Dimitri Perrin on Muck Rack

Dimitri Perrin

Brisbane, Kobe
As seen in: Yahoo Canada, PNAS, The Conversation, Phys.org, Medical Xpress, ScienceAlert, The Quint, Scroll.in, Slate France, Le HuffPost and
Co-Director, @QUTDataScience. Assoc Professor @QUT. Head of Data Science, @Reds_Rugby. Data Science & ML/AI in health/biology (@BMDS_lab) and in sports.
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Articles

The US government can shut off access to AI at will. What does this mean for Australia? Original

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By Bernadette Hyland-Wood, Dimitri Perrin, Michael Guihot
| The Conversation Verified
Last Friday, US-based artificial intelligence (AI) company Anthropic received an “export control” directive from its government. The company was told it must block access to two of its most capable models, Fable and Mythos, for all foreign nationals. Within hours, Anthropic shut down access to the models for users everywhere in the world, including researchers, clinicians and analysts in Australia. This happened with no warning and no backup plan. Why did this happen?
Open in Who Shared View Duplicates (5) Issue with article?

The raw data that proves Super Rugby Pacific is currently a cut above

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By Dimitri Perrin
| RugbyPass Verified
Why is Super Rugby Pacific so exciting this season? There is a positive mood surrounding Super Rugby Pacific. Attendances are up 34%, viewership figures are on the rise as well (+12% in New Zealand, +17% in Australia, and +41% in Fiji), and there is no doubt that the 2025 season has been very exciting to date. But why have the first seven rounds of the competition been so exciting? It is true that winning margins have reduced, but that is only part of the story.
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Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

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By Carl Schmitz, Jacob Bradford, Robert Salomone, Dimitri Perrin
| biorxiv.org
Abstract CRISPR-based genome editing relies on guide RNA sequences to target specific regions of interest. A large number of methods have been developed to predict how efficient different guides are at inducing indels. As more experimental data becomes available, methods based on machine learning have become more prominent. Here, we explore whether quantifying the uncertainty around these predictions can be used to design better guide selection strategies.
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