Student-Led Reproducibility Challenge

Reproducibility is a key Thematic Program for the Data Sciences Institute. The goal of this challenge is to encourage and raise awareness of reproducibility amongst students. The process of reproducing published analytic research is often difficult as the original process for conducting that work may not be explicit. The event brings together students from across four courses, across different fields, to investigate the reproducibility of published papers and verify their empirical results and claims by reproducing the computational methods.  Students present how they approached reproducing a paper in their teams and share learnings and challenges.

The 2024 Challenge program is in development. Hold the date – May 2, 2024

Below is the 2022 schedule:

May 2, 2024 
10:30 AM – 1:30 PM 
Data Sciences Inst 
10th floor Seminar room 
700 University Ave 

Registration will be open soon!

May 10, 2022
10:35-10:40 am
Welcome and Land Acknowledgement
Timothy Chan, Associate Director, Research & Thematic Programming, Data Sciences Institute
10:40-10:45 am
About the Reproducibility Challenge
Jason Hattrick-Simpers, Reproducibility Co-lead and Professor, Materials Science & Engineering, Faculty of Applied Science & Engineering
10:45-11:30 am
Reproducibility Challenge Presentations
Alyssa Schleifer and Hudson Yuen
Challenge: Western, B. (2021). Inside the Box: Safety, Health, and Isolation in Prison. Journal of Economic Perspectives, 35(4), 97-122. Team 1 Rohan Alexander, Faculty of Information

Kimlin Chin
Challenge: Kearney, M. S., Levine, P. B., & Pardue, L. (2022). The Puzzle of Falling US Birth Rates since the Great Recession. Journal of Economic Perspectives, 36(1), 151-76. Team 2 Rohan Alexander, Faculty of Information

Swarnadeep Chattopadhyay, Arsh Lakhanpal, and Olaedo Okpareke
Challenge: Kearney, M. S., Levine, P. B., & Pardue, L. (2022). The Puzzle of Falling US Birth Rates since the Great Recession. Journal of Economic Perspectives, 36(1), 151-76.
Team 3 Rohan Alexander, Faculty of Information

Christie Lau, Laurie Lu, Ruiyan Ni and Emily So
Challenge: Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S.,. & Ideker, T. (2021). Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nature Cancer, 2(2), 233-244.
Team Benjamin Haibe-Kains, University Health Network

Sanjot Grewal, Steve Jeoung, Walid Maraqa, and Mu Yang
Challenge: Siedner, M. J., Harling, G., Reynolds, Z., Gilbert, R. F., Haneuse, S., Venkataramani, A. S., & Tsai, A. C. (2020). Social distancing to slow the US COVID-19 epidemic: Longitudinal pretest–posttest comparison group study. PLoS medicine, 17(8), e1003244.
Team Aya Mitani, Dalla Lana School of Public Health

Daniel Persaud
Challenge: Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016). A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2(1), 1-7.
Team Jae Hattrick-Simpers, Dept of Materials Science & Engineering
11:30-11:55 am
Lightning Talk – Reproducibility in Machine Learning Systems for Web-Scale Software
Holly Xie, Senior Applied Scientist - Machine Learning Products, Xero Accounting
11:55-12:20 pm
Lightning Talk – Reproducible Survey Research at the Bank of Canada
Chris Henry, Senior Economist, Bank of Canada
12:20-12:55 pm
Networking Lunch
12:55-1:25 pm
Panel with students
Moderator: Benjamin Haibe-Kains
1:25-1:30 pm
Closing
Benjamin Haibe-Kains