from Guide to Undergraduate on Nov 6, 2022

How to prepare for research, without prior experience.

Making yourself an attractive candidate for research mentorship is hard. What even is "research"? Here is a brief guide to the foundations you need, to conduct research.

Preparation for research is effectively about building fundamentals — you don't need to be the world's expert, nor should you wait that long to begin. Having just enough understanding of the necessary components suffices. In this post, I'll skip over defining what "research" really is; this is a longer conversation, and instead, we'll focus on building fundamentals1 to conduct research.

Step 1 - Pick a field of study.

Your first step is to find a problem space, so you can focus your efforts on becoming the ideal candidate for that field. If you have no idea how to pick a field, don't worry: My advice here is to arbitrarily pick a domain and begin preparing for that domain. If you lose interest halfway, then there's no need to try research in the area. Your success in a field will be defined by your interest in that field's problems.

Your success in a field will be defined by your interest in that field's problems.

There are of course many broad areas of study, such as biology, computer science, statistics, etc. If you have not yet landed on a major, you can use your university's list of departments as a very general list of options. Within each department, you can then use colleges or the introductory classes to get a sense of broad topics.

For artificial intelligence and machine learning (AI/ML) in particular, here are several problem spaces; I'll focus only on the parts of these fields that overlap with AI/ML today:

There are also many researchers that study a blend of two or more of the above fields. For example, combinations of computer vision and natural language are very common, with text-to-image models, such as OpenAI's DALLE-2, being a very hot topic in the community at the moment. In sum, pick a general field at this point. Either you have an inclination, or you don't; if you fall into the latter category, pick one at random, so you can start knocking items off your list.

Step 2 - Evaluate your interest.

Once you've picked a field of study, evaluate your interest in that field. For AI/ML, take this 1-hour primer on "Tools to Learn Machine Learning". After this primer, you'll be much better equipped to process the deluge of information available. Then, follow the below steps to evaluate interest in your field. I suggest following these in order, either sequentially or in parallel.

It's worth noting that the above points aren't silver bullets for discovering research interest. It took me my entire PhD to finally find a subfield that I can call my passion, so don't sweat it if you only have vague interests. Vague is all you need. If you've found some level of interest, it's time to start thinking about preparing for research in that field.

Step 3 - Prepare with classes, papers, and internships.

To prepare for research, hone the relevant skills with established resources for doing so. For AI/ML, hone your coding and math skills, focusing your efforts based on your research interests:

These steps above will give you the fundamental knowledge needed to hold technical discussions and conduct research in your field of choice. To start developing research qualities off the bat, see What defines a "good" researcher?

Step 4 - Decide when to apply.

There are several "levels of interest" in research, which can help dictate how much time you want to sink in. It will also dictate the latest point you should begin conducting research. Here are the different distinctions and when you should begin research, for each objective:

These are the most common reasons for interest in research, but I'm sure there are many others I haven't covered. In general, my advice would be to start conducting research in your sophomore year to maximize research productivity.

If you haven't already, complete as many todos as you can, from the first and second steps above. If you're busy, set a timer for 10 minutes, and knock out as many as you can. Piquing your interest and finding a new curiosity is certainly worth 10 minutes of digging.

After you've taken these steps to prepare yourself, (or in my case, after having followed nearly none of these steps), start reaching out about research opportunities. You can find a fairly straightforward breakdown of how to do this in How to get into research, as an undergraduate.

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  1. Examples in this post are tailored to AI/ML, and this bias may have bled over into the tips themselves. However, these tips likely generalize to any applied field with a coding and math component.