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SciPy 2018 Talk Highlights

Sun, 29 Jul 2018 00:00:00 -0400

Last year I gave a list of my favorite SciPy 2017 talks. The SciPy 2018 conference took place from July 9 to 15 and the talks and tutorials are now in a YouTube playlist created by Enthought. I have gone through all of this years talks and watched through any that seemed interesting. Read on for my suggestions!

Overall, it felt like there were a lot of machine learning and geoscience/geo-related talks and packages. A lot of the ML talks seemed to be missing practical insight into applying a specific method or seeing how someone solved a problem in a way that generalizes to other problems. The geoscience ones weren't very interesting to me, but might be worth checking out if you're interested.

Why Jupyter Notebooks Won't Replace Academic Papers

Sun, 29 Jul 2018 00:00:00 -0400

Recently there has been some buzz around Jupyter Notebooks in science, especially in light of the LIGO team sharing their detection and analysis of gravitational waves in Jupyter Notebooks. Others have claimed that Jupyter Notebooks will render traditional academic journal articles obsolete. The notebook or literate programming format improves on the reproducibility and disseminating of scientific work, however several key factors limit the notebook as a way to communicate science. In the article, I'll touch on these issues and explain why I believe that the current model of sharing computational science is here to stay.

SciPy 2017 Talk Highlights

Mon, 17 Jul 2017 00:00:00 -0400

The full SciPy 2017 conference took place from July 10 to 16. The talks and tutorials are now live in a YouTube playlist created by Enthought. I have watched most of the available talks that seemed interesting from a materials science perspective, specifically talks geared towards building scientific packages and computational tools like pycalphad. Read on to see the four talks I found most interesting

Keynote - Coding for Science and Innovation

Computing has been driving forward a revolution in how science and technology can solve new problems. Python has grown to be a central player in this game, from computational physics to data science. I would like to explore some lessons learned doing science with Python as well as doing Python libraries for science. What are the ingredients that the scientists need? What technical and project-management choices drove the success of projects I've been involved with? How do these demands and offers shape our ecosystem?

Open-Source Learning

Tue, 06 Jun 2017 00:00:00 -0400

New researchers, graduate and undergraduate students, spend much of their first 6 months to first year in their research group learning the techniques the lab uses to do their science. This is espeically true in computational fields, where there are numerous softwares (open- and closed-source) using in specific domains. Challenges associated with joining computation-focused research groups are exacerbated by the fact that many undergraduate curricula in applied research fields tend to lag behind in the use of software tools. In my experience, relatively few undergraduates in engineering consider themselves to be proficent software developers (CS, CE, etc. aside). Research groups need better ways to teach new scientists software fluency.