If a tree falls in the woods and nobody hears, does it make a sound? (Or, making sure your code runs anywhere.)

Friday, September 28, 2018 - 1:25pm - 2:25pm
Lind 305
Tyler Whitehouse (Gigantum)
Most researchers don’t have the time or the skill to apply the kinds of software development best practices needed to make their computational work transparent, reproducible, and easy to use. Despite repeated calls by publishers and funders to include usable and understandable code with publications, it is still too much work to be done with the resources allotted. Such problems are not unique to academia, and the penetration of machine learning and data science in industry has brought these issues to the attention of commercial enterprise as well. Reproducibility is a problem for everyone.

This talk will demonstrate an open source data science platform that automates the issues around transparency, reproducibility, and ease of use for work done in open source languages and frameworks like Python and R. It will show how easy it is to set up computational and data science environments of varying complexity that can be shared with anybody in the world with no extra labor or set up.

Tyler Whitehouse did an undergraduate degree in math at UC Santa Cruz and a Ph.D. at the University of Minnesota. He graduated in 2009 after working with Professor Gilad Lerman on problems dealing with the rectifiability of sets and measures in Hilbert spaces, then going on to Vanderbilt University as a postdoc for 3 years. From 2012 to 2017 he worked as a data scientist and consultant in the Washington DC area. Currently, he is the president of a data science software startup in the DC area.