Jess Mankewitz is a developmental psychologist and computational researcher who combines experimental work on infant and toddler curiosity with strong data-science and modeling skills. With eight years of experience across Stanford, UW–Madison, and UC Berkeley, she specializes in cleaning and integrating messy multimodal datasets, building statistical and computational models in R and Python, and visualizing complex developmental patterns. She has managed and standardized large cross-lab databases (including CHILDES and eye-tracking corpora) to enable novel comparative analyses of language and learning. Early IT experience at SpaceX and leadership of a campus tech team give her uncommon pragmatism for scaling research infrastructure and tools. Currently a PhD student in psychology at UW–Madison and a research assistant at Stanford, she bridges theory and reproducible computation to ask new questions about how children seek information.
8 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Cognitive Science, Bachelor's degree, Cognitive Science at University of California, Berkeley
Doctor of Philosophy - PhD, PSYCHOLOGY, Doctor of Philosophy - PhD, PSYCHOLOGY at University of Wisconsin-Madison
The childespy package allows you to access data in the childes-db from Python.
Contributions:2 releases, 40 commits, 15 PRs in 3 months
data-accesspython
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Jess Mankewitz - Graduate Student Research Assistant