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Special Seminar: April Wang, "Designing Future Computational Notebooks for Collaboration and Learning"

Event Type
Seminar/Symposium
Sponsor
Illinois Computer Science
Virtual
wifi event
Date
Feb 1, 2023   10:00 am  
Views
66
Originating Calendar
Computer Science Special Seminar Series

Zoom: https://illinois.zoom.us/j/89524929939?pwd=THRFejd6aG0xSlNrbFlocnR2c0VRQT09 

Abstract:
Computing technologies allow people to work, learn, and socialize remotely but seamlessly together, particularly over complex computational tasks like programming and data science. Yet, collaboration in data science is often hard. Since data science is highly exploratory, the artifact and analysis often iterate fast. It is difficult to maintain a shared understanding across various collaborators. On the other hand, tools like computational notebooks provide a convenient approach for data scientists to run, document, and share analysis in a storytelling way. However, there are still many open-ended questions about how to improve the collaboration experience by designing better collaborative data science tools. For example, data scientists often neglect to keep updated documentation during rapid exploration, which results in computational notebooks that are messy and difficult to read; without strategic planning, working together in a shared notebook may block each other's work. My research draws upon human-centered design techniques to identify barriers in real-world data science programming practices, and explore the design space of collaborative data science environments through tool-building.

Bio:
Computing technologies allow people to work, learn, and socialize remotely but seamlessly together, particularly over complex computational tasks like programming and data science. Yet, collaboration in data science is often hard. Since data science is highly exploratory, the artifact and analysis often iterate fast. It is difficult to maintain a shared understanding across various collaborators. On the other hand, tools like computational notebooks provide a convenient approach for data scientists to run, document, and share analysis in a storytelling way. However, there are still many open-ended questions about how to improve the collaboration experience by designing better collaborative data science tools. For example, data scientists often neglect to keep updated documentation during rapid exploration, which results in computational notebooks that are messy and difficult to read; without strategic planning, working together in a shared notebook may block each other's work. My research draws upon human-centered design techniques to identify barriers in real-world data science programming practices, and explore the design space of collaborative data science environments through tool-building.

Faculty Host: Karrie Karahalios

Meeting ID: 895 2492 9939; Password: csillinois

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