Rate My Professor Chee-Ming Ting

Ting's research focuses on statistical signal processing, machine learning, deep neural networks, computational statistics, and statistical models for networks, applied to biomedical signal and image analysis, neuroimaging including fMRI and EEG, brain connectivity analysis, and computer-aided detection for disease prediction and patient monitoring. He has published over 38 journal papers, 64 refereed conference papers, and 6 book chapters. Key publications include 'Adaptive Graph Learning with Multi-graph Convolutions for Brain Disorder Classification' (2026), 'Multi-Modal masked autoencoder and parallel Mamba for 3D brain tumor segmentation' (2026), 'Connectome-GTC: A Unified Framework for Brain Functional and Structural Connectomes Generation, Translation, and Classification' (2025), and 'Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation' (2025). As principal investigator and co-investigator, he has obtained grants totaling over RM2.5 million, graduated 4 PhD students and 5 Master's students, and currently supervises 10 PhD and Master's students. Ting received Research Excellence Awards for the Best IEEE Journal Paper from the IEEE Signal Processing Society Malaysia in 2019, 2022, and 2023 for brain network analysis research, along with several national and international innovation awards. He serves as Associate Editor for Statistics and Data Science in Imaging and Frontiers in Human Neuroscience, is a Senior Member of the IEEE Signal Processing Society, and has refereed for over 30 journals and IEEE conferences while contributing to technical and organizing committees.