Dr Layton joined the University of Waterloo in 2018. Before then, she held the position of the Robert R. and Katherine B. Penn Professor of Mathematics, and Professor of Biomedical Engineering and Medicine at Duke University. She is the Deputy Editor of the American Journal of Physiology–Renal Physiology, Associate Editor of SIAM Review Book Section, and Associate Editor of the SIAM Journal on Applied Dynamical Systems. She also serves as the Associate Dean, Research and International, for the Faculty of Mathematics, and chairs the Research Equity, Diversity and Inclusion Council at the University of Waterloo.
Anita Layton
From this contributor
Early-detection technique analyzes how children with autism scan faces
Researchers at the University of Waterloo in Ontario, Canada, have developed a new autism detection technique that distinguishes among different eye-gaze patterns to help doctors more quickly and accurately detect autism in children.
Early-detection technique analyzes how children with autism scan faces
By
Anita Layton, Mehrshad Sadria, The Conversation
6 August 2019 | 4 min read
Explore more from The Transmitter
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
By
Juan Gallego
13 April 2026 | 8 min read
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
By
Natalia Mesa
10 April 2026 | 5 min read
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
By
Linda Douw, Klaus Eyer, Lara Keuck
9 April 2026 | 5 min read