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Dr Hua Mao

Assistant Professor

School: Computer Science

Hua Mao

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. 

  • Please visit the Pure Research Information Portal for further information
  • A Unified Framework and Benchmark Study for Optimization of Diverse, Plausible, and Actionable Counterfactual Explanations, Sanderson, J., Mao, H., Woo, W. 18 Jul 2026, Agents and Artificial Intelligence - 17th International Conference, ICAART 2025, Revised Selected Papers, Cham, Switzerland, Springer
  • CACE: A Framework for Generating Counterfactual Explanations Aligned with Causal Structure via Jointly Learned Conditional Distributions, Sanderson, J., Mao, H., Yi, Q., Woo, W. 1 Jul 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. 1 Sep 2026, In: Pattern Recognition
  • Temporally Coherent Counterfactual Explanations for Time Series Forecasting, Hayes, L., Sanderson, J., Kho, A., Mao, H., Woo, W. 20 Jun 2026, ECML PKDD 2026 proceedings, Cham, Switzerland, Springer
  • 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
  • Jacob Sanderson ??Encoding Structural Knowledge for Plausible Counterfactual Generation? Start Date: 01/10/2023
  • Lucas Hayes Temporally Coherent Counterfactual Explanations Start Date: 05/05/2026
Computing Science PhD June 30 2013

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