Gia Bao Doan

Gia Bao Doan

School of Computer Science

Faculty of Sciences, Engineering and Technology

My research is about Trustworthy Machine Learning System, security aspect of Machine Learning and Deep Learning.

My research is about trustworthy and security of Machine Learning and Deep Learning systems.

  • Appointments

    Date Position Institution name
    2022 - ongoing Postdoctoral Research Associate The University of Adelaide
    2014 - 2018 Process and Equipment Engineer Intel Products Vietnam
  • Language Competencies

    Language Competency
    English Can read, write, speak, understand spoken and peer review
    Vietnamese Can read, write, speak, understand spoken and peer review
  • Education

    Date Institution name Country Title
    2018 - 2022 University of Adelaide Australia Doctor of Philosophy
    2012 - 2014 RMIT University Vietnam Vietnam Master of Engineerings
    2007 - 2012 Danang University of Technology Vietnam Bachelor of Electronics and Communication Engineering
  • Research Interests

  • Journals

    Year Citation
    2022 Doan, B. G., Xue, M., Ma, S., Abbasnejad, E., & Ranasinghe, D. C. (2022). TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems. IEEE Transactions on Information Forensics and Security, 17, 3816-3830.
    2021 Gao, Y., Kim, Y., Doan, B. G., Zhang, Z., Zhang, G., Nepal, S., . . . Kim, H. (2021). Design and Evaluation of a Multi-Domain TrojanDetection Method on Deep Neural Networks. IEEE Transactions on Dependable and Secure Computing, 19(4), 1-14.
    DOI WoS1
  • Conference Papers

    Year Citation
    2022 Yang, S., Doan, B. G., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Kanhere, S. S. (2022). Transferable Graph Backdoor Attack. In ACM International Conference Proceeding Series (pp. 321-332). ACM.
    2020 Doan, B. G., Abbasnejad, M., & Ranasinghe, D. C. (2020). Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems. In Annual Computer Security Applications Conference, ACSAC 2020 (pp. 897-912). online: ACM.
    DOI Scopus28 WoS17

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