Rate My Professor Jeremiah Deng

Professor Jeremiah Deng is a Full Professor in the School of Computing at the University of Otago, where he has served since 2002 and was promoted effective February 2026. He earned a BSc from the University of Electronic Science and Technology of China, an MSc from South China University of Technology, and a PhD from South China University of Technology. As Director of the Telecommunications Programme, he teaches undergraduate and postgraduate courses including INFO 204 (Introduction to Data Science), INFO 411 (Machine Learning and Data Mining), and INFO 508 (Research Project). A Senior Member of both IEEE and ACM, he serves on the editorial board of Cognitive Computation and the board of the New Zealand Artificial Intelligence Researchers Association. Deng co-chairs the Machine Learning for Sensory Data Analysis workshops in conjunction with PAKDD and has served on program committees for international conferences such as IJCAI, PRICAI, ACCV, GlobeCom, ICC, and ECE. He leads the Pattern Recognition and Machine Learning Group and has supervised 12 PhD candidates to completion, many of whom have obtained academic or research positions in New Zealand and abroad.

Deng's research focuses on developing intelligent algorithms for pattern recognition, machine learning, and optimization of computer and network systems. His recent investigations include online adaptive learning algorithms for anomaly detection, scene categorization, semantic video analysis, event detection, performance modeling and optimization of wireless networks, computational intelligence, neural networks, biomedical data analysis, and AI applications in public health. Through interdisciplinary collaborations with neuroscience and computer science colleagues, he identifies brain biomarkers for mental and cognitive disorders using robust machine learning on EEG and fMRI data. Deng has authored or co-authored more than 100 peer-reviewed papers in journals, conference proceedings, and book chapters, with over 3,498 citations on Google Scholar. Notable publications include 'Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states' (Communications Medicine, 2026, with Yue et al.); 'EEG connectivity features associated with fibromyalgia revealed by machine learning' (Frontiers in Pain Research, 2026, with Li et al.); 'Quantum granular-ball generation methods and their application in KNN classification' (Scientific Reports, 2025, with Yuan et al.); 'Quantum color image edge detection algorithm based on Sobel operator' (Quantum Information Processing, 2025, with Yuan et al.); and 'Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking' (PNAS Nexus, 2025, with Tetereva et al.).