Discovering How AI is Utilized Across UW

The eScience Institute co-hosted Discovering AI@UW, an event that brought together artificial intelligence experts from across the University of Washington campus to discuss their projects and how they intersect. Over 180 people attended the event, both streaming online via zoom and in person in the Lyceum Room of the Husky Union Building on campus, and included UW faculty and staff, postdoctoral fellows, graduate students, and undergraduates. “The energy around all these [AI initiatives] that people are doing here makes me tremendously excited,” said Nathan Kutz, Senior Data Science Fellow and Adjunct Professor of Electrical Engineering & Physics. “We not only have a university that is incredibly collaborative, but we also have so much work going on in so many different domains.”

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Evolving the Hackweek Model with ICESat-2 2022

The eScience Institute hosts a variety of hackweeks every year, which are designed to immerse participants in collaborative project work around a specific topic. Hackweeks try to blend elements of a hackathon, where participants work collaboratively in project teams, with tutorials on a variety of data science topics in an immersive and inclusive environment. eScience hackweeks provide a deep dive into an area of science with a focus on how data science methods and tools can be utilized to further research. For each hackweek, the program format evolves and is modified and adjusted to best suit the problem space and the user community. A great example of this process is the ICESat-2 Hackweek, which wrapped up earlier this year.

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Python for Humanities: an Intro for Researchers

The eScience Institute and UW Libraries Open Scholarship Commons recently co-hosted a workshop called “Python, your personal research assistant” for participants studying the humanities to explore the Python programming language and how to use it as a tool to aid in qualitative humanities work. Led by eScience Technical Education Specialist Naomi Alterman, the program encouraged students to decipher lines of Python, and learn how to make use of it to complete repetitive tasks. “I’m expecting folks to show up to the workshop with no experience with computer code,” Naomi Alterman said. “And I want them to leave with a suitable argument as to why it’s useful for them in the future.”

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Modeling & Predicting Tree Growth with Data Science


Stuart Ian Graham is a graduate student in the University of Washington’s Biology program who recently published a paper with Senior Data Science Fellow and eScience Institute Research Scientist Ariel Rokem, along with others from the University of Washington, Université de Montpellier, and University of California Los Angeles. The paper, published in the Forests journal and titled “Regularized Regression: A New Tool for Investigating and Predicting Tree Growth,” initially grew from a 2019 Winter Incubator project at eScience, which paired Graham and Rokem together to utilize data science to explore how neighboring tree species can influence one another’s growth rates in Mt. Rainier National Park in Washington State.

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Data Science Student Profiles: Stefan de Villiers


The University of Washington hosted the Data Science Minor Showcase several weeks ago, an event for undergraduates to explore the curriculum offered as part of the Data Science Minor program that was launched in Fall 2020. The showcase featured UW faculty outlining the new courses they have developed for the Minor, personal experiences from students who are currently enrolled in the minor, as well as smaller breakout sessions for participants to learn more about possible pathways towards data science from their areas of interest. One of the students who shared their experience with the Data Science Minor program was Stefan de Villiers, a UW senior who is majoring in Economics in addition to minoring in both Data Science and Mathematics.

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Help the Fibr Algorithm Learn to Read MRI Scans

At the University of Washington, eScience Data Science Fellow and Research Assistant Professor of Psychology Ariel Rokem and UW Data Science Postdoctoral Fellow Adam Richie-Halford have created a way for the general public to help an algorithm learn to read MRI scans. Fibr utilizes the vast dataset of the Healthy Brain Network to better understand how mental health disorders are first diagnosed in childhood and adolescence. But in order for the algorithm to differentiate between scans that show long-range fiber connections in the brain and those that don’t, it must first learn what to look for. Regardless of scientific training, anyone who wants to participate can view a short tutorial and start guiding Fibr towards new innovations in neuroscience and beyond.

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