Triet Le

Triet Le

Higher Degree by Research Candidate

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

I am currently a PhD student in Computer Science at The University of Adelaide, Australia. I obtained my first-class honours bachelor degree in Computer Science with a GPA of 95.6/100 and graduated as a valedictorian from the International University – Vietnam National University, Ho Chi Minh City. My research interests include but are not limited to data mining and machine learning as well as their interdisciplinary applications. In my undergraduate study, I utilized machine learning and data mining techniques to predict some challenging chemical properties. I published 10 papers including 2 SCI-indexed journal articles and won 4-time best conference paper awards. My PhD will expand my research works to investigate how machine learning can perform predictive analytics in various domains of software engineering and cybersecurity.

I am investigating the applications of machine learning and deep learning methods for the prediction of software vulnerabilities in public databases such as Common Vulnerabilities and Exposures as well as National Vulnerability Database. I plan to identify the essential features that can effectively represent the software vulnerabilities. Moreover, I want to build a robust predictive analytics framework to support the security experts to resolve the vulnerabilities with priority.

  • Journals

    Year Citation
    2020 Le, T., Chen, H., & Babar, M. (2020). Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges. ACM Computing Surveys, 53(3), 38 pages.
    2018 Le, T., Tran, T., & Huynh, L. (2018). Identification of hindered internal rotational mode for complex chemical species: A data mining approach with multivariate logistic regression model. Chemometrics and Intelligent Laboratory Systems, 172, 10-16.
    DOI Scopus28 WoS24
    2017 Le, T., Do, S., & Huynh, L. (2017). Algorithm for auto-generation of hindered internal rotation parameters for complex chemical systems. Computational and Theoretical Chemistry, 1100, 61-69.
    DOI Scopus25 WoS22
    2017 Minh Le, T., & Duong, T. (2017). Online collaborative video annotation framework using Good Relations ontology for E-commerce. International Journal of Advanced Computer Research, 7(31), 121-135.
    Le, T. H. M., Croft, R., Hin, D., & Babar, M. A. (n.d.). Demystifying the Mysteries of Security Vulnerability Discussions on
    Developer Q&A Sites.
  • Conference Papers

    Year Citation
    2019 Le, T., Sabir, B., & Babar, M. (2019). Automated software vulnerability assessment with concept drift. In IEEE International Working Conference on Mining Software Repositories Vol. 2019-May (pp. 371-382). online: IEEE.
    DOI Scopus2
    2018 Le, T. H. M., Tran, T. T., & Huynh, L. K. (2018). Linear support vector machine to classify the vibrational modes for complex chemical systems. In Proceedings of the 2nd International Conference on Machine Learning and Soft Computing (pp. 10). Phu Quoc Island, Viet Nam: Association for Computing Machinery.
    2016 Nguyen, H., Pham, H., Duong, T., Nguyen, T., & Le, H. (2016). Personalized facets for faceted search using wikipedia disambiguation and social network. In T. Nguyen, T. VanDo, H. LeThi, & N. Nguyen (Eds.), Advances in Intelligent Systems and Computing Vol. 453 (pp. 229-241). Vienna, AUSTRIA: SPRINGER-VERLAG BERLIN.
    DOI Scopus1
    2015 Triet, H. M. L., Tung, T. T., & Lam, K. H. (2015). Linear support vector machine to classify the vibrational modes for complex chemical systems. In 2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018) (pp. 10-14). VIETNAM: ASSOC COMPUTING MACHINERY.
    DOI WoS1
    Le, T. H. M., Hin, D., Croft, R., & Babar, M. A. (n.d.). PUMiner: Mining Security Posts from Developer Question and Answer
    Websites with PU Learning.

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