Photo of Souti Chattopadhyay
Souti Chattopadhyay, graduate student of computer science.

Souti Chattopadhyay, graduate student of computer science in the College of Engineering at Oregon State University, was first author on a paper that won the Honorable Mention Award at the 2020 ACM CHI Conference on Human Factors in Computing Systems. The distinction is given to the top 10% of the papers presented.

Other authors include her advisor, Anita Sarma, associate professor of computer science, and colleagues at Microsoft and University of Tennessee-Knoxville.

“This award means that our research matters and provides deeper insight into what the future can hold in terms of accessible and inclusive computing,” Chattopadhyay said.

Chattopadhyay’s research examines how data scientists make decisions when interacting with programming interfaces. The goal is to make programming tools contextually assistive with freedom to delay and review the outcomes of decisions along the path.

What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities

Souti Chattopadhyay1, Ishita Prasad2, Austin Z. Henley3, Anita Sarma1, Titus Barik2

Oregon State University1, Microsoft2, University of Tennessee-Knoxville3


Computational notebooks—such as Azure, Databricks, and Jupyter—are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all within a single document. Nevertheless, as data scientists incorporate more of their activities into notebooks, they encounter unexpected difficulties, or pain points, that impact their productivity and disrupt their workflow. Through a systematic, mixed-methods study using semi-structured interviews (n = 20) and survey (n = 156) with data scientists, we catalog nine pain points when working with notebooks. Our findings suggest that data scientists face numerous pain points throughout the entire workflow—from setting up notebooks to deploying to production—across many notebook environments. Our data scientists report essential notebook requirements, such as supporting data exploration and visualization. The results of our study inform and inspire the design of computational notebooks.

Anita Sarma
Anita Sarma, associate professor

“Open source software is changing the technology and workforce landscape. Our work will help open source software tools and technology support diverse cognitive styles that will help bring diversity in thought by enabling diversity in open source contributors.”

 – Anita Sarma, associate professor of computer science in the College of Engineering at Oregon State.

Principal investigators:

  • Lead PI: Anita Sarma, associate professor of computer science, Oregon State University
  • Co-PI: Margaret Burnett, Distinguished Professor of computer science, Oregon State University

In collaboration with:

  • PI: Igor Steinmacher, assistant professor, Northern Arizona University
  • Co-PI: Marco Gerosa, associate professor, Northern Arizona University


National Science Foundation

Award amount:

$1.4 million between the two universities, $870,773 to Oregon State.

Research objectives:

This research will investigate whether and how open source software tools and technologies have gender biases tied with diverse problem-solving styles, and how to remove any such biases.

This work will harness foundational gender research to provide theory-based yet practical solutions and redesigns of open source software projects to address the underrepresentation of women.

The redesigns and the process of creating inclusive tools will be empirically evaluated to create a compendium of “best practices” for fixing gender-bias bugs, in both products (what suitable fixes are to such bugs) and processes (how open source software teams can work together to fix gender-bias bugs).

Broader impacts:

Open source is having a significant impact on society, in the products it produces and the career paths that it facilitates. However, women are vastly underrepresented among open source developers. This is a significant concern to these communities because it prevents them from receiving the benefits of a larger talent pool and of team diversity. The problem is perpetuated when women developers miss the learning and professional growth opportunities that open source software projects provide, and are overlooked when open source contributions are used to make hiring decisions. Our work will help break down these gender-bias barriers in tools and technology used in open source software.

More information is on the NSF website.