from Guide to the PhD on Dec 11, 2022

How PhD admissions committees assess applications

PhD admissions are notoriously selective: Quite simply, there are many more qualified candidates than there are advisers and funded slots. So your goal is not just to meet qualifications, it's to make your future adviser realize you're the opportunity of a lifetime.

In the admissions process, PhD students are asked to help review and filter candidates in the first pass. This post covers what we look for and what you can do to hit those criteria.

The tips here are mostly tailored for AI/ML PhD admissions at UC Berkeley. Other admissions processes may be structured differently — either for different fields or for different schools. The tips should generally apply broadly, and I'll do my best to identify parts of the process I know are specific to our program.

Step #1: Effective recommendations.

Our first and foremost criteria is a strong recommendation. In particular, there are two parts to this assessment:

Both of these factors are under your influence:

Your takeaway: Put effort into research to earn a strong recommendation from your letter writer. Pick relevant recommenders.

Step #2: Research capability

Our second priority is to assess the quality of research. There are two places where we can find materials and assess this:

  1. Resume: We look in the resume for itemized lists of publications from the candidate. There are extra points for any of the following pieces we may find: (a) A known conference, (b) a first author position and (c) conference awards. We take note of workshop submissions as well, but conference submissions are the gold standard. Regardless of the submission type, any sort of submission is easiest to take note of. A masters program raises the bar you need to meet, for admission. 2
  2. Personal statement: We skim the personal statement looking for publications as well. In the absence of publications, we look for evidence of research experience and do our best to assess research readiness. Unfortunately, it's hard to assess research readiness without publications. For an explanation of how we attempt to judge research readiness, in addition to tips to exemplify these characteristics, see How to write your personal statement, for PhD admissions.

Your takeaway: Clearly itemize your publications in your resume, and highlight them in your personal statement using the linked post's tips.

Step #3: Academic strength

In general, a high GPA is passable and a low GPA is a red flag. Unfortunately, GPA alone can only hurt. However, if the high GPA is coupled with the following, your GPA can be a plus:

  1. Challenging coursework, such as graduate-level courses. We look for computer science courses of course, but we also look for course titles in relevant fields such as optimization, statistics, mathematics, etc.
  2. A known university. To the best of our ability, we normalize GPA expectations per university with rough priors: For example, CalTech deflates GPAs and Harvard inflates them.

There is a caveat here: Graduate coursework with A grades are very good signs. If you're already living the life of a graduate student, needless to say you're fit for a graduate program. However, graduate coursework with grades other than an A are a bad sign, even worse than not taking graduate courses at all. Your takeaway should be the following: If you take graduate coursework, make certain to achieve an A.

Specific to our program, we look for the university's analogous "Introduction to Machine Learning" course and double-check the student earned an A in that course.

Graduate coursework with grades other than an A are a bad sign, even worse than not taking graduate courses at all.

Your takeaway: Challenging courses and a high GPA are a plus, but don't take graduate courses unless you're certain you'll earn an A.

Then, everything else.

There are several other elements of the application that may play a decent role in the initial pass:

Your takeaway: Contribute to diversity rather than find some esoteric, inauthentic way to make your identity diverse. Write clearly.

Example profiles

As a quick summary, here are the criteria we looked for in candidates, roughly ordered from highest to lowest priority:

Here are example, completely made-up profiles of candidates and how we would rank them on a score from 1 to 5, with 1 being the highest.

  1. Top 2% - Candidate already living the life of a Berkeley PhD student.

    1. Recommendation states "would take student as my own PhD student".
    2. Has co-authored several papers at known venues.
    3. High GPA with challenging coursework.
  2. Next 8% - Candidate is strong and understands research in the area.

    1. Recommendation ranks student highly, in top 10% of undergraduates they've advised.
    2. Has co-authored a paper at a known venue.
    3. Decent GPA with relevant coursework.
  3. Next 30% - Candidate passes the threshold, but nothing stands out.

    1. Recommendation generally praises the student, ranks student in top 30% of undergraduates they've advised
    2. Has co-authored a paper, possibly a workshop paper or one at an unknown venue. Or, hasn't co-authored a paper but has relevant experience.
    3. Graduating from a known university, but no remarkable coursework or GPA.
  4. Next 30%

    1. Recommendation praises the student but does not rank the student.
    2. Has research experience but no published papers. No relevant experience as substitute.
    3. Graduating from an unknown university with a mediocre GPA.
  5. Last 30% - Not a fit for graduate school.

    1. Recommendation does not praise the student. This is common when the recommendation is written for a student-teacher relationship. "Student X was in my class…".
    2. No research experience.
    3. Major other than computer science, with no relevant coursework. Low GPA.

Note that for various reasons, these profiles may receive higher or lower rankings. For example, unclear communication impairing readability would bump down a rating. Conversely, extremely relevant experience may bump up a rating.

That's it for the instructions we're given, and how we review applicants in the first pass. Use the takeaways above to improve your application before submitting. Best of luck!


  1. The emphasis on conferences is specific to the AI/ML field, where research in the field outpaces the journal review process. As a result, conference publications are the gold standard. 

  2. A masters program is almost always detrimental to your chances at applying for a PhD program. This is because with extra time, you should have conducted higher quality research. In fact, we're asked to only note masters candidates with world-class research. A 5th-years masters is somewhat excluded from this. 

Got a question? Ask me on Twitter, at @lvinwan. Want more tips? Drop your email below, and I'll keep you in the loop.

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