I’ve often closed out the year with two blog posts: my tool report, and a year-in-review. It’s been a pretty full year!

One of the major accomplishments was moving to Philadelphia to take a new position in the information science department at Drexel University¹. I’m very excited for the coming years here, as the interdisciplinary information science context is an ideal home for my approach to research and scholarship. I did have to give up tenure for the move, but I think it was worth it.

Beyond that, a few other good things have happened this year…

It’s time for another review of my current toolkit!

With a new job and a new city, I needed to re-assemble my work computing setup from scratch and am now running MacBooks both at home and work, so there are a number of changes. I also completely overhauled our home network. Quite a few software things have stayed the same, though.

It is a truth universally acknowledged that parallel computing on Python is an experience that ranks somewhere alongside “facial exfoliation with a rusted cheese grater” in its pleasantness.

In this post I’m going to briefly review our particular (common!) problem, the state of multiprocessing in Python, and why it unfortunately appears necessary to continue using my own parallel processing layer in LensKit.

There are a number of steps in preparing the final (or “camera-ready”) version of a paper for publication. This post attempts to document them. Many of these points are about making the paper consistent with itself, so that it doesn’t look sloppy. This list probably isn’t complete, but hopefully it’s a helpful start.

Things change. Seasons end, and new ones emerge.

In 2016, I joined Boise State University, and founded the People and Information Research Team with Sole Pera. This was the first (only?) multi-PI research group in the department, and one of a very small number of multi-PI recommender systems groups in the US.