Eric Johnson: About Me

I am a passionate educator and researcher, currently living in Minneapolis, Minnesota with my longtime partner and two cats.

I work as a postdoctoral researcher for the Mani and Pincus groups at Northwestern University and the University of Chicago, where I study the ways in which we analyze high-dimensional data in biology. Specifically, I’m looking at how we can best understand single-cell sequencing data from Yeast. More generally, I like to think about what modern data sets look like and how we can choose or adapt modern techniques to analyze these data.

Professional Background

This website serves as a landing page that will connect my various projects and interests.

For those who might be interested in my professional background, my C.V., teaching statement, list of publications, and other materials can be found linked to the left.

What Do Your Data Say?

What Do Your Data Say? is a course that I have co-developed with Professor Mani over the past several years in order to teach students how to think quantitatively about data, especially in biological contexts. As a result of this work, we have developed a set of course notes and materials that can be used to teach this one-term course. This course has been successfully taught to high-level undergraduates and graduate students several times and has also been adapted into a 4-week bootcamp that we taught to hundreds of students in April 2020. These resources can be found at my WDYDS repository.

Python Tutorial

In order to prepare students for quantitative thinking about data, it is sometimes necessary to bring them up to speed on basic programming. This tutorial was developed to bring students from little or no programming experience to the level of exposure needed to participate in the What Do Your Data Say? class.

EMBEDR

Empirical Marginal resampling Better Embeds Dimensionality Reduction is a method I developed during my Ph.D. to quantitatively evaluate the extent to which dimensionality reduction methods are actually preserving high-dimensional structures in their embeddings. This work was also presented in my thesis and the code can be found here.