About Me

Dr. Armstrong is a researcher at the intersection of software ecosystem sustainability, affective computing, the trustworthiness of safety-critical systems, and software engineering for machine learning applications, including foundational models, AIWare, and Agentware. He mines massive datasets, including software repositories, and applies socio-technical data science techniques to uncover patterns and empirically make informed decisions.

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Interests
  • Affective Computing
  • Artificial Intelligence
  • Data Science
  • AI Trusthworhiness AI
  • Safety-critical systems
  • SECO Sustaianbility
  • Carbon foorprint/ Climate change
Education
  • PhD SECO Sustaianbility

    Queens University

  • M.A.Sc Data Science

    Polytechnique Montreal

📚 My Research

My work bridges theory and practice, addressing fundamental questions on the WHY, HOW, and WHAT of sustainable and trustworthy AI in complex socio-technical ecosystems.

In parallel, I actively contribute to the scientific community as a core reviewer for leading conferences and journals, including ICSE, EMSE, TOSEM, NeurIPS, AAAI, SANER, and MSR. Through these roles, I help uphold scholarly standards and foster innovation in software engineering and artificial intelligence. Additionally, as an international judge for science and engineering competitions, I mentor and support emerging scientists, advancing the next generation of scientific discovery and leadership.

— Please reach out to collaborate 😃

Featured Publications
Recent Publications
(2024). An empirical study of testing machine learning in the wild. ACM Transactions on Software Engineering and Methodology.
(2024). Deep learning model reuse in the huggingface community: Challenges, benefit and trends. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
(2023). A Grounded Theory of Cross-community SECOs: Feedback Diversity vs. Synchronization. IEEE Transactions on Software Engineering.
(2023). Studying the Practices of Testing Machine Learning Software in the Wild. arXiv preprint arXiv:2312.12604.
(2022). A mixed-methods analysis of micro-collaborative coding practices in OpenStack. Empirical Software Engineering.
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