News Detection
• Large Language Models and Generative AI
• Quantitative and Computational Financial Analysis
Journal Articles (Q1, Top-tier)
1. TOIS 2024 (Q1, IF 5.7) – Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen, Hongzhi Yin, Thanh Tam Nguyen, and Nguyen Quoc Viet Hung. “Certified Unlearning for Federated Recommendation.” ACM Transactions on Information Systems, 2024.
2. VLDBJ 2023 (Q1, IF 5.63) – Nguyen Thanh Tam, Thanh Trung Huynh, Hongzhi Yin, Matthias Weidlich, Thanh Thi Nguyen, Thai Son Mai, and Quoc Viet Hung Nguyen. “Detecting rumours with latency guarantees using massive streaming data.” The VLDB Journal, 2023.
3. Information Sciences 2023 (Q1, IF 9.01) – Nguyen Thanh Tam, Thanh Trung Huynh, Matthias Weidlich, Quan Thanh Tho, Karl Aberer, and Quoc Viet Hung Nguyen. “Scalable maximal subgraph mining with backbone-preserving graph convolutions.” Information Sciences, 2023.
4. TKDE 2022 (Q1, IF 6.977) – Huynh Thanh Trung, Tong Van Vinh, Nguyen Thanh Tam, Hongzhi Yin, Matthias Weidlich, and Nguyen Quoc Viet Hung. “Learning Holistic Interactions in LBSNs with High-order, Dynamic and Multi-role Contexts.” IEEE Transactions on Knowledge and Data Engineering, 2022.
5. TKDE 2021 (Q1, IF 6.977) – Huynh Thanh Trung, Duong Chi Thang, Nguyen Thanh Tam, Tong Van Vinh, Abdul Sattar, Hongzhi Yin, and Nguyen Quoc Viet Hung. “Network Alignment with Holistic Embeddings.” IEEE Transactions on Knowledge and Data Engineering, 2021.
6. TKDE 2021 (Q1, IF 6.977) – Nguyen Thanh Tam, Huynh Thanh Trung, Hongzhi Yin, Tong Van Vinh, Darnbi Sakong, Bolong Zheng, Nguyen Quoc Viet Hung. “Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks.” IEEE Transactions on Knowledge and Data Engineering, 2021.
7. ESWA 2020 (Q1, IF 6.954) – Huynh Thanh Trung, Nguyen Thanh Toan, Tong Van Vinh, Hoang Thanh Dat, Duong Chi Thang, Nguyen Quoc Viet Hung, and Abdul Sattar. “A comparative study on network alignment techniques.” Expert Systems with Applications, 2020.
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Conference Papers (A, A-ranked)*
1. EMNLP 2024 (A*) – Nguyen Tuan Dung, Thanh Trung Huynh, Minh Hieu Phan, Phi Le Nguyen, Nguyen Quoc Viet Hung. “CARER – ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction.” Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
2. PKDD 2024 (A) – Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen, Thanh Tam Nguyen, Nguyen Quoc Viet Hung, and Karl Aberer. “Distillation-free federated unlearning with provable robustness.” European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (PKDD), 2024.
3. WSDM 2023 (A*) – Thanh Trung Huynh, Minh Hieu Nguyen, Thanh Tam Nguyen, Phi Le Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer. “Efficient integration of multi-order dynamics and internal dynamics in stock movement prediction.” Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM), 2023.
4. ICDE 2021 (A*) – Nguyen Thanh Tam, Huynh Thanh Trung, Hongzhi Yin, Tong Van Vinh, Darnbi Sakong, Bolong Zheng, Nguyen Quoc Viet Hung. “Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks.” IEEE 37th International Conference on Data Engineering (ICDE), 2021.
5. ICDE 2020 (A*) – Huynh Thanh Trung, Tong Van Vinh, Nguyen Thanh Tam, Hongzhi Yin, Matthias Weidlich, and Nguyen Quoc Viet Hung. “Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks.” IEEE 36th International Conference on Data Engineering (ICDE), 2020.
• 2022: Ph.D. in Artificial Intelligence, Griffith University, Australia
• 2017: M.E. in Information System, Hanoi University of Science and Technology
• 2016: B.E. in Information System, Hanoi University of Science and Technology
• Best conference paper award, PRICAI, 2019
• Full PhD Scholarship for International Student, Griffith University, 2018
