Dr Hua Mao

Assistant Professor

School: Computer Science

Research Themes and Scholarly Interests

Dr. Hua Mao has been an assistant professor in the Department of Computer and Information Sciences at Northumbria University since July 2019. Before that, she was with the School of Computer Science at Sichuan University, China. She worked as a Lecturer, followed by an Associate Professor from Jan. 2014 – Dec. 2018. Dr. Mao received her Ph.D. degree from Aalborg University, Denmark, in 2013. Her research interests include deep learning and AI.

Key Publications

Please visit the Pure Research Information Portal for further information
CACE: A Framework for Generating Counterfactual Explanations Aligned with Causal Structure via Jointly Learned Conditional Distributions, Sanderson, J., Mao, H., Yi, Q., Woo, W. 30 Apr 2026, In: IEEE Transactions on Knowledge and Data Engineering
Conditional Distribution Learning for Graph Classification, Chen, J., Mao, H., Liu, C., Wang, Z., Peng, X. 14 Mar 2026, Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence, Singapore, Association for the Advancement of Artificial Intelligence (AAAI)
Homophilic-aware graph contrastive learning, Zhang, L., Mao, H., Woo, W., Chen, J. 16 Feb 2026, In: Pattern Recognition
Cross-View Graph Consistency Learning for Invariant Graph Representations, Chen, J., Mao, H., Woo, W., Liu, C., Peng, X. 11 Apr 2025, Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, Washington DC, United States, Association for the Advancement of Artificial Intelligence (AAAI)
DiPACE: Diverse, Plausible and Actionable Counterfactual Explanations, Sanderson, J., Mao, H., Woo, W. 25 Feb 2025, Proceedings of the 17th International Conference on Agents and Artificial Intelligence, Scitepress
Efficient algorithms for collecting the statistics of large-scale IP address data, Liu, H., Cao, Y., Cai, Z., Mao, H., Chen, J. 18 Jul 2025, In: Computer Science and Information Systems
GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks, Sanderson, J., Mao, H., Woo, W. 1 Oct 2025, In: IEEE Transactions on Artificial Intelligence
Hierarchical Sparse Representation Clustering for High-Dimensional Data Streams, Chen, J., Mao, H., Gou, Y., Peng, X. Oct 2025, In: IEEE Transactions on Neural Networks and Learning Systems
One-Step Adaptive Graph Learning for Incomplete Multiview Subspace Clustering, Chen, J., Mao, H., Woo, W., Liu, C., Wang, Z., Peng, X. 1 May 2025, In: IEEE Transactions on Knowledge and Data Engineering
Progressive Low-Confidence Pseudolabeling for Semisupervised Node Classification, Zhou, T., Mao, H., Liu, H., Chen, J. 1 Dec 2025, In: Neurocomputing

Current and Recent Projects

Please visit the Pure Research Information Portal for further information
COVER - Cooperative and intelligent unmanned aerial vehicles for emergency response applications, Chen, X. (Principal Investigator), Mao, H. (Co Investigator), European Commission, 01/07/23 - 30/06/27, £63,601.00

PGR Supervision

Jacob Sanderson Explainable Artificial Intelligence: Improving AI Interpretability through Advances in Counterfactual Explanations Start Date: 01/10/2023
Lucas Hayes Explainable AI for Adaptive Multi-Modal Flood Prediction Start Date: 05/05/2026