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Lei Gao received his PhD in Electrical and Computer Engineering from the Ryerson University in 2017. Prior to joining Laurier, he was a Lecturer and Research Associate at the Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University.
His research interests include multi-modal generative artificial intelligence (AI) based learning and computing, interpretable machine learning (ML)/AI, green learning, intelligent healthcare systems, and human-computer interaction (HCI).
- The Best Paper of IEEE International Conference on Multimedia Information Processing and Retrieval
- Best Student Paper of IEEE International Symposium on Multimedia
- Top 10% Papers Award of IEEE International Conference on Image Processing
- The Fellowship at Microsoft Research
- Doctoral Completion Award of Ryerson University
I have research assistantships opportunities for undergraduate and graduate students interested in machine learning, artificial intelligence, and intelligent signal computing.
Lei Gao, Zheng Guo, and Ling Guan. "ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation." IEEE Trans. on Neural Network and Learning Systems (Accept), 2025.
Lei Gao, Kai Liu, Zheng Guo, and Ling Guan. "Mathematics-Inspired Models: A Green and Interpretable Learning Paradigm for Multimedia Computing." ACM Transactions on Multimedia Computing, Communications, and Applications (Accept), 2025.
Lei Gao, Kai Liu, and Ling Guan. "A Discriminative Multi-modal Adaptation Model for Heterogeneous Network-based Video Action Recognition."Neural Networks, vol.185, pp.1-13, 2025.
Lei Gao, Zheng Guo, and Ling Guan. "An Optimal Edge-weighted Graph Semantic Correlation Framework for Multi-view Feature Representation." ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 7, pp. 1-23, 2024.
Lei Gao and Ling Guan. "Interpretability of Machine Learning: Recent Advances and Future Prospects." IEEE Multimedia Magazine, vol. 30, no. 4, pp. 105-118, 2023.
Lei Gao and Ling Guan. "A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation." ACM Trans. on Intelligent Systems and Technology, vol. 14, no. 3, pp. 1-24, 2023.
Lei Gao, and Ling Guan. "A Discriminative Vectorial Framework for Multi-modal Feature Representation." IEEE Trans. on Multimedia, vol.24, pp. 1503-1514, 2022.
Lei Gao, Rui Zhang, Lin Qi, Enqing Chen, Ling Guan. "The Labeled Multiple Canonical Correlation Analysis for Information Fusion." IEEE Trans. on Multimedia, vol. 21, no. 2, pp. 375-387, 2019.
Lei Gao, Lin Qi, Enqing Chen, Ling Guan. "Discriminative Multiple Canonical Correlation Analysis for Information Fusion." IEEE Trans. on Image Processing, vol. 27, no. 4, pp. 1951-1965, 2018.
Lin Qi, Ling Guan."A Novel Discriminative Model for Online Behavioral Analysis with Application to Emotion State Identification." IEEE Intelligent Systems, vol. 31, no. 4, pp. 32-39, 2016.
- CP264 Data Structures