On September 29, I received my master’s degree in industrial engineering from Seoul National University!

It’s been a wonderful two years. Despite the challenges of social distancing, I was able to take a diverse assortment of courses in optimization, economics, and human factors; participate in research projects in two different labs; present at a conference in Jeju; and write (in both Korean and English) a thesis I’m rather proud of.

In this post, we’ll use fake numbers and a simplified model to argue that the most college applicants should apply to far more colleges than they actually do. You can probably extend the argument to job applications and the dating game. I’ll also compute a few fake comparative statics and speculate about why real students don’t apply to more schools.

Consider the following test question:

Which of the following are US States? (select multiple)

1. Washington
2. Delaware
3. Frankfurt
4. Memphis

This is a multiple-selection question, so the correct answer is a and b. How would you grade a question like this on an exam?

I recently created Mulberry, a web app students can use to compute their optimal college application strategy. It is a free, open-source, user-friendly interface to Algorithm 1 from my MS thesis.

One of my current projects involves editing several hours’ worth of subtitle files in the .srt format to accompany an online course in numerical optimization. Because we haven’t decided exactly how we want to break up the videos, I needed an efficient way to delay or advance all the subtitles in time, so I wrote a small Python program called srt_delay.py today to help me with this task.

Usage examples below the cut, or you can just go straight to the README in the GitHub repository.