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Rumi Naito defends her PhD

Congratulations to Rumi for an excellent PhD defence! “Messaging for wildlife conservation: leveraging attitudes, intentions, and actions for transformative change” With co-supervisor Jiaying Zhao, Rumi’s supervisory committee included Robin Naidoo, Jeff Sayer, and Intu Boedhihartono. Her external examiner was Kathryn Williams of the University of Melbourne, and her UBC examiners were Dave Hardisty and Daniel Weary. Bravo, Rumi!

Fig. 1 | How data leakage might occur in ecological applications, explained through the lens of shortcut learning.

CHANS Lab researchers depict how machine learning can suffer data leakage

In a new paper in Nature Ecology & Evolution, Andy Stock, Ed Gregr, and Kai Chan demonstrate how many routine applications of machine learning can inadvertently “leak data”, undermining accurate predictions.

Sánchez et al., Climate change and rainbow trout habitat

Sánchez, C., E.J. Gregr, E.A. Parkinson and K.M.A. Chan (2023). “The benefits of climate change mitigation to retaining rainbow trout habitat in British Columbia, Canada.” Regional Environmental Change 23(3): 108. 10.1007/s10113-023-02097-0

Stock et al., Problems with machine learning in ecology

Stock, A., E.J. Gregr and K.M.A. Chan (2023). “Data leakage jeopardizes ecological applications of machine learning.” Nature Ecology & Evolution 7(11): 1743–1745. Doi: 10.1038/s41559-023-02162-1