About me

Hi I’m Jeremy! I am a research scientist in the Steinmetz Lab at the University of Washington. In the lab, I am currently developing statistical models to link animal behavior to neural activity, designing methods to integrate transcriptomic and electrophysiology data, and recording from the mouse brain using Neuropixels probes.

My academic interests lie at the intersection of neuroscience, and machine learning. In particular I am interested in how machine learning can help propel neuroscientific discovery by helping to decipher massive neural datasets. We are in an unprecedented time in neuroscience, in which we have the ability to simultaneously record from many more neurons than ever before. At the same time, advances in machine learning have given us powerful tools for analyzing this complex, and high-dimensional data to extract meaningful insights into how the brain works.

The machine learning flavor I am most interested in is known as latent variable modeling, which itself is a type of probabilistic machine learning. Latent variable modeling is all about relating observed data to a smaller set of unobserved factors or latent variables, which are inferred from our observations. These latent variables often reveal beautiful structure in otherwise dense and difficult to understand data. My research aims to leverage these methods in order to better understand how networks in the brain work in concert to enable perception, cognition, and action.

I am also passionate about neuroscience education. I designed and teach NEUSCI440, a course at the University of Washington which introduces undergraduates to computer programming in the context of neural data analysis. By focusing the course on neuroscience, we strive to make coding accessible for students who would perhaps not enroll in a progamming course otherwise.

When I’m not thinking about the brain, you can most often find me playing the guitar or making electronic music.

Thank you Gregory Gundersen for the blog theme!