Professional Education

  • Doctor of Philosophy, University of Oxford, Computer Science (2020)

Stanford Advisors

Current Research and Scholarly Interests

Safe and trustworthy AI: robustness, explainability, and fairness

All Publications

  • A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability? COMPUTER SCIENCE REVIEW Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., Wu, M., Yi, X. 2020; 37
  • A game-based approximate verification of deep neural networks with provable guarantees THEORETICAL COMPUTER SCIENCE Wu, M., Wicker, M., Ruan, W., Huang, X., Kwiatkowska, M. 2020; 807: 298-329
  • Robustness Guarantees for Deep Neural Networks on Videos Wu, M., Kwiatkowska, M., IEEE IEEE. 2020: 308-317
  • Assessing Robustness of Text Classification through Maximal Safe Radius Computation Findings of the Association for Computational Linguistics: EMNLP 2020 La Malfa, E., Wu, M., Laurenti, L., Wang, B., Hartshorn, A., Kwiatkowska, M. 2020: 2949-2968
  • Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles Wu, M., Louw, T., Lahijanian, M., Ruan, W., Huang, X., Merat, N., Kwiatkowska, M., IEEE IEEE. 2019: 6210-6216
  • Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D., Kwiatkowska, M. 2019: 5944-5952

    View details for DOI 10.24963/ijcai.2019/824

  • Concolic Testing for Deep Neural Networks Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M., Kroening, D., Huchard, M., Kastner, C., Fraser, G. IEEE. 2018: 109-119
  • Safety Verification of Deep Neural Networks Huang, X., Kwiatkowska, M., Wang, S., Wu, M., Majumdar, R., Kuncak SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 3-29