Gia Bao Doan

Dr Gia Bao Doan

Postdoctoral Researcher

School of Computer and Mathematical Sciences

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
    Artificial Intelligence Artificial Intelligence & Image Processing Computer System Security

  • Journals
    Year Citation
    2023 Doan, B. G., Yang, S., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Ranasinghe, D. C. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37, 14783-14791.
    Scopus2
    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.
    DOI Scopus20 WoS9
    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 Scopus33 WoS6 Europe PMC2

  • Conference Papers
    Year Citation
    2024 Doan, B. G., Nguyen, D. Q., Montague, P., Abraham, T., De Vel, O., Camtepe, S., . . . Ranasinghe, D. C. (2024). Bayesian Learned Models Can Detect Adversarial Malware for Free. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14982 LNCS (pp. 45-65). Springer Nature Switzerland.
    DOI
    2023 Doan, B. G., Yang, S., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Ranasinghe, D. C. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. In B. Williams, Y. Chen, & J. Neville (Eds.), THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 12 (pp. 14783-14791). Online: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
    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). Online: ACM.
    DOI Scopus13 WoS4
    2022 Doan, B. G., Abbasnejad, E., Shi, J. Q., & Ranashinghe, D. C. (2022). Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. In INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162 Vol. 162 (pp. 15 pages). Baltimore, MD: JMLR-JOURNAL MACHINE LEARNING RESEARCH.
    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 Scopus154 WoS58

  • Preprint
    Year Citation
    2024 Doan, B. G., Shamsi, A., Guo, X. -Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., . . . Abbasnejad, E. (2024). Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian
    Neural Networks.
    2024 Doan, B. G., Nguyen, D. Q., Lindquist, C., Montague, P., Abraham, T., Vel, O. D., . . . Ranasinghe, D. C. (2024). On the Credibility of Backdoor Attacks Against Object Detectors in the
    Physical World.
  • Position: Postdoctoral Researcher
  • Email: giabao.doan@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor Level Four
  • Room: 4.23
  • Org Unit: School of Computer and Mathematical Sciences
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