Rate My Professor Yuqing Lin

Lin's research interests encompass software engineering, machine learning, data science, graph theory, discrete mathematics, and deep learning. Key areas include source code plagiarism detection, fake news detection using large language models, conversational intention detection, spectral graph theory, graph invariants, multi-criteria decision-making, network optimization, molecular modelling, and artificial intelligence applications. Selected publications feature 'A lightweight cryptographic algorithm incorporating path coloring of cartesian product of graphs' (2026, Scientific Reports), 'ABERT: Adapting BERT model for efficient detection of human and AI-generated fake news' (2025, International Journal of Information Management Data Insights), 'On odd-graceful coloring of graphs' (2025, Communications in Combinatorics and Optimization), 'A scheme for emergency material allocation based on decision makers' subjective preference' (2025, Operational Research), and 'Towards Fake News Detection on Social Media' (2022, IEEE International Conference on Machine Learning and Applications). He has successfully supervised 15 PhD completions on topics ranging from software product lines and interconnection networks reliability to machine learning frameworks and affective analysis. Lin has received awards for Research Performance and HDR Supervision from the Faculty of Engineering and Built Environment. He teaches courses on cutting-edge technologies, Agile development, and DevOps, promoting interdisciplinary collaborations.