MICHAEL FU 🏀

🧐Research Statement 👨‍🏫Teaching Statement 📝PhD Thesis
• 👋 Greetings!
• 🚀 Actively seeking one PhD student

PhD Opportunities in Software Security and Engineering
I am currently seeking a motivated PhD student to join my research group in exploring exciting new directions in software security and software engineering. My research focuses on proactive security solutions across the DevSecOps lifecycle, with an emphasis on using cutting-edge deep learning techniques, such as large language models (LLMs), to address key challenges in software and cloud security.
Academic Background and Research Focus
I am a Lecturer (Assistant Professor) in the Software Engineering group within the School of Computing and Information Systems at the University of Melbourne. I completed my PhD at Monash University in January 2025, under the supervision of Dr. Kla Tantithamthavorn and Dr. Trung Le. My research focuses on developing proactive security approaches for the DevSecOps lifecycle. My work has been published in top-tier Software Engineering conferences, including the International Conference on Software Engineering (ICSE), the International Conference on the Foundations of Software Engineering (FSE), the International Conference on Automated Software Engineering (ASE), and the Mining Software Repositories Conference (MSR). I have also published in high-impact journals such as IEEE Transactions on Software Engineering (TSE), ACM Transactions on Software Engineering and Methodology (TOSEM), and Empirical Software Engineering (EMSE). My broader research interests lie in software engineering, particularly in software security throughout the DevOps lifecycle (DevSecOps).

I am originally from Taipei, Taiwan. I currently live in Melbourne, Australia with my partner.

• 🔨 Build Neural Networks & Large Language Models

  • ► Transformers
  • ► Recurrent Neural Networks
  • ► Graph Neural Networks

• 🧠 Train for Cybersecurity Purposes

  • ► Detect Software Vulnerabilities in Large Code Base
  • ► Explain Vulnerability Types
  • ► Suggest Automated Vulnerability Repairs

• 🧰 Deploy as Practical Security Tools

  • ► A Deep Learning-based Real-Time Security Testing Tool in IDEs (Website | Free Download)

Selected Publication

• Citations: 1,198 | h-index: 9 | i-10 index: 9
• 📄 DeepVulMatch: Learning and Matching Latent Vulnerability Representations for Dual-Granularity Vulnerability Detection
  Accepted at IEEE TR'25
• 📄 DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
  Under Review at IEEE TR
• 📄 AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
  Accepted to ASE 2025, Industry Showcase Track
• 📄 RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice
  Accepted at JSS'25 | Impact Factor: 3.7
• 📄 AI for DevSecOps: A Landscape and Future Opportunities
  Accepted at TOSEM'25 | Impact Factor: 6.6
• 📄 Vision Transformer-Inspired Automated Vulnerability Repair
  Accepted at TOSEM'23, ICSE'25 (Journal-First) | Impact Factor: 6.6
• 📄 Chatgpt for vulnerability detection, classification, and repair: How far are we?
  Accepted at APSEC'23
• 📄 VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types
  Accepted at TSE'23, ICSE'24 (Journal-First) | Impact Factor: 6.5
• 📄 AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities
  Accepted at EMSE'24, ICSE'24 (Journal-First) | Impact Factor: 4.8
• 📄 Toward More Effective Deep Learning-based Automated Software Vulnerability Prediction, Classification, and Repair
  Accepted at ICSE'23 | Conference Rank: CORE A*
• 📄 VulRepair: a T5-based automated software vulnerability repair
  Accepted at FSE'22 | Conference Rank: CORE A*
• 📄 LineVul: A Transformer-based Line-Level Vulnerability Prediction
  Accepted at MSR'22 | Conference Rank: CORE A
• 📄 GPT2SP: A transformer-based agile story point estimation approach
  Accepted at TSE'22 | Impact Factor: 6.5
• 📄 Pyexplainer: Explaining the predictions of just-in-time defect models
  Accepted at ASE'21 | Conference Rank: CORE A*

🏆Award🏆

• Distinguished Reviewer Award in MSR 2024 | Conference Rank: CORE A
• ACM SIGSOFT Distinguished Paper Award in ASE 2021 | Conference Rank: CORE A*
• High Distinction Graduate Award for my master degree at Monash University
• Special Invitees in MSR 2022 to submit an extended paper to the EMSE journal

Teaching

• 👨‍💻 Lecturer in a graduate research training program, IT Quantitative Research Methods in 2024 at Monash University
• 👨‍💻 Guest Lecturer in a postgraduate unit, FIT5136 - Software Engineering, where I taught topics related to software engineering, cybersecurity, and AI in S1 2024 at Monash University
• 👨‍🏫 Teaching Associate in a postgraduate unit, FIT5215 - Deep Learning, where I taught technical contents such as mathematical fundamentals for deep learning, basic deep learning architectures like RNNs and CNNs, and advanced topics including Transformers, diffusion models, and adversarial learning in S2 2024 at Monash University
  📝 Student Evaluation: 92/100 (Very High, Exceeding expectations). 🔍 View report.
• 👨‍🏫 Teaching Associate in a postgraduate unit, FIT5136 - Software Engineering, where I taught software engineering concepts from agile planning to object-oriented software implementation in S2 2024 at Monash University
  📝 Student Evaluation: 98/100 (Very High, Exceeding expectations). 🔍 View report.

Student Testimonials

• ✅ I enjoyed the perspective and the anecdotes given by a person actively contributing to the software engineering landscape. I would like to hear more!
• ✅ Good insights about AI in Software engineering. Good engagement with audience.
• ✅ Michael is very passionate about teaching AI for SE. His teaching is very enthusiastic, and is at a good pace, clear, well-organised, and get to the point. The lecture is very informative, and useful to the students. Keep up the great work. He can add interactive components (e.g., Flux, PollEV), or peer discussion activities during the lecture (e.g., breakout rooms) in the future.
• ✅ Michael’s class was fabulous! It would be highly appreciating if we can have more classes from Michael.
• ✅ The lecture was clearly laid out, very comprehensive and very easy to understand. Thank you for today's lecture!

Community Service

• My peer review experience for journals
• 🔎 A PC in the Research Track in ICSE 2026 | Conference Rank: CORE A*
• 🔎 A Regular Reviewer for IEEE TSE and ACM TOSEM
• 🔎 A PC in the AI Models / Data Track in EASE 2025 | Conference Rank: CORE A
• 🔎 A PC in AI-IDE@FSE (Integrated Development Environment) 2025
• 🔎 A PC in APR@ICSE (Automated Program Repair) 2025
• 🔎 A PC in SVM@ICSE (Software Vulnerability Management) 2025
• 🔎 A Shadow PC in the Research Track in ICSE 2025 | Conference Rank: CORE A*
• 🔎 A PC in DSN 2024 Doctoral Forum | Conference Rank: CORE A
• 😼 A Student Volunteer in ICSE 2024 | Conference Rank: CORE A*
• 🔎 A PC in the Artifact Evaluation Track in ICSE 2024 | Conference Rank: CORE A*
• 🔎 A PC in the Technical Paper Track in MSR 2024 | Conference Rank: CORE A
• 👨‍🏫 Presenter at Monash University Open Day Event 2022, 2023, 2024
• 😼 A Student Volunteer in ICSE 2023 | Conference Rank: CORE A*
• 🔎 A Junior PC in the technical paper track in MSR 2023 | Conference Rank: CORE A
• 🔎 A Web Co-Chair as part of the organization committees in MSR 2023 | Conference Rank: CORE A
• 👨‍🏫 Presenter at Monash University TechFutureFest 2022
• 🔎 A PC in the Artifact Evaluation Track in ASE 2022 | Conference Rank: CORE A*

Fundings

• 💰 SIGSOFT CAPS Travel Support Funding for ICSE 2024
• 💰 Google Cloud Research Credits 2023
• 💰 Full Monash University Graduate Scholarship for my PhD study

Key Projects

• 👨‍💻 AIBugHunter Project: A Practical Open-Source Tool available in VSCode for Predicting, Classifying, and Repairing Software Vulnerabilities (Google Cloud Research Credits provide funding for the computing service of the resulting tool)
• 👨‍💻 Explainable AI Tools for Explaining Software Defect Predictions of Machine Learning Models
• 👨‍💻 PhD Thesis: Toward More Effective Deep Learning-based Automated Software Vulnerability Prediction, Classification, and Repair
Job Talk by Michael Fu

Conference Presentation

• 👨‍🏫 Presenter at the ICSE 2024 For 2 Papers in the Journal-First Track | Conference Rank: CORE A*
• 👨‍🏫 Presenter at APSEC 2023 For 1 Technical Paper
• 👨‍🏫 Presenter at the ICSE 2023 Doctoral Symposium | Conference Rank: CORE A*
• 👨‍🏫 Presenter at the MSR 2022 For 1 Technical Paper | Conference Rank: CORE A

Education

• Doctor of Philosophy (Department of Software Systems and Cybersecurity) at Monash University, Australia (Feb 2022 -)
• Master of Information Technology at Monash University, Australia (March 2020 - Dec 2021)

🦉 Book List

• 📖 Poor Charlie’s Almanack: The Essential Wit and Wisdom of Charles T. Munger
  by Charles T. Munger
• 📖 Antifragile: Things That Gain from Disorder
  by Nassim Nicholas Taleb
• 📖 The Black Swan: The Impact of the Highly Improbable
  by Nassim Nicholas Taleb
• 📖 Fooled By Randomness: The Hidden Role of Chance in Life and in the Markets
  by Nassim Nicholas Taleb
• 📖 A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market
  by Edward O. Thorp
• 📖 Thinking, Fast and Slow
  by Daniel Kahneman