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Description
Schedule
My schedule is a bit wonky:
I'm arriving in London at 11am on Monday the 12th (the week before our big meeting).
- Mon 12th - 12:00pm onward: Oxford e-Research Centre
- Tues 13th - Oxford
- Wed 14th - Open
- Thurs 15th - Cambridge + eLife
- Fri 16th - Cambridge + eLife
- Mon 19th - Turing London
- Tues 20th - Turing London
- Wed 21st - AIDA workshop
- Thurs 22nd - Flying back :-(
Interests / projects
My background is in cognitive neuroscience, and I've been a dev with the MNE-python team for many years now. I'm interested in applying machine learning to electrophysiology data, and open source analytics around these questions. Also interested in neuroscience more generally.
Currently, I'm a core developer on the Jupyter project. The major projects I work on are:
- JupyterHub - which lets you create a jupyter server that runs in the cloud which servers can access
- Binder - which lets you create sharable interactive computing environments that exist on GitHub
- MNE-Python - tools for electrophysiology analysis and machine learning in neuroscience
- Data Science Education Program - Is a collection of classes at Berkeley in foundations for data analytics. All courses are taught on open-source software and in tandem with technical development in the OS world.
More generally, I'm interested in building core infrastructure ("building blocks" if you will) that is open-source, community driven, and has a number of potential applications. I'd love to find scientific or educational use-cases to drive more development for tools in the JupyterHub/Binder ecosystem, or for analytics packages such as MNE.
I'm also very interested in questions around sustainability in the open-source and technical development world for the sciences. Such as:
- How do we combat burn-out?
- How do we fund projects without sacrificing the open-source soul of science?
- How do we create technology that loosens bottlenecks such as the one the publishing industry currently has in academia?
- How can we engage in a long-term culture shift towards more technical / computational / open methods and skills in science and the academy?
- How can we encourage people to contribute to pre-existing things in open-source rather than creating new things?
- How do we retain smart, talented analysts and developers when they're in high demand from companies?
- How can we blur the line between academic work and more "applied" work from within universities?
If any of that interests you, I'd love to chat!