Assistant Professor
University of Toronto
Department of Electrical & Computer Engineering
Department of Computer Science (Cross-Appointment)
shuruiz (at) ece.utoronto.ca
I am interested in helping distributed and interdisciplinary software teams to collaborate more efficiently, especially in the context of modern open-source collaboration forms, fork-based development, and interdisciplinary teams when building AI-enabled systems or scientific software. To achieve my goals, I combine advances in tooling and software engineering principles with insights from other disciplines that study human collaboration, for which I combine and mix a wide range of research methods. I discover and evaluate existing interventions and develop new ones that steer collaborative development toward better practices.
Interdisciplinary Research, Software Engineering (SE), Empirical SE, SE for AI, Collaborative Software Development, Fork/Branch/Pull-based Development, GitHub, Open Source
I am actively looking for talented, motivated colleagues at different levels.
I received my Ph.D.'s degree in May. 2020 from the Institute for Software Research, School of Computer Science at Carnegie Mellon University. I am very fortunate to work with my advisor Professor Christian Kästner, and my ‘informal’ advisor and collaborator Professor Bogdan Vasilescu. I received my Master's degree in Software Enigneering from Peking University in 2014, and my Bachelor's degree in Software Engineering from Xi'an Jiaotong University in 2011.
Got my first ever grant CARET Seed Award from Faculty of Applied Science & Engineering at UofT jointed with Professor Alison Olechowski!
[Invited Talk at CSER'20] Improving Collaboration Efficiency for Distributed and Interdisciplinary Software Teams.
My Ph.D. work on Forking was featured on the Sustain Open Source Podcast.
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Sep. 11 2020
Talked about Forking in Open Source at Sustain Open Source Podcast
May. 5 2019
Dagstuhl Seminar 19191
-- Software Evolution in Time and Space: Unifying Version and Variability Management. [Seminar abstract] [lighting talk - Version Control For AI]