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.
May 2, 2024
10:30 AM – 1:30 PM
Data Sciences Inst
10th floor Seminar room
700 University Ave
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11:00-11:05 am |
Welcome and Land Acknowledgement |
Benjamin Haibe-Kains |
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11:00-11:05 am |
About the Reproducibility Challenge |
Jason Hattrick-Simpers, Reproducibility Co-lead and Professor, Materials Science & Engineering, Faculty of Applied Science & Engineering |
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11:10-Noon |
Reproducibility Challenge Presentations |
Anastasia Teterina Vandeputte, D., De Commer, L., Tito, R. Y., Kathagen, G., Sabino, J., Vermeire, S., Faust, K., & Raes, J. (2021). Temporal variability in quantitative human gut microbiome profiles and implications for clinical research. Nature Communications, 12(1), Article 1. Team 1 Aya Mitani, Dalla Lana School of Public Health Ente Kang On the challenge of reproducing a global health Team 2 Aya Mitani, Dalla Lana School of Public Health Julia Nguyen, Lucas Penny, Aleem Aamir A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors Team 1 Benjamin Haibe-Kains, Department of Medical Biophysics Deisha Paliwal, Andres Felipe Melani De La Hoz, Almas Khan Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients Team 2 Benjamin Haibe-Kains, Department of Medical Biophysics Julia Kim, Abbass Sleiman Significant declines in standardised test scores due to COVID-19 school closures disproportionately affect vulnerable students: A replication analysis using data from the United States and a case study on the Netherlands Team 1 Rohan Alexander, Faculty of Information & Statistical Sciences, Faculty of Arts & Science Thomas Fox Leaks, Attribution, and Academic Research: A Case for Professional Reflexivity Team 2 Rohan Alexander, Faculty of Information & Statistical Sciences, Faculty of Arts & Science Emma Teng Echoes of Economic Downturn: Investigating the Persistent Impact of the Great Recession on Birth Rates Among Young Americans Team 3 Rohan Alexander, Faculty of Information & Statistical Sciences, Faculty of Arts & Science David Sanchez Patino Exploiting redundancy in large materials datasets for efficient machine learning with less data Team 1 Jason Hattrick-Simpers, Department of Materials Science and Engineering |
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Noon-12:45pm |
Networking Lunch |
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12:45-1:10 pm |
Lightning Talk – Making transparent, reproducible and reusable in a lab |
Sisira Kadambat Nair Research Associate & Lab Coordinator, Benjamin Haibe-Kains Lab, Princess Margaret Cancer Centre, UHN |
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1:10-1:30 pm |
Panel with Students |
Moderator: Benjamin Haibe-Kains |
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1:30 pm |
Closing |
Benjamin Haibe-Kains |
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10:35-10:40 am |
Welcome and Land Acknowledgement |
Timothy Chan, Associate Director, Research & Thematic Programming, Data Sciences Institute |
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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 |
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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 |
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11:30-11:55 am |
Lightning Talk – Reproducibility in Machine Learning Systems for Web-Scale Software
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Holly Xie, Senior Applied Scientist - Machine Learning Products, Xero Accounting |
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11:55-12:20 pm |
Lightning Talk – Reproducible Survey Research at the Bank of Canada |
Chris Henry, Senior Economist, Bank of Canada |
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12:20-12:55 pm |
Networking Lunch |
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12:55-1:25 pm |
Panel with students |
Moderator: Benjamin Haibe-Kains |
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1:25-1:30 pm |
Closing |
Benjamin Haibe-Kains |
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