PhD & Research Resources

Growing up, I didn’t really know what research was or what life in academia looked like. I’m still figuring it out, but I have a better picture now of what a PhD and day-to-day research involve. These are some articles and resources I’ve collected along the way.

Most come from computer science, machine learning, and robotics, with a few perspectives from other fields.

Links compiled by me; descriptions written by AI (GPT).

Updated November 13, 2026.

A few starting points

A broad overview: A Survival Guide to a PhD — Andrej Karpathy.
Choosing a direction: Research tastes — Jiasi Shen.
Getting unstuck: How to proceed when being stuck — Jiasi Shen.
Writing a paper: Tips for Writing Technical Papers — Jennifer Widom.
Keeping perspective: Counter-Advice for the PhD — Jean Yang.

Getting started

The illustrated guide to a Ph.D. — Matt Might. A short visual explanation of what it means to make a research contribution, and how a small advance fits into a much larger body of knowledge.
A Survival Guide to a PhD — Andrej Karpathy. A wide-ranging guide to advisors, choosing problems, writing papers, and becoming an independent researcher, grounded in a CS and machine learning PhD.
Nature of research vs. schoolwork — Jiasi Shen. On the transition from solving well-defined assignments to working out which questions to ask, with slower feedback and less obvious measures of progress.
Tips for junior PhD students — Lulu Shang. Practical notes on coding, organizing projects, reading, writing, and preparing for meetings, including examples from computational research.
3 qualities of successful Ph.D. students: Perseverance, tenacity and cogency — Matt Might. An opinionated account of persistence and communicating ideas clearly. Its assumptions about academic careers reflect the context in which it was written.

Choosing problems and developing research taste

How To Choose a Good Scientific Problem — Uri Alon, Molecular Cell, 2009. A framework for weighing feasibility and interest, choosing projects that fit a researcher’s stage, and allowing a question to change as research unfolds. Also explores personal motivation and the role of mentorship. PDF copy.
Research tastes — Jiasi Shen. Ways to reconsider an imperfect starting point: simplify an ambitious problem, use a small example as a prototype, or look for questions that emerge when a theoretical idea meets practice.
How to dig a well with water / Strategy for making discoveries — Jiasi Shen. Reflections on exploring possible research directions and choosing where to invest effort. In Chinese.
You and Your Research — Richard Hamming. A talk about choosing consequential problems and building a research life around them. Pairs well with Sophia Sun’s essay in the next section, which offers a different perspective on ambition.
How to Do Great Work — Paul Graham, 2023. A broad essay on following curiosity, developing expertise and taste, noticing overlooked questions, and sustaining ambitious work. Applies across scientific, technical, and creative fields.
How to win a best paper award — Nicholas Carlini. An opinionated discussion of problem selection, collaborators, technical work, and writing a compelling paper. The emphasis is on the research process behind meaningful work.
Advice for young researchers — Julian Togelius, November 2026. On developing research taste and a distinctive set of interests as AI tools take on more of the execution. Introduces “understanding debt”: the gap that grows when delegated work moves beyond what its researcher can explain or evaluate.
The Core of Research — Kuan-Hao Huang. Argues for investigating why methods work, which design choices matter, and where findings generalize, beyond improving a benchmark score.

Getting unstuck

How to proceed when being stuck — Jiasi Shen. A case for starting with a small, related problem when the original goal feels too difficult to approach. In Chinese.
How I transformed into a senior PhD student — Jiasi Shen. On recognizing less visible progress, revisiting fundamentals, and learning to reshape a problem as understanding develops.
How to graduate your PhD when you have no hope — Sophia Sun. A reflection on the pressure to do important work, paired with Richard Feynman’s letter about the value of smaller problems that can actually be solved.
Counter-Advice for the PhD — Jean Yang. Questions familiar advice about passion, perfect advisor fit, innate talent, and constant work. Especially useful when a supposedly universal rule does not fit a particular situation.

Advisors, collaborators, and everyday work

This is Your PhD — Kuan-Hao Huang. On developing independence, making decisions about research, seeking collaborators, and learning how to use an advisor’s support effectively.
Expectation setting — Eugene Vinitsky. An example of making early PhD expectations explicit: technical foundations, research practice, meetings, and increasing independence. Written for a particular lab and field; useful as a starting point for an advisor conversation.
A Guide to Cold Emailing — Eugene Vinitsky. Examples of approaching researchers with specific questions, evidence of engagement with their work, and a clear request.
The Missing Semester of Your CS Education — MIT. Lessons on the command line, editors, version control, and other computing tools that research often assumes students already know.
In the Era of AI, Do We Still Need to Learn How to Code? — Kuan-Hao Huang. An argument for continuing to develop programming logic, problem decomposition, and system design skills when using coding agents.

Reading, writing, and presenting

Ways to Engage Deeply with a Paper — Eugene Vinitsky. A paper-reading template and exercises for testing comprehension: explain an idea, identify an unresolved question, or propose an experiment that would strengthen the evidence.
How to read research papers, in 5 minutes — Mason Wang. A guide to moving from the main idea to the evidence, then deciding which technical details deserve a closer read.
How to Read a Paper — Kuan-Hao Huang. A selective reading process: anticipate the approach, ask what evidence would support the claims, and decide which parts warrant deeper study.
Tips for Writing Technical Papers — Jennifer Widom. Concrete guidance on titles, abstracts, introductions, related work, experiments, and presentation. A useful reference while outlining or revising a technical paper.
How to Write a Paper — Kuan-Hao Huang. On guiding readers from the central idea to the technical details, with clear connections between challenges, design choices, contributions, and supporting evidence.
Presenting Your Research — Huy Ha. A slide deck on preparing a research talk, thinking about its audience, and improving visual communication, with robotics examples. PDF.
Presentation Skills — Kuan-Hao Huang. Exercises for explaining research at different lengths and to different audiences, then checking what listeners actually understood.
Rebuttal advice — Frédo Durand, with additional notes from Ramesh Raskar. Advice on organizing a response, making answers easy to find, and addressing criticism constructively. Written in a SIGGRAPH context; submission rules come from the relevant venue.
How to Write a Rebuttal — Kuan-Hao Huang. Advice for AI conference rebuttals: prioritize concerns that affect the decision, write for the area chair as well as reviewers, and connect responses to the paper’s central contribution.

PhD life and longer reflections

How many papers do I need to publish during my PhD? — Anirudha Majumdar, 2024. On balancing curiosity-driven research with career goals and treating publication counts as an imperfect proxy. The numerical examples reflect his robotics advising context and the job market at the time.
Why junior PhD students are miserable — Jiasi Shen. On uncertain progress, changing expectations, and the social isolation that can accompany the move away from coursework.
PhD: a retrospective analysis — Eugene Vinitsky. A candid account of overcommitting to projects, isolation, and the costs of organizing too much of life around paper output.
Lessons from my PhD — Austin Z. Henley. Retrospective lessons from a computer science PhD.
Struggling During Your PhD — Michael Milford. A video and podcast discussion of difficulties that can arise during a long research project, including adjusting to its scale.
Should We Rethink The Bar For Accepting A Paper? — Kuan-Hao Huang. A short opinion piece on publication speed, meaningful contributions, and whether papers help other researchers understand a problem and build on the work.

Robotics

Rethinking Robotics Research in the AI-Native Age — Toru Lin, October 2026. Reflections on choosing research problems that remain meaningful as methods change, maintaining the understanding needed to evaluate AI-assisted work, and using the PhD to develop judgment and independence. Draws on a personal path into robotics and the pressure to keep up with a fast-moving field.
What a PhD in Robotics is Really Like — Michael Milford. An overview of research, uncertainty, creativity, and the range of skills developed during a robotics PhD. Written for prospective students in his group and the Australian PhD system.

For a robotics presentation example, see Huy Ha’s slides above.

Before the PhD: applications and choosing a program

Choosing Between a PhD and Industry for New Computer Science Graduates — Shreya Shankar. A comparison of graduate research and industry, written from the perspective of a new CS graduate interested in machine learning.
Applying to Ph.D. Programs in Computer Science — Mor Harchol-Balter. An introduction to what a CS PhD involves and how the admissions process works. PDF; last updated in 2014.
Machine Learning PhD Applications — Everything You Need to Know — Tim Dettmers. A detailed account of preparing applications and interpreting application materials. Published in 2018.
How to Choose Your Grad School — Tim Dettmers. Considerations for choosing among graduate programs after receiving offers. Published in 2022.
What Advisors Care About Most — Kuan-Hao Huang. One advisor’s perspective on initiative, research experience, critical thinking, research direction, and communication in PhD applications.
How to Position Yourself — Kuan-Hao Huang. Suggests studying potential labs and the backgrounds of previously admitted students to understand how expectations differ among advisors.
Cold Email — Kuan-Hao Huang. Advice for contacting prospective advisors with specific research connections, concrete observations about their papers, and a clear account of relevant experience.
Red Flags in CV — Kuan-Hao Huang. On representing publication status, conference tracks, and author order accurately in an academic CV.

These essays provide context; current requirements and deadlines belong on each program’s own admissions page.

More collections

PhD Survival and PhD Application series — Kuan-Hao Huang. The full index of his posts on research, reading, writing, presentations, rebuttals, applications, and related reflections.
Awesome PhD Advice — Paul Pu Liang. A broad collection for prospective and current PhD students.
Resources for Students & Scholars — Frédo Durand. Resources on research, writing, talks, reviewing, and academic careers.
List of PhD reflections — Stephen Tu. Accounts of graduate school from different CS researchers, organized by date.
Chicken soup for the CS PhD student — Sophia Sun. Readings about research and the PhD experience.
Academic and research resources — Leena Mathur. Links covering research, applications, fellowships, and other aspects of academic life.
EconGradAdvice — Chris Roth and David Schindler. A collection from economics, including general graduate-school advice and their Advice for Ph.D. Students resource.