Pooya Rostami Mazrae

I am a software engineering researcher specializing in the intersection of machine learning, automation, and large-scale software ecosystems. I am completing my PhD in Software Engineering at the University of Mons (UMONS), Belgium, where I investigate the evolution, quality, and reliability of GitHub Actions automation workflows. During my doctoral research, I have collaborated with Radboud University, and other research groups, producing peer-reviewed publications, datasets, and open-source tools.

Research: My work focuses on analyzing and improving software development processes through data-driven methods. I study the evolution of automated DevOps pipelines, evaluate CI/CD ecosystems, and develop techniques for understanding and supporting large-scale workflow automation. I combine quantitative empirical analyses (mining GitHub data) with qualitative research (surveys, interviews, coding frameworks) to generate actionable insights for both researchers and practitioners. My works and collaborations has been published in leading venues including ICSME, MSR, EMSE, and Springer Nature, and has received distinctions such as the ICSME 2022 Distinguished Paper Award and the MSR 2022 Best Hackathon Paper Award.

Education: I hold an MSc in Software Engineering from Sharif University of Technology, where I developed machine-learning-based approaches for automating issue–commit link recovery. I earned my BSc in Computer Engineering from K. N. Toosi University of Technology, with a thesis on applying AI techniques to disease-gene detection. My academic trajectory integrates software analytics, machine learning, and empirical research methods.