In the course of training to be a scientist, you generally learn some statistics and probability theory. I’ve grown to be quite fond of the topic, but as I’ve learned it, there are a few things in particular that I’ve found brilliantly satisfying. Simple tricks, some of which may seem counter-intuitive, but for some reason fascinated me when I grasped them.

Here are a few of them.

Right now, open a Word document and write down everything you've accomplished in 2016. Then celebrate! https://t.co/2XU0o8fH7F

— EricAnthonyGrollman (@grollman) December 28, 2016

Here it goes!

• Started my new position at Boise State University.

• Wrapped things up at Texas State and successfully passed four M.S. students.

• Submitted papers from three of those students’ theses, and a poster from some of the fourth student’s non-thesis work. The poster and one paper were rejected (the paper with very helpful reviews), the other papers are currently under review.

• Reviewed a large pile of papers for various venues.

• Saw the Grapht paper finally go to press. That was a very satisfying piece of work, and Journal of Object Technology was a great publication to work with.

• Founded the People and Information Research Team (PIReT) with Sole Pera. We currently have the two of us and 4 graduate students; we’re looking for new M.S. and Ph.D students for next fall, so apply if you’re interested.

• Wrote and presented a position paper for ACM RecSys 2016 with Martijn Willemsen, who I’ve worked with for a number of years now.

• Co-authored a paper with Jennifer for the first time. That worked really well, and she presented it at the RecSys ’16 Workshop on Recommender Systems for Engendering Health.

• Submitted a proposal to the Google Faculty Research Award program.

• Attended FAT ML and DAT and started to make connections in those communities. I am hoping for fairness to be a significant component of my research over the next few years.

• Gave my first on-the-road research seminar that wasn’t a job talk at the University at Albany.

• Launched a new writing collaboration that will hopefully produce a nice paper (or two?) next semester.

• Took on an exciting major service responsibility that I’m sure you’ll hear more about next year.

• Taught databases as a ‘normal’ class instead of once-a-week over ITV. I like this class, and it was useful to have something I’ve taught before as my first Boise State class to have some familiarity as I come to understand a new student body. I’m somewhat disappointed in how it went, as I know that I can deliver much better classroom experiences than the students got, but we move forward and learn.

• Proposed a new Introduction to Data Science graduate class I hope to offer in Fall 2017.

• Rebuilt the Recommmender Systems MOOC with Joe Konstan as a Coursera specialization, about 40% of which is currently available.

• Added code to automatically include citation counts in my CV.

• Made substantial progress on LensKit 3.

• Bought a house.

• Started building social connections and a support network outside the university. For us this primarily means finding a church community, but we have also made first steps towards connecting with local refugee support work.

My software toolbox evolves quite a bit, and I keep trying new things (a good or a bad habit, depending on who you ask; I’m grateful that my Ph.D adviser encouraged a reasonable amount of this tinkering). I’ve written about some of these tools before, but thought I’d compile a list of some of the important ones in my current stack.

I also maintain lists of some of the open source software I am using on OpenHub.

One of the themes in the most recent round of changes to my stack is reducing technical distance: making it easier to be able to recommend the software that I use to others, so that they can obtain a productive environment quickly. This means picking widely-available, usable software that works well (and provides modern conveniences) out-of-the-box. There are definitely places where I make exceptions to this, but I select a lot of user-facing software and development tools with this in mind.

This past week, I was at a pair of workshops on Workshop on Fairness, Accountability, and Transparency in Machine Learning and Data and Algorithm Transparency. They were both great workshops.

For obvious reasons, the election hung as something of a cloud over the meetings. It wasn’t constantly discussed, but we kept returning to it from time to time. It’s pretty sad, in my opinion, when ‘what does this work like if rule of law collapses?’ is a live question. Regulation is a key outcome of fairness research, and representatives from a number of regulatory agencies were in attendance. There’s a very real concern that regulation and policy will not be available levers for the next several years.

Some of the discussion, therefore, was about ways to supplement or compensate for lack of regulatory mechanisms. Far more questions were raised than answered, I think, but it was discussed both in the panels and in hallway discussions.

As we were talking about this, I couldn’t help but think of Bruce Schneier’s Liars and Outliers. I think this book provides a very helpful framework and language for reasoning about what, exactly, we might be trying to do as we promote fairness and nondiscrimination.

Inspired by Philip Guo’s post, here are the application materials I submitted in the course of my two computer science faculty job searches.

I am posting these in the hopes that having more examples available helps some job applications. However, it’s important to note that there is not a formula you can — or should — follow slavishly for these documents. When I took Preparing Future Faculty at UMN, our instructor encouraged us not to read other teaching statements before we wrote our own, so that our statements came from us. I don’t know that you need to go that far, but your teaching and research statements should reflect you as a teacher and scholar.