Rate My Professor Thanh Nguyen

Nguuyen's research specializations include social network mining, stream processing, big data analytics, deep learning applications, and artificial intelligence. His work addresses critical challenges such as deepfake creation and detection, machine unlearning techniques, anomaly and rumour detection in social media data streams, adaptive network alignment with convolutional networks, crowdsourcing aggregation, web table search summarization, and adversarial attacks on graph neural network-based recommender systems. His publications feature prominently in leading journals and conferences, including Computer Vision and Image Understanding, ACM Transactions on Intelligent Systems and Technology, Applied Soft Computing, IEEE International Conference on Data Engineering (ICDE), Proceedings of the VLDB Endowment, and ACM Transactions on Information Systems. Highly cited papers encompass "Deep learning for deepfakes creation and detection: A survey" (2022, 1104 citations), "A Survey of Machine Unlearning" (2025, 606 citations), "Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions" (2020, 351 citations), "Monitoring agriculture areas with satellite images and deep learning" (2020, 222 citations), "Adaptive network alignment with unsupervised and multi-order convolutional networks" (2020, 146 citations), "From anomaly detection to rumour detection using data streams of social platforms" (2019, 115 citations), and "Poisoning GNN-based recommender systems with generative surrogate-based attacks" (2023, 103 citations). These contributions have profoundly impacted AI trustworthiness, data analytics, and applications in healthcare, agriculture, and cybersecurity.