Shurui Zhou

[pronunciation: Shoo-ray Joe]

周抒睿

Assistant Professor
University of Toronto
Department of Electrical & Computer Engineering
Department of Computer Science (Cross-Appointment)
shuruiz (at) ece.utoronto.ca
Office: PT484D
Phone: 416-978-4628

Bio W3Schools W3Schools

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.

Ph.D. Thesis

Title: Improving Collaboration Efficiency in Fork-based Development
Committee Members: Christian Kästner, James D. Herbsleb, Laura A. Dabbish, Andrzej Wąsowski.

...

News

  • Dec 2. 2021

    Our paper "Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process" has been accepted to ICSE 2022!

  • Oct 20. 2021

    [Invited Talk at Perceive'21] Improving Collaboration Efficiency for Building AI-based Software. [Recording]

  • Oct 8. 2021

    I received the IBM Canada Advanced Studies (CAS) Fellowship award, co-PI with Professor Kelly Lyons!

  • Oct 2. 2021

    Our paper "Interactive Patch Filtering as Debugging Aid" was awarded IEEE TCSE Distinguished Paper Award.!

  • Sep 27. 2021

    Our paper "Splitting, Renaming, Removing: A Study of Common Cleaning Activities in Jupyter Notebooks" has been accepted to the RAISE 2021!

  • Aug 11. 2021

    I received the John R. Evans Leaders Fund (JELF) – Funding for research infrastructure!

  • Aug 2. 2021

    Our paper "Subtle Bugs Everywhere: Generating Documentation for Data Wrangling Code" has been accepted to the ASE 2021!

  • ...


Talks&Media

Oct 20. 2021
Invited Talk at Perceive'21 -- Improving Collaboration Efficiency for Building AI-based Software. [Recording]

Nov 14. 2020
Invited Talk at CSER'20 -- Improving Collaboration Efficiency for Distributed and Interdisciplinary Software Teams.

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]

Research


My FORCOLAB students and I are working on 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.

Prospective Students

I am actively looking for talented, motivated colleagues at different levels. See here for detail.

Teaching

2021 Fall
ECE444 - Software Engineering
ECE345 - Algorithms and Data Structures
2021 Winter
ECE1724 - Empirical Software Engineering
2020 Fall
ECE444 - Software Engineering

Service

  • [Organizer] FOSD 2018 Meeting
  • [Co-Chair] CSER 2021 Fall Meeting
  • [PC] FSE2022, ICSE2022-Poster, ACMwomENcourage2021-Posters, FSE2021-Student Research Competition, VariVolution 2020 Workshop
  • [Reviewer] CSCW 2021, Journal of Systems & Software (2021), Journal of Software (2021), TOSEM (2021), IST 2020, TSE (2020, 2019)
  • [Sub-Reviewer] ASE 2020, ICSE 2020, FSE 2019, ASE 2019, ICSE 2018, ASE 2017, FSE 2017, SPLC 2017, ICSE 2017, VAMOS 2017, SPLC 2016, ASE 2015 and TSE 2015

    • Blog

  • Materials for academic job-hunting

    • Prior Projects

    Identifying Redundant PRs on GitHub

    We monitor the coming pull request of each GitHub project and detect potentially redundant pull request pairs to save maintainer and contributors' effort. This is intended to be a GitHub bot still under implementation for our [SANER'19] paper. [code]

    forks-insight.com

    This project is designed as a complementary of the GitHub network view. We sift all active forks and summarize changes with statistics and representative keywords. It is a light-weight programming language independent web service for our [ICSE'18] INFOX paper and the [ICSE'18 poster] paper.