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Dr Minghao Liu

Lecturer in Computer Science

School of Science & Technology Department of Computer Science

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About

Overview

Dr. Minghao Liu is a Lecturer at the Department of Computer Science, City St George’s, University of London, and a member of the Artificial Intelligence Research Centre. He received the PhD degree in Computer Science from the University of Chinese Academy of Sciences in 2023. From 2023 to 2026, he was a Research Associate at the University of Oxford, where he worked on the AutoCS, FAIR, and ELSA projects.

His research interests lie in neuro-symbolic AI, automated reasoning, constraint programming, and formal verification. He is particularly interested in integrating symbolic reasoning and machine learning to improve the scalability, reliability, and interpretability of AI systems.

His work has been published in leading venues in artificial intelligence and software engineering, such as AAAI, NeurIPS, FM, ASE, and ISSTA. His research has received distinctions including an ACM SIGSOFT Distinguished Paper Award at ISSTA and a Best Student Abstract Honorable Mention at AAAI. He serves regularly on the programme committees of major international conferences, including AAAI and ECAI, and as a reviewer for leading journals including TNNLS, TKDE, and Information Fusion.

Qualifications

  • PhD, University of Chinese Academy of Sciences, China, September 2017 - June 2023
  • BSc, Northeast Normal University, China, September 2013 - June 2017

Employment

  • Lecturer in Computer Science, City St George's, University of London, United Kingdom, July 2026 - present
  • Research Associate, University of Oxford, United Kingdom, October 2023 - June 2026
  • Research Assistant, Chinese Academy of Sciences, China, September 2017 - September 2023

Languages

Chinese (Mandarin) (can read, write, speak, understand spoken, peer review) and English (can read, write, speak, understand spoken, peer review)

Publications

Publications by category

Conference papers and proceedings (15)

  • Liu, M., Han, R., Jia, F., Huang, P., Ma, F., Zhang, H.... Zhang, J. Investigating the Existence of Holey Latin Squares via Satisfiability Testing. .doi:10.1007/978-981-99-7022-3_38
  • Jia, F., Han, R., Ma, X., Cui, B., Liu, M., Huang, P.... Zhang, J. (2023). PSMT: Satisfiability Modulo Theories Meets Probability Distribution. 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) 11-15 September.doi:10.1109/ase56229.2023.00183
  • Liu, M., Lv, K., Huang, P., Han, R., Jia, F., Zhang, Y.... Zhang, J. (2023). NRAgo: Solving SMT(NRA) Formulas with Gradient-Based Optimization. 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) 11-15 September.doi:10.1109/ase56229.2023.00013
  • Jia, F., Han, R., Huang, P., Liu, M., Ma, F. and Zhang, J. Improving Bit-Blasting for Nonlinear Integer Constraints. ISSTA '23: 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis.doi:10.1145/3597926.3598034
  • Jia, F., Dong, Y., Liu, M., Huang, P., Ma, F. and Zhang, J. Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning. .
  • Huang, P., Yang, Y., Liu, M., Jia, F., Ma, F. and Zhang, J. 𝜀 -weakened robustness of deep neural networks. ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis.doi:10.1145/3533767.3534373
  • Huang, P., Li, R., Liu, M., Ma, F. and Zhang, J. Efficient SAT-Based Minimal Model Generation Methods for Modal Logic S5. .doi:10.1007/978-3-030-80223-3_16
  • Liu, M., Zhang, F., Huang, P., Niu, S., Ma, F. and Zhang, J. Learning the Satisfiability of Pseudo-Boolean Problem with Graph Neural Networks. .doi:10.1007/978-3-030-58475-7_51
  • Huang, P., Liu, M., Wang, P., Zhang, W., Ma, F. and Zhang, J. (2019). Solving the Satisfiability Problem of Modal Logic S5 Guided by Graph Coloring. Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19} 10-16 August.doi:10.24963/ijcai.2019/153
  • Huang, P., Liu, M., Ge, C., Ma, F. and Zhang, J. Investigating the Existence of Orthogonal Golf Designs via Satisfiability Testing. ISSAC '19: International Symposium on Symbolic and Algebraic Computation.doi:10.1145/3326229.3326232
  • Liu, M., Ma, F. and Yan, J. A Community-Division Based Algorithm for Finding Relations Among Linear Constraints. .doi:10.1007/978-3-319-99247-1_2
  • Liu, M., Huang, P., Jia, F., Zhang, F., Sun, Y., Cai, S.... Zhang, J. Can Graph Neural Networks Learn to Solve the MaxSAT Problem? (Student Abstract). .doi:10.1609/aaai.v37i13.26992
  • Huang, P., Yang, Y., Jia, F., Liu, M., Ma, F. and Zhang, J. Word Level Robustness Enhancement: Fight Perturbation with Perturbation. .doi:10.1609/aaai.v36i10.21324
  • Jia, F., Dong, Y., Han, R., Huang, P., Liu, M., Ma, F.... Zhang, J. A Complete Algorithm for Optimization Modulo Nonlinear Real Arithmetic. .doi:10.1609/aaai.v39i11.33224
  • Liu, M., Cerna, D.M., Gouveia, F. and Cropper, A. Scalable Knowledge Refactoring Using Constrained Optimisation. .doi:10.1609/aaai.v39i14.33650

Journal articles (3)

  • Liu, F., Chen, J., Wang, Z., Xu, Z., Wang, G. and Liu, M. (2026). Nonideal Phase Error Compensation Based on Global-Aware 3-D U-Net for GNSS-InSAR. IEEE Transactions on Geoscience and Remote Sensing, 64, pp. 5801920-5801920. doi:10.1109/tgrs.2026.3702256

    [publisher’s website]

  • Zhou, Y., Liu, M., Ma, F., Luo, N. and Yin, M. (2022). Modelling and solving the supply marketing order allocation problem with time consistency and bundle discounts. Journal of the Operational Research Society, 73(8), pp. 1682-1691. doi:10.1080/01605682.2021.1932619

    [publisher’s website]

  • Gao, J., Lv, Y., Liu, M., Cai, S. and Ma, F. (2022). Improving Simulated Annealing for Clique Partitioning Problems. Journal of Artificial Intelligence Research, 74, pp. 1485-1513. doi:10.1613/jair.1.13382

    [publisher’s website]