Kate Tsiplova is a statistician with the Population Health Research Institute in Hamilton, Canada, and has also worked in the field of economic evaluation with Wendy Ungar at the Technology Assessment at Sick Kids in Toronto, Canada.
Kate Tsiplova
Statistician
Population Health Research Institute
From this contributor
Detecting a signal amid noise in autism early-intervention research
Studies of behavioral treatments for autism are complex and can easily be misunderstood. Here we provide some guidance.
Detecting a signal amid noise in autism early-intervention research
By
Isabel Smith, Kate Tsiplova, Wendy Ungar
28 March 2023 | 5 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