Kirsten Ziman - Lecturer at Princeton University

Kirsten Ziman

Lecturer at Princeton University

New Jersey, United States

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Summary

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Kirsten Ziman is a cognitive neuroscience researcher and lecturer at Princeton University with a decade of experience bridging computational methods and brain science. After earning a PhD from Dartmouth and a BS from USC, she transitioned from a postdoctoral role at the Princeton Neuroscience Institute to a faculty position where she teaches and mentors students. She contributes to open-source scientific tooling—implementing visualization modules and test data for the Hypertools Python library—bringing practical skills in Python, matplotlib, and PCA-driven data exploration. Based in New Jersey, Kirsten combines rigorous experimental training with hands-on data-science engineering to make high-dimensional neural data more interpretable. An underappreciated strength is her ability to translate complex geometric insights into accessible visualizations that inform both research and teaching.

10 years of coding experience

2 years of employment as a software developer

Bachelor of Science - BS, Neuroscience, Bachelor of Science - BS, Neuroscience at University of Southern California

Doctor of Philosophy - PhD, Cognitive Neuroscience, Doctor of Philosophy - PhD, Cognitive Neuroscience at Dartmouth College

Github Skills (7)

data-visualizations10

data-visualization10

data-visualisation10

python10

matplotlib10

principal-component-analysis9

pca9

Programming languages (4)

TeXHTMLJupyter NotebookPython

Github contributions (5)

ContextLab/hypertools

Jul 2016 - Dec 2017

A Python toolbox for gaining geometric insights into high-dimensional data

Role in this project:

Back-end Developer & Data Scientist

Contributions:106 commits, 26 pushes, 1 branch in 1 year 4 months

Contributions summary:Kirsten contributed to the project by implementing a plotting module and test data. The module was built using the python and matplotlib, enabling a visualization of data to be outputted based on the array's dimensions. Furthermore, the user touched on some concepts such as PCA (Principle Component Analysis).

high-dimensional-datapythontoolboxdimensionalpython-toolbox

ContextLab/pupil-memory-analysis

Oct 2020 - Feb 2021

Contributions:94 commits in 3 months

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