So, I won the NSF CAREER award. To say I’m excited about this would be an understatement — my first Ph.D student has support locked in, I get to actually do the work I’ve been building towards for years now, and we’re going to have a much better understanding of how recommender systems (mis)behave in response to their individual and social human contexts.

One of the things I found useful while planning and writing was hearing a variety of ‘path-to-the-CAREER’ stories and trying to take from them the things that would work for me. So here’s mine, for what it is worth. There are many paths to success; the opening line of Anna Karenina does not apply to grantwriting. My road is neither necessary nor sufficient.

This post is adapted and heavily expanded from notes I wrote in preparation for the successful applicant panel at Boise State’s CAREER prep workshop this spring.

Over the years of teaching and research, I have gradually standardized the notation that I use for describing the math of recommender systems. This is the notation that I use in my classes, Joe Konstan and I have adopted for our MOOC, and that I use in most of my research papers. (And thanks to Joe for helping revise it to its current form.)

If you haven’t already settled on a notation, perhaps you would consider adopting this one. I also welcome feedback on improving it.

Fabric posters are great. You don’t need a clumsy poster tube, just fold it up and put it in your suitcase.

We use Spoonflower for ours, and there are even instructions. But there are a few details that take some extra work to get right; I hope that this will guide you through them.

I have also made a video of the process, which you can view on YouTube.

The very short version, so you have an idea of where we’re going:

As you’re hopefully well aware if you follow my Twitter, the end of this month will bring the first Conference on Fairness, Accountability, and Transparency (FAT*). It’s been an honor to be involved in some of the planning for this conference series; it is also a great honor to have two papers in the first edition. Algorithmic fairness is the main focus of my research agenda for the next several years.

Last year, I wrote up the software and hardware tools I use. I am still using a lot of that stack, so I thought this year I would just highlight the changes: