I’m a fellow at the Berkeley Institute for Data Science and a member of Project Jupyter as well as the Binder Project. I work at the intersection of technical development, open communities, scientific research, and education.
I work with teams to create and improve open-source technology for scientists, educators, and data analysts. My goal is to help people do their work more effectively, openly, inclusively, and reproducibly.
I used to be a cognitive neuroscientist using predictive modeling to study the ways in which the human auditory system understands speech.
Check out the links above for more information on some of the things I’ve been up to, or see my blog for some of my thoughts.
Scientific Research and Open Scholarship
I’ve been involved with several projects in academic / scientific research. These mostly focus around “meta” issues in open source communities, open standards and practices in scientific fields, and computational neuroscience.
For a list of publications and scholarly artifacts in which I’ve been involved, check out my ORCID page.
Open Source and Open Community
Much of my work revolves around open communities that create tools and projects for scientific research as well as education. Below are a few core projects I’ve been involved with recently.
The Binder Project
Binder allows you to create custom computing environments that can be shared and used by many remote users. It is powered by BinderHub, which is an open-source tool that deploys the Binder service in the cloud. One-such deployment lives at mybinder.org, which we run as a free service.
The goal of Binder is enable people to share reproducible, interactive versions of their code with others as easily as possible. It is used by people across the scientific, education, and analytics communities.
Project Jupyter and JupyterHub
JupyterHub is a tool that lets an administrator serve many user sessions from a single machine. The Zero to JupyterHub guide is an instructional and opinionated guide to deploying a JupyterHub on Kubernetes, a framework for deploying / managing cloud resources.
The Zero to JupyterHub guide was originally written as an extension of the technical infrastructure for UC Berkeley’s Data 8 course, and since then has become the most popular method for running a JupyterHub at scale in the cloud.
The Docathon is a week-long global sprint where we focus our efforts on improving the state of documentation in the open-source and open-science world. This means writing better documentation, building tools, and sharing skills.
The first Docathon was held in 2017, and had participants from across the globe.
More than 40 open-source projects contributed, and in total we put out a roughly ten-fold increase in contributions to documentation over the week!
The next Docathon is TBD, but if you’re interested in being involved please reach out!
MNE-Python is open-source software for exploring, visualizing, and analyzing human neurophysiological data (MEG, EEG, sEEG, ECoG, etc).
After my PhD, I spent some time generalizing the code I had written for receptive field analysis of human ECoG data, which now exists in MNE-Python.
Here’s some more “official” CV-style info, if that’s what you’re looking for.
- B.S. in Neuroscience, Tulane University, 2009
- M.S. in Neuroscience, Tulane University, 2010
- Ph.D. in Neuroscience, University of California at Berkeley, 2017
If you want a hard-copy CV, you can find a reasonably up-to-date CV here