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

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

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

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

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.
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 Scopus7 WoS3
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. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 162, 15 pages.
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 Scopus8 WoS6 Europe PMC1

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). Online: ACM. DOI Scopus5
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 Scopus81 WoS48