My year in review post. 2020 was a terrible year, all things considered, but I found opportunity to rekindle my reading habit and write consistently. Inside are reflections on personal and career growth, the development of my research skills, and some of the best things I read this year.
It’s been a really challenging year. I don’t know that this needs to be said, but this post would feel incomplete without acknowledging it. I’ve noticed tension in personal and professional relationships that is nobody’s fault—just people being on edge.
But I’ve tried to make the most of it. I was promoted to senior data scientist at work, which felt great after the ups and downs of the past couple of years. I gave my first conference talk at PyMCon. I moved in with my partner and adopted a new kitten (this is Rhubarb). And I’ve read more books this year than since I was a child, which particularly feels great.
I’ve set three goals for 2021:
- to write something every day (whether it’s journaling, a paper write-up, or a summary of something I read), inspired by Stevie Chancellor’s How to write more with less stress by writing every day
- to cook more often (I started getting takeout a little too much at the end of the year!)
- to do some kind of mindfulness exercise, usually meditation, every day
I hope to keep myself accountable to these.
I use GoatCounter for analytics, starting in June of this year. I had originally set up Google Analytics, but was overwhelmed by the complexity of the interface and felt that it was unnecessary. All I needed were order-of-magnitude estimates for how many visitors I was getting and what pages were most popular. GoatCounter does this with a simple UI, and is open-source and privacy-respecting.
According to GoatCounter:
- I had roughly 1400 visits between June and now (they only retain data for 6 months)
- Around 30% of visits were to my homepage. The next most popular pages were my resume, about me, and reading list; I’ll have to spend some time improving those in the new year.
- I saw a surge of traffic after giving a talk at PyMCon. In particular, lots of folks found my post on Bayesian inference resources from the PyMC forums
- 20% of my hits came from Google—even more for individual paper write-ups—suggesting that people are finding my site by searching the names of papers.
External validation: this year, I received cold emails from readers for the first time! Most of these came from people searching papers that they’d written about. It felt amazing to see that people actually read this; I write primarily for myself, but external signals are reassuring, too.
Finding community: in September, I joined a Discord server for tech writers founded by Will Larson: techwriters.dev. I don’t know if this group will last, but I’ve loved having a group of people who value writing like I do. If you’re interested, I suggest joining us!
Next year: I think leaning into marketing myself would be valuable. I’ve relied on organic traffic so far, but I imagine that e.g., tweeting about new posts would be a good idea. That’s intimidating, though; no commitments here.
I published 204 posts in 2020; that’s roughly one every other day or so. Of these:
- 72 were paper summaries, including 17 from my reading club with Judah
- 48 were weekly roundups of articles I read online
- 29 were general blog posts
- 25 were on books I read
- 21 were notes on talks from Spark + AI summit, after having done the same last year
I’m particularly pleased with the 72 papers; my goal for the year was 50 (one per week), and I’m happy to see I blew past it.
Next year: I’d like to continue reading books and papers, but I’m not setting any specific goals. I think I read enough in 2020, and I’m intrinsically motivated enough to know I’ll keep it up. I expect to be happy with whatever I end up with at the end of the year. I would like to read more fiction, though!
As listed above, I wrote about 72 papers this year. Of these, 20 were from CHI, 7 from CSCW, and 5 from ICWSM. Some of the topics covered included social computing, content moderation and mental health on social media, and fairness in machine learning.
How did I find papers? Many of them came from conferences that were happening, and others came from Twitter. I really liked the practice of keeping up with conferences throughout the year, so I plan to continue that this year.
My favorite papers included:
- ‘At the End of the Day Facebook Does What It Wants’: How Users Experience Contesting Algorithmic Content Moderation by Kristen Vaccaro, Christian Sandvig, Karrie G Karahalios
- Moving Across Lands: Online Platform Migration in Fandom Communities by Casey Fiesler, Brianna Dym
- Critical Race Theory for HCI by Ihudiya Finda Ogbonnaya-Ogburu, Angela D.R. Smith, Alexandra To, Kentaro Toyama
- Emergent Self-Regulation Practices in Technology and Social Media Use of Individuals Living with Depression by Jordan Eschler, Eleanor R. Burgess, Madhu Reddy, David C. Mohr
- Methods in predictive techniques for mental health status on social media: a critical review by Stevie Chancellor, Munmun De Choudhury
- Random, Messy, Funny, Raw: Finstas as Intimate Reconfigurations of Social Media by Sijia Xiao, Danae Metaxa, Joon Sung Park, Karrie Karahalios, Niloufar Salehi
Looking back at my write-ups from a year ago reveals how much I’ve grown as a researcher. I can get through papers and extract the main points more quickly (though writing about them still takes a while). I have a clearer sense of my research interests (less fairness, more social computing than I originally realized). And I can more easily ask questions and think about extensions when reading something new.
This is good! These are all signs that I’m growing as a researcher. At the start of this year, I had no idea I would have read 70 different papers. To be sure, COVID gave me more free time and I’d have much preferred to spend time with my friends. But as a forcing function for reading more, I guess I’ll take it.
I’m looking back at Why all the paper summaries? from November 2019. I wrote that I would like to develop clearer research interests, and that reading a lot would be the best way for me to learn. Those still hold up.
I read 17 nonfiction books and 5 fiction ones (the Percy Jackson series, haha). This post is getting long, so I’ll be brief here.
My favorite book of the year: How to Do Nothing by Jenny Odell. The book consists of a series of loosely linked essays on finding your place in the world, a deeply personal topic that helped me to reflect on my existence and relationship with everything around me.
The book that made me laugh the most: You Look Like a Thing and I Love You by Janelle Shane. This combined clear and accessible explanations of what AI is with hilarious examples of how it can go wrong. From ice cream flavors like “Chocolate Peanut Chocolate Chocolate Chocolate” to “Blood Pecan,” I was entertained on every page. I recommend this to anyone wanting to learn more about AI in a lighthearted way!
Other highlights included Because Internet by Gretchen McCulloch, No Filter by Sarah Frier, and It’s Complicated by danah boyd. I haven’t posted reviews of them, and I don’t know that I will (since I’d rather just read more!), but all were excellent.
Next year: I simply want to keep reading; no numbers, though I would like to read more fiction. I loved reading as a kid, but stopped reading for fun in college. As I wrote earlier, COVID keeping me at home (thankfully) gave me time back to read. Now that I’m in a habit of it, I hope I can keep it up.
I’ll keep this short. My main project was a rescue tool for the video game Pokemon Mystery Dungeon: Rescue Team DX; I published a series of posts as I built it. I mostly wanted an excuse to use Pyodide (Python compiled to WebAssembly) and Tailwind CSS (which I’ve come to love), and this project was a fun learning experience.
I also wrote a tool to generate and solve “zebra puzzles,” also called logic grid puzzles or the Einstein puzzle. I’ve loved these since I was a kid, and building this out was really great.
Finally, I had two projects that made use of Github Actions, as I’m trying to learn more about CI: CI-Doku, which runs a nightly web-scraping job to get me Sudoku puzzles to solve, and shinyrates, which automatically grabs and logs Pokemon GO shiny rates data. Building projects like this makes me miss software engineering; I think if I weren’t so interested in HCI research, I would pivot my career to machine learning engineering.
This was a challenging year. I’m fortunate to have been able to stay safe and healthy. Having stable employment and free time are signs of privilege and fortune. Recognizing all of this has helped me to cultivate habits of mindfulness and gratitude, which I’m excited to carry with me into the rest of my life.
I appreciate having had more time to read and write. Of course, I’d rather have seen my friends and family, but we weren’t offered that choice! Next year, I hope to build back up the friendships that have been tested by stress and distance; there are so many friends I’ve been looking forward to seeing for a while.
I don’t know what 2021 will offer, and it’s not yet clear how much better it will be. But just being able to say that it’s not 2020 anymore is a bright spot. I’m looking forward to another great year of reading and writing-hopefully with less time in my apartment.