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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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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