When I first heard about the concept of a Career Fair in my freshman year at Caltech, I half-joked to my friends, “I don’t want a career!” I came to college to learn math and science, and was quite honestly disgusted with classmates who would choose their course loads, student groups, or volunteer opportunities for the sole purpose of looking good on a resume.
This didn’t mean that I resisted growing up or planning ahead. I suspected I would want to go to graduate school, so I spent my first two summers doing research in my two favorite fields: math and chemistry. After all, this looked good on a grad school application for precisely the right reason: Experience doing research would prove to both myself and graduate admissions committees that I would thrive there.
During the rest of the year, I tried to learn all the things. I took the more difficult options for physics and biology requirements in addition to numerous advanced math and chemistry classes as a freshman and sophomore. I even sat in on the first few weeks of the main major-specific classes for astronomy and biology my sophomore year before the workload of organic chemistry lab caught up to me.
Over the last couple of weeks, I’ve been fascinated and inspired by an important milestone at the cutting edge of Artificial Intelligence: The first 11 games of StarCraft played between professional StarCraft players and AlphaStar, a team of AI StarCraft agents built by DeepMind, the team behind previous expert-defeating game-players AlphaGo and AlphaZero.
It started with this 5-minute teaser video put out by the DeepMind team a couple of weeks ago:
If you’re as hooked by that teaser as I was, you might enjoy the full demonstration video with famous StarCraft casters Artosis and RotterdaM:
Finally, if you’re more able to read than watch, I’d recommend DeepMind’s write-up. Spoilers coming ahead…
Last fall, I participated in a job-seeking fellowship called Insight Data Science. The official program lasts for seven very full weeks, but the job-seeking process continues afterwards for anywhere between a few weeks and a few months.
I was one of the lucky ones to get a job in the month following the program. So based on that fact alone, you might imagine that this will be a glowing review of the program’s success.
I finally got to see La La Land with my church community group this week. I appreciated its down-to-earth, intentionally banal depiction of Hollywood as well as the subtle poking fun at LA traffic and lack of seasons (a flash mob dance number during a traffic jam opens the movie, in “WINTER”).
The palm tree is part of the joke.
Everyone in the movie is striving to make it in the entertainment industry somehow. And it’s the depiction of this striving that forms the main tension in the movie and my deepest thoughts after it ended.
Boston has a similar feel, with seemingly everyone striving to achieve academic or entrepreneurial success. Well, that’s not entirely true — I’ve certainly met many, particularly in church, whose efforts also included a healthy dose of family and community. But if you spend enough time on campus and casual social gatherings, the first topic that often comes up is what you work on, or what you’ll be doing after you graduate, and you can come away with the same sort of impression that it’s why everyone came here.
But it wasn’t the cities that the movie made me think about the most, it was myself. What has happened to my dreams?
Last week, I wrote about some of the misconceptions that I had of math graduate school, essentially offering suggestions for how grad students should act and react within the academic system. In this post, I’d like to offer some suggestions for improving the system.
All of these suggestions relate in some way to building better community among students and researchers, something that’s been a bit of a pet project for me. I’ve been able to act on most of these ideas personally, but I also offer a moonshot at the end.