My PhD dissertation topic is ππ notebook programming ππ (e.g. Jupyter Notebooks) and how ML/data practitioners use notebooks to experiment. I am using interview, survey, and contextual inquiry studies to understand how data scientists code, then designing and prototyping new forms of editor and version control support.
Kery, M. B., Horvath, A., & Myers, B. A. (2017). Variolite: Supporting Exploratory Programming by Data Scientists. In CHIβ17 Preprint. Retrieved from variolite-supporting-exploratory-programming.pdf
Kery, M. B., & Myers, B. A. (2017). Exploring exploratory programming. In Visual Languages and Human-Centric Computing (VL/HCC), 2017 IEEE Symposium on (pp. 25β29). Retrieved from ExploringExploratoryProgramming.pdf
Rojas, J. A. R., Kery, M. B., Rosenthal, S., & Dey, A. (2017). Sampling techniques to improve big data exploration. In Large Data Analysis and Visualization (LDAV), 2017 IEEE 7th Symposium on (pp. 26β35). Retrieved from RosenthalRojas_LDAV17.pdf
Kery, M. B., Radensky, M., Arya, M., John, B. E., & Myers, B. A. (2018). The Story in the Notebook: Exploratory Data Science using a Literate Programming Tool. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 174). Retrieved from Kery-The-Story-in-the-Notebook-Exploratory-Data-Science-using-a-Literate-Programming-Tool.pdf
Kery, M. B., & Myers, B. A. (2018). Interactions for Untangling Messy History in a Computational Notebook. In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). Retrieved from Kery-InteractionsForMessyHistory.pdf
Kery, M. B., Ren, D., Hohman, F., Moritz, D., Wongsuphasawat, K., & Patel, K. (2020). mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. In Proceedings of the 2020 UIST 33rd ACM User Interface Software and Technology Symposium. Retrieved from mage.pdf
Kery, M. B., Ren, D., Wongsuphasawat, K., Hohman, F., & Patel, K. (2020). The Future of Notebook Programming Is Fluid. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1β8). Retrieved from lbw.pdf
Kery, M. B., John, B. E., OβFlaherty, P., Horvath, A., & Myers, B. A. (2019). Towards effective foraging by data scientists to find past analysis choices. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1β13). Retrieved from paper092-Kery-CHI2019.pdf