research skills starter pack

Created on April 27, 2026

2026   ·   research   writing   phd   methods   statistics   mentorship   ·   guides

Getting started in research is not just about joining a lab.

It is also about slowly building the invisible skills that make research work feel less mysterious: how to write, how to read, how to scope a project, how to run a study, how to interpret evidence, and how to stay alive through long, uncertain projects.

This is a small starter pack I point students to when they ask, “What should I learn on my own before, during, or after joining a research project?”

It is not a complete syllabus. It is a map.

It is also an ongoing thing. I will keep updating this post when I run into more resources that feel genuinely helpful.

1. Learn to read research papers

Reading papers is its own skill. You are not supposed to understand every paper perfectly on the first pass, and you are not supposed to read every paper with the same level of attention.

Prof. Philip Guo recommended two resources to me that are especially helpful here.

The first is Srinivasan Keshav’s How to Read a Paper. Keshav describes a three-pass method: first get the shape of the paper, then understand the content, then go deep enough to reconstruct and critique it. This is useful because it gives you permission to read strategically instead of getting stuck on page one.

The second is Jacob O. Wobbrock’s Catchy Titles Are Good: But Avoid Being Cute, which is a very useful guide to how SIGCHI-style HCI papers are typically structured. Even though it is written as a paper-writing guide, it is also great for reading because it teaches you what each section is trying to do: abstract, introduction, related work, method, results, discussion, limitations, and contribution.

When you read a paper, try to answer:

  • What kind of paper is this?
  • What problem or opportunity motivates it?
  • What is the main contribution?
  • What evidence supports the contribution?
  • What assumptions does the paper make?
  • What would I need to believe for this paper to be convincing?
  • What would I do differently if I were designing the study or system?

Reading well is not passive. You are reconstructing the research.

2. Learn to write research clearly

Research writing is not decoration after the “real work” is done. Writing is where you discover what your contribution actually is, what evidence you still need, and what your reader is likely to misunderstand.

For technical writing, start with Tzu-Mao Li’s writing tips. I like this page because it is short, direct, and very practical. A few lessons I especially want students to absorb:

  • Know who you are writing for.
  • Start early, ideally when the research idea is still forming.
  • Explain the “why” before the “what” and “how.”
  • Put the important points before the implementation details.
  • Make figures early and use them to shape the story.
  • Treat collaborators’ confusion as useful evidence about where readers will also get confused.
  • Be generous and careful when writing about prior work.

Tzu-Mao Li also recommended two writing resources that I would read after his page. The first is Fredo Durand’s Notes on writing. Durand emphasizes something that sounds obvious but is surprisingly easy to forget: your contribution only matters if people can understand it. His notes are especially useful for learning how to build a paper around a clear story, hierarchy of ideas, motivation, overview, figures, and results that support your claims.

Then read Bill Freeman’s How to write a good CVPR submission. Even if you do not work in computer vision, it is useful because it explains the reviewer’s situation very honestly. Reviewers are busy. Area chairs are often looking for reasons to reject borderline papers. Your job is not to hope they figure out your point. Your job is to make the problem, contribution, evidence, limitations, and relationship to prior work easy to see.

My practical takeaway from Tzu-Mao Li’s advice, the Durand/Freeman resources he recommended, and my own writing habits:

  • Draft the outline before drafting polished paragraphs.
  • Make the central figure earlier than feels comfortable.
  • Write the first paragraph as if a tired reviewer will only give you 30 seconds.
  • For every section, ask: what does the reader need to believe by the end of this?
  • For every claim, ask: what evidence makes this believable?
  • For every related-work paragraph, ask: am I being accurate, fair, and useful?
  • Rewrite more than you think you need to.

Good writing is not about sounding fancy. It is about making the reader’s job easier.

3. Learn to review papers generously

Reviewing is another way to learn research taste. A good review does not just find flaws. It identifies what is valuable, what is missing, what is overstated, and what would help the work become stronger.

Ken Hinckley recommended his own essay, So You’re a Program Committee Member Now: On Excellence in Reviews and Meta-Reviews and Championing Submitted Work That Has Merit. It was written for MobileHCI 2015 program committee members, but it is useful well before you are actually on a program committee.

The big lesson I take from it is that reviewing is a responsibility to the field, not just an exercise in criticism. Look for the strongest version of a paper’s contribution. Be fair about weaknesses. Separate correctable presentation problems from deeper research problems. Write in a way that helps authors improve, especially when you recommend rejection.

When practicing reviews, ask:

  • What is the paper trying to contribute?
  • What is genuinely valuable here?
  • What evidence is strong?
  • What evidence is weak or missing?
  • What claims are too broad for the evidence?
  • What are the most important changes the authors could make?
  • Am I being fair to work that is outside my exact taste or method comfort zone?

Writing reviews is also a sneaky way to become a better author. You start to feel what makes a paper easy or hard to evaluate.

4. Learn what Ph.D. life can feel like

If you are considering a Ph.D., you should read about the lived experience, not just the application process or the highlight reel.

Philip Guo’s The Ph.D. Grind is valuable because it gives a concrete, personal account of a computer science Ph.D. journey: uncertainty, advisor fit, failed directions, rebuilding momentum, publishing, graduating, and making sense of the whole experience afterward.

Do not read it as universal truth. Every department, advisor, funding structure, research area, and person is different. Read it as one detailed case study of how research can feel from the inside.

The main reason I recommend it is that it makes the hidden parts visible. Research often looks clean after it is published. During the process, it can feel like false starts, ambiguous feedback, lonely debugging, changing goals, and small wins that only make sense months later. Knowing that ahead of time can help you avoid over-interpreting normal struggle as personal failure.

Useful questions to ask while reading:

  • What kinds of uncertainty does this person have to tolerate?
  • What changes when a project starts to become their own?
  • How do mentors, collaborators, and institutions shape the experience?
  • What parts of this life sound energizing to me?
  • What parts sound costly, and am I honest about that cost?

A Ph.D. can be meaningful and joyful. It can also be hard in ways that are not obvious from the outside. Both can be true.

5. Learn empirical methods and statistics

For HCI, design, psychology, education, and human-centered AI, methods matter a lot. If you want to study people, you need to learn how evidence gets made.

This is where statistics is not just a class requirement. It is part of your research taste. It helps you notice weak comparisons, confounds, underpowered claims, measurement problems, and conclusions that are stronger than the study can support.

Prof. Scott Klemmer has recommended reading David W. Martin’s Doing Psychology Experiments for learning experimental thinking.

I would use this book to learn the mindset, not only the formulas:

  • What is the actual research question?
  • What is being manipulated?
  • What is being measured?
  • What are the dependent and independent variables?
  • What are the threats to validity?
  • What would a better control condition look like?
  • What can this study conclude, and what can it not conclude?

For students entering HCI or human-centered AI, I would pair methods reading with practice. Take a paper you like and reverse-engineer the study:

  • Write the research question in one sentence.
  • Identify the claim the study is trying to support.
  • Sketch the study design.
  • List the variables and measures.
  • Write down possible confounds.
  • Ask what result would have changed your mind.
  • Check whether the paper’s conclusion matches the evidence.

This exercise is slow at first, but it builds the kind of judgment that makes you a much stronger collaborator.

6. A note on credit

This list credits both the people who recommended resources to me and the people who wrote those resources:

Research is a craft. The nice thing about craft is that you can practice it before anyone gives you permission.

Last updated: April 27, 2026.