The last day of RIPS was a week ago. It ended up being a good program for me, but I remember how unsure I was when I first got the offer and how long I considered it before saying yes. I wasn't sure what it would be like to be an engineer at RIPS, if I would end up as the team project manager because of my experience on teams (or worse, end up PMing despite not being the official PM), and whether I would end up on a project that would be interesting to me. So now, looking back, here's what I would tell another Oliner considering RIPS.
1. You will be in a mathematical environment. This is important. A lot of the participants will be math majors. The problems you work on will be mathematical, some more than others. (I ended up with one of the most mathematical RIPS projects in years.) There's a lot of encouragement to present at math conferences and publish your work in a math journal.
2. You won't be the only non-math major. Despite #1, RIPS is a very interdisciplinary environment. CS and physics are the most common other majors, but we also had a couple of engineers and someone doing economics and stats. It's also not the kind of math environment in which everyone is planning on getting a PhD, much less going into academia.
3. You will probably have the most experience with teaming. In an interview for a RIPS promo video, I was asked whether I'd worked on a team project like this before. I counted the team projects I've done at Olin, and there are at least ten significant ones. That's a lot compared to other RIPS students. In general, though, people have at least been in a research group of some kind before, so most people have team experience.
Even if you haven't PMed at Olin at all, you'll probably be one of the people who best understands the role of a PM. Don't volunteer to be your team's PM if you don't want to do it, but if you're at all interested, emphasize how much team experience you have. You know what good and bad teaming look like. Whether you're PM or not, keep an eye on team health, and don't be afraid to say something if there are issues. I really wish I had done this more and earlier; it could have prevented some problems my team had near the end.
4. A lot of the projects are data science-y. If this is something you enjoy, great! If what you really want to do is modeling or something more related to the sciences, there will probably be a couple of relevant projects, but they're less common than they used to be. No matter what your interests are, though, it's a good idea to mention in your application what projects from previous years look interesting. One of my team members was on the Lawrence Livermore team because he mentioned really liking the project they sponsored last year. RIPS does a pretty good job of placing students on teams, but telling them what previous projects you would have wanted to do helps them a lot. The sponsors for your year won't be posted when they apply, but they probably will be by the time you have to accept an offer. If they aren't, the teams don't change a ton each year, so looking at the sponsors from the past couple of years should give you a good idea of the kinds of projects there will be.
5. It will probably feel a little guided to you. Math SCOPE is how I usually describe RIPS to Oliners, but I think there's more guidance than there is in SCOPE. The academic mentors (equivalent of faculty advisors in SCOPE) are a little more involved, there are more rounds of drafts of everything, and there's a lot more focus on presentations. A more concrete example of guiding is that the final decision about who will be a team's PM does not rest with the team. Everyone who is interested talks to the academic mentor, who discusses it with the director, and they choose.
6. A lot of your experience will depend on your team and your project. This should not be a surprise to any Oliner.
7. You will learn some math, you will learn about your application, and you will probably code a lot. My team was the one exception to that last point this year, and we still wrote some code. The first point varies, too. My team learned so much math, but others probably only worked with a few relatively straightforward algorithms.
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