How undergraduates actually get into a lab

Why this is worth doing

Research is one of the few things an undergraduate can do that is genuinely rare among applicants. Coursework is common, projects are common, internships are competitive but common. A publication with your name on it is not common, and it signals something specific: that you can work on an open problem for months without a solution key and write it up to a standard that survived peer review.

It also compounds. One paper makes the second easier, gives you a reference who has seen you work rather than seen your grades, and opens graduate admissions if that path ever becomes interesting.

Finding a lab

You are not looking for "a research opportunity". You are looking for a specific person whose specific work you can describe in a sentence. That distinction is what separates an email that gets a reply from one that does not.

1. Read the department's faculty directory and shortlist five to eight people whose research area you can actually follow at your level.
2. Find their recent papers — the lab page, their Google Scholar profile, or the conference proceedings for their field. Read the abstract and introduction of two of them properly.
3. Check for a lab website. Many list open positions, expected commitments, and the exact application process, which makes the cold email unnecessary.
4. Ask your instructors. A professor who has taught you and likes your work is the shortest path in the entire process, and their referral to a colleague carries real weight.
5. Look beyond your own department. CS skills are wanted in education, biology, linguistics, public health, and economics labs, and the competition there is far thinner.
6. Check for funded programmes — undergraduate research awards, REU-style summer programmes, and departmental assistantships all have formal applications and deadlines.

The unfair advantage for a CS undergraduate is that most labs outside CS have data they cannot process well. Being the person who can write the pipeline, clean the dataset, or build the study tool is a legitimate entry ticket even before you understand the domain.

The email

A professor receives a lot of generic requests and ignores nearly all of them. The three things that get a reply are: evidence you read their work, a concrete offer of what you can do, and a small ask. Keep it under 200 words.

Subject: Undergraduate interested in [specific topic] — [your name] Dear Professor [Name], I am a [year] [major] student at [university]. I read your paper "[exact title]" and found [one specific thing — a method, a result, a limitation you noticed] genuinely interesting because [one honest sentence, in your own words]. I have [concrete relevant skill: Python, data cleaning, building study tools, statistics] and used it to [one line about a real project]. I would like to contribute to your lab, including on the unglamorous parts — data collection, cleaning, transcription, tooling. Would you have 15 minutes in the next two weeks to talk about whether there is anything I could help with? I have attached my CV. Thank you for your time, [Name] · [email] · [portfolio or GitHub link]

• Name the paper exactly. A generic "I am interested in your research" is the single clearest signal that you read nothing.
• Offer the boring work explicitly. Labs need data cleaning and transcription more than they need ideas from strangers.
• Ask for 15 minutes, not a position. A small ask is easy to say yes to.
• Attach a one-page CV and link something clickable.
• Follow up once after ten days, then move on. Non-reply is usually inbox volume, not judgement.
• Email five to eight people, not one. The reply rate is genuinely low and that is normal.

What the first task actually looks like

It is almost never the interesting part, and this is the point at which most undergraduates quietly disappear. Expect literature screening, data cleaning, transcription, annotating responses against a codebook, running participant sessions, or building a small internal tool nobody outside the lab will ever see.

Do it well and finish it on time. The entire progression from data-cleaner to co-author runs on that one behaviour, because a lab's scarcest resource is a person who reliably completes things without supervision. The students who ask for a research question in month one and vanish by month three are the norm; being the exception is not difficult, only consistent.

The vocabulary: IRB and mixed methods

IRB

An Institutional Review Board is the university committee that reviews research involving human participants before it starts. If your study involves people — surveys, interviews, classroom studies, usability sessions — you need approval, and running the study first is not recoverable: data collected without approval generally cannot be used or published.

In practice it means a written protocol describing who the participants are, what you will ask them, what data you collect, how it is stored, how it is anonymised, and what the consent form says. Approval takes weeks, so it sits at the front of the timeline. As an undergraduate you will typically complete a short human-subjects training course and be added to an existing protocol rather than writing your own.

Mixed methods

Mixed-methods research combines quantitative data — scores, timings, counts, survey scales you can run statistics on — with qualitative data — interviews, open responses, observation notes that you analyse by coding for themes.

The reason to combine them is that each covers the other's blind spot. Quantitative data tells you that something changed and how much; qualitative data tells you why, and often surfaces the explanation you would never have thought to measure. A typical design runs a measurable intervention, collects the numbers, then interviews a subset of participants and codes the transcripts for recurring themes that explain the numbers.

Publishing, and why it is disproportionately valuable

Undergraduate authorship is rare enough that it is memorable in almost any application pile. Beyond the signal, the process teaches things coursework does not: how to scope a claim so the data actually supports it, how to write for reviewers who will attack the weakest sentence, and how to respond to criticism without either collapsing or arguing.

Realistic routes in, roughly in order of accessibility: a poster at a conference or a departmental symposium, a workshop paper, a short paper or experience report, then a full paper as a middle author. Posters are not a consolation prize — they are the normal entry point, and they still involve peer review, a deadline, and defending the work in person.

A concrete example

My own path is not exceptional and that is the point. I joined Dr. Ethel Tshukudu's CS Education Research Lab at SJSU in August 2024 as an undergraduate researcher, starting on the ordinary work — study logistics, data handling, tooling. That became co-authorship on two papers at the SIGCSE Technical Symposium 2026: one on bilingual coding for inclusive CS learning (DOI 10.1145/3770761.3777339), and a poster on adaptive curriculum maps using graph-augmented retrieval-oriented LLMs for education.

The sequence was: be useful on small things, be reliable, stay long enough for a project to reach a submission deadline. There was no shortcut in it, and there did not need to be.

More on what I work on is on the about page. If the research is in CS education specifically, the bilingual coding guide covers one of these studies in detail.

Tools referenced in this guide

• About — the research, the projects, and what I am currently working on.
• Bilingual coding guide — a worked example of a mixed-methods CS education study.
• Resume rebuild — how to present research on a one-page CV.