I am Triet Huynh Minh Le (Lê Huỳnh Minh Triết in Vietnamese). I am a Continuing Lecturer, a.k.a. Assistant Professor, in the School of Computer and Mathematical Sciences at The University of Adelaide (UofA). I am also the lead of the Software Security Intelligence research at the Centre for Research on Engineering Software Technologies (CREST). I obtained my Ph.D. from CREST (UofA) under the supervision of Professor M. Ali Babar.
My research interests are Mining Software Repositories and Software Security Intelligence. My research aims at developing high-performing and robust Machine Learning and Deep Learning, and recently Large Language Model based techniques to perform large-scale analysis and assessment of various types of security vulnerabilities collected from multiple security sources (e.g., CVE/NVD), developer's forums (e.g., Stack Overflow) and software repositories (e.g., GitHub). My proposed vulnerability analytics and assessment models help to provide detailed and timely information for security and software practitioners to effectively and efficiently plan and prioritize vulnerability remediation and prevent cybersecurity attacks.
I am recruiting full-time PhD students to work with me in the above-mentioned areas. If you are interested, please contact me via triet.h.le@adelaide.edu.au, Twitter, LinkedIn.
8/2025: Invited to be an Artefacts/Models (AI models and/or Data) track (co-)Chair with Zhou Yang at EASE 2026. Stay tuned for more updates!
6/2025: Received an Early Career Seed Research Grant 2025 as a Chief Investigator from the School of CMS, The University of Adelaide.
5/2025: Our paper "Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction" in collaboration with Hanoi University of Science and Technology led by Prof. Thang Huynh-Quyet was accepted at the research track of ICSME 2025. Early acceptance rate: 6.2% (9/146). Preprint coming soon.
2/2025: Invited to be a PC member of the research track of EASE 2025. Please consider submitting your work.
1/2025: Our paper "LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations" led by our PhD student Ziyang Ye was accepted at the technical track of MSR 2024. Acceptance rate: 27.3%.
12/2024: Invited to be a PC member of the research track of ICSE 2026. Please consider submitting your work.