Why and how to email faculty prior to applying to graduate school

by Steve Luck and Lisa Oakes

[Note: Our experience is in Psychology and Neuroscience, but this probably applies to most other disciplines.]

It is now the season for students in the U.S. to begin the stressful, arduous, and sometimes expensive process of applying to PhD programs. One common piece of advice (that we give our own students) is to send emails to faculty at the institutions where you plan to apply. In this blog post, we explain why this is a good thing to do and how to do it. Some students find it very stressful to send these emails, and we hope that the “how to do it” section will make it less stressful. You don’t have to email the faculty, but it can be extremely helpful, and we strongly recommend that you do it.

In many programs (especially in Psychology), individual faculty play a huge role in determining which students are accepted into the PhD program. In these programs, students are essentially accepted into the lab of a specific faculty member, and the faculty are looking for students who have the knowledge, skills, and interests to succeed in their labs. This is often called the “apprenticeship model.”

In other programs (including most Neuroscience programs), admissions decisions are made by a committee, and individual faculty mentors play less of a role. Moreover, in most Neuroscience programs, grad students do lab rotations in the first year and do not commit to a specific lab until the second year. We’ll call this the “committee model.”

Why you should email the faculty

Although many students are accepted into graduate programs without emailing faculty prior to submitting applications to programs, there are many good reasons to do so. This can be especially useful for programs that use the apprenticeship model. First, you can find out whether they are actually planning to take new students. You don't want to waste money applying to a given program only to find out that the one faculty member of interest isn’t taking students this year (or is about to move to another university, take a job in industry, etc.). Information about this may be on the program’s web site or the faculty member’s web site, but web sites are often out of date, so it’s worth double-checking with an email.

Second, and perhaps most important, this email will get you “on the radar” of the faculty. Most PhD programs get hundreds of applicants, and faculty are much more likely to take a close look at your application if you’ve contacted them in advance.

Third, you might get other kinds of useful information. For example, a professor might write back saying something like “I’m not taking any new students, but we’ve just hired a new faculty member in the same area, and you might consider working with her.” Or, the professor might say something like “When you apply, make sure that you check the XXX box, which will make you eligible for a fellowship that is specifically for people from your background.” Or, if the professor accepts students through multiple programs (e.g., Psychology and Neuroscience), you might get information about which one to apply to or whether to apply to both programs. Both of us take students from multiple different graduate programs, and we often provide advice about which program is best for a given student (which can impact the likelihood of being accepted as well as the kinds of experiences the students will get).

If admissions are being done by a committee, an email can still be important. For example, decisions may take into account whether the most likely mentor(s) are interested in the student. Or you might find out that none of the faculty of interest in a given program are currently taking students for lab rotations. This could impact the likelihood that you get into a program, and it might make you less interested in a program if you know in advance that you won’t have the opportunity to do a rotation in that person’s lab. In addition, faculty members can (and will) contact the committee before decisions are made to ask them to take a close look at a particular student’s application, pointing out things that might not otherwise be obvious to them. Finally, the faculty are often involved in the interview process, and having already established a relationship will make the interview less intimidating and more productive.

How to email the faculty

Now that you are (we hope) convinced that you should contact the faculty, you have to muster up the courage to actually send that message, and you need to make sure that your message is effective. To address both of these issues, we’ll provide give you some general advice and then provide an email template that you can use as a starting point.

First, the general advice. Faculty are very busy, and they get a lot of emails that aren’t worth reading. Each of us gets many emails each year from prospective students, and we find that the right e-mail can pique our interest and make us look carefully at a student’s materials. On the other hand, generic e-mails that simply say “Are you accepting students” are likely to be ignored.

You need to make sure that your email is brief but has some key information to get their interest. We recommend a subject heading such as “Inquiry from potential graduate applicant.” For the main body of the email, your goals are to (a) introduce yourself, (b) inquire about whether they are taking students, (c) make it clear why you are interested in that particular faculty member, and (d) get any advice they might offer. Here’s an example:

Dear Dr. XXX,

I’m in my final year as a Cognitive Science major at XXXX, where I have been working in the lab of Dr. XXX XXX. My research has focused on attention and working memory using psychophysical and electrophysiological methods (see attached CV). I’m planning to apply to PhD programs this Fall, and I’m very interested in the possibility of working in your lab at UC Davis. I read your recent paper on XXX, and I found your approach to be very exciting.

I was hoping you might tell me whether you are planning to take new students in your lab in Fall 2019 [or: …whether you are planning to take rotation students in your lab…]. I’d also be interested in any other information or advice you have.

[Possibly add a few more lines here about your background and interests.]


It’s useful to include some details about yourself—where you got or are getting your degree, what kind of research experience you’ve had, and/or what you’ve been doing since you graduated. Even if your research experience isn’t directly related to what you want to do, it’s a good idea to include at least a phrase about what you’ve been doing (e.g., “I did internships in a neuroscience lab working with rodents and a social psychology lab administering questionnaires”). But if this experience is very different from the intended mentor’s research, you need to make it clear that you’re planning to move in a different direction for your graduate work. We also pay more attention to emails from students who seem to know something about us. Mention a paper or a research project you saw on the professor’s website. You don’t need details; just show that you’ve done your homework and are truly interested in that individual.

It’s a good idea to attach a CV, even though there won’t be a lot on it. That’s a good place to provide some more details about your skills and experience. Also, if you have an excellent GPA or outstanding GRE scores, you can put them on your CV (although these would not go on a CV for most other purposes). Your goal is to stand out from the crowd, so you should include anything relevant that will be impressive (e.g., “3 years of intensive Python programming experience” but not “Familiarity with Excel and PowerPoint”). Don’t put posters, papers in progress, etc., in a section labeled “Publications” – that section should be reserved for papers/chapters that have actually been accepted for publication. You should include these things, but use more precise labels like “Manuscripts in Progress”, “Conference Presentations”, etc.

If you’re a member of an underrepresented/disadvantaged group, you can make this clear in your email or CV if you are comfortable doing so (although this may depend on your field). We recognize that this can sometimes be a sensitive issue, but there are often special funding opportunities for students with particular underrepresented identities, and most faculty are especially eager to recruit students from underrepresented/disadvantaged groups. Usually, this information can be provided indirectly (e.g., by listing scholarships you’ve received or programs that you’ve participated in, such as the McNair Scholars), but it can be helpful if you make this information explicit to your prospective faculty mentor and program. However, this can backfire if it’s not done just right, so we strongly recommend that you ask your current faculty mentor for advice about the best way to do this given your field and your specific situation.

No matter what your situation, we recommend having your faculty mentor(s) take a look at a draft of the email and your CV before you send them. Grad students and postdocs can also be helpful, but they may not really know what is appropriate given that they haven’t been on the receiving end of these emails.

Most importantly, don’t be afraid to send the email. The worst thing that will happen is that the faculty member doesn’t read it and doesn’t remember that you ever sent it. The best thing that can happen is that the e-mail leads to a conversation that helps you get accepted into the program of your dreams.

What to expect

Many faculty will simply not reply. In this case, no information is no information. There are many faculty who simply don’t read this kind of e-mail, and a “no reply” might mean you contacted one of those faculty. Of course, it’s also possible that they’re not interested in taking grad students and didn’t want to spend time replying. Or, it could mean that the message was caught by a spam filter, that they received 150 emails that day, etc. So, if you really want to work with that person, you may still want to apply.

You may get a brief response that says something like “Yes, I’m taking students, and I encourage you to apply” or “I’m always looking for qualified students.” This indicates that the faculty member will likely look at applications, and you don’t need to follow-up.

If you’re lucky, you may get a more detailed response that will lead to a series of email exchanges and perhaps an invitation to chat (usually on Skype or something similar). This will be more likely if you say something about what you’ve done and why you are interested in this lab. We know it may be stressful to actually talk to the faculty member, but isn’t that what you’re hoping to do in graduate school? Now is the time to get over that hurdle.

You may get a response like “I’m not taking new students this year” or “I probably won’t take new students this year” or “I’m not currently taking rotation students” (which is code for “don’t bother applying to work with me”). Or you might get something like “Given your background and interests, I don’t think you’d be a good fit for my lab.” Now you know not to waste your money applying to work with that person, so you’ve learned something valuable.

We’ve never heard of a student receiving a rude or unpleasant response. It may happen, but it would be extremely rare. So, you really don’t have much to lose by emailing faculty, and you have a lot to gain. It’s not 100% necessary, but it will likely increase your odds of getting into one of the programs you most want to attend.

Some thoughts about the hypercompetitive academic job market

Many young academics are (justifiably) stressed out about their career prospects, ranging from the question of whether they will be able to get a tenure-track position to whether they will be able to publish in top-tier journals, get grants, get tenure, and do all of this without going insane.  Life in academia has been challenging for a long time, but the level of competition seems to be getting out of control. The goal of this piece is to discuss some ideas from population biology that might help explain the current state of hypercompetition and perhaps shed light on what kinds of changes might be helpful (or unhelpful).

Here’s the problem in a nutshell: if we want to provide a tenure-track faculty position for every new PhD who wants one, the number of available positions would need to increase exponentially with no limit.  This is shown in the graph below.  


If we assume that a typical faculty member has a couple grad students at any given time, and most of them want jobs in academia, this faculty member will have a student who graduates and wants a faculty position approximately every three years.  As a result, we would need to create a new faculty position approximately every three years just to keep up with the students from a single current faculty member.  As if this wasn’t bad enough, these recent PhDs will then get their own grad students, who will also need faculty positions. This leads to an exponential growth in the number of positions needed to fill the demand.  

For example, if we have 1000 positions in a given field in the year 2018, we will need another 1000 positions in that field by the year 2021 to accommodate the new students who have received their PhDs by that time, leading to a total of 2000 positions to accommodate the demand that year.  The faculty in these 2000 positions will have students who will need another 2000 positions by the year 2024, leading to a total need for 4000 positions that year.  

If the number of positions kept increasing over time to fill the demand, we would need over a million positions by the year 2048!  This doesn’t account for retirements, etc., but those factors have a very small effect (unless we start forcing faculty to retire when they reach the age of 40 or some such thing).  There are various other assumptions here (e.g., a new PhD every 3 years), but virtually any realistic set of parameters will lead to an exponential or nearly-exponential growth function.

This is just like the exponential increase you might see in the size of a population of organisms, with a rate factor (r) that describes the rate of reproduction.  However, an exponential increase can happen only if reproduction is not capped by resource limitations.  Resource limitations lead to a maximum population size, which population biologists call K (for the “carrying capacity” of the environment).  When the exponential growth with rate r is combined with carrying capacity K, you get a logistic function.  This is shown in the picture below (from Khan Academy), which illustrates the growth rate of a population of organisms with no limit on the population size (the exponential function on the left) and with a limit at K (the logistic function on the right).

Population Growth.png

At early time points, the two functions are very similar: K doesn’t have much impact on the rate of growth in the logistic function early in time, and growth is mainly limited by r (the replication rate).  This is called “r-limited” growth.  However, later in time, the resource limitations start impacting the rate of growth in the logistic function, and the population size asymptotes at K.  This is called “K-limited” growth.  It’s much nicer to live in a period of r-limited growth, when there are plenty of resources.  When growth is K-limited, this means that the organisms in the population have so few resources that they die before they can reproduce, or are so hungry they can’t reproduce, or their offspring are so hungry they can’t survive, etc.  Not a very pleasant life.

In academia, r-limited growth means that jobs are plentiful, and the main limitations on growth are the number of students per lab and the rate at which they complete their degrees.  By contrast,  K-limited growth basically means that a faculty member needs to die or retire before a new PhD can get a position, and only a small fraction of new PhDs will ever get tenure-track jobs and start producing their own students.  This also means that the competition for tenure-track jobs and research grants will be fierce.  Sound familiar?

In the context of academia, K represents the maximum number of faculty positions that can be supported by the society.  The maximum number of faculty positions might increase gradually over time, as the overall population size increases or as a society becomes wealthier.  However, there is no way we can sustain an exponential growth forever (especially if that means we need over a million positions by 2048 in a field that has only a thousand positions in 2018).  

I think it’s pretty clear that we’re now in a K-limited period, where the number of positions is increasing far too slowly to keep up with the demand for positions from people getting PhDs.  When I was on the job market in the early 1990s, there were already more people with PhDs than available faculty positions.  However, the problem of an oversupply of PhDs was partially masked by an increase in the availability of postdoc positions.  Also, it was becoming more common for faculty at “second-tier” universities to conduct and publish research, so the actual number of positions that combined research and teaching was increasing.  But this balloon has stretched about as far as it can, and highly qualified young scholars are now having trouble getting the kind of position they are seeking (and we’re seeing 200+ applicants for a single position in our department).

In addition to a limited number of tenure-track faculty positions, we have a limited amount of grant money.  In some departments and subfields, getting a major grant is required for getting tenure.  Even if this isn’t a formal requirement, the resources provided by a grant (e.g., funding for grad students and postdocs) may be essential for an assistant professor to be sufficiently productive to receive tenure.  But an increase in grant funding without a commensurate increase in permanent positions can actually make things worse rather than better.  We saw that when the NIH budget was doubled between 1994 and 2003.  This led to an increase in funding for grad students and postdocs (leading to the balloon I mentioned earlier).  However, without an increase in the number of tenure-track faculty positions, there was nowhere for these people to go when they finished their training.  Their CVs were more impressive, but this just increased the expectations of search committees.  Also, a lot of the increased NIH funding was absorbed by senior faculty (like me) who now had 2, 3, or even 4 grants instead of just 1.  As usual, the rich got richer.

One might argue that competition is good, because it means that only the very best people get tenure-track positions and grants.  And I would be the first person to agree that competition can help inspire people to be as creative and productive as possible.  However, the current state of hypercompetition clearly has a dark side.  Some people write tons of grants, often with little thought, in the hopes of getting lucky.  This can lead to poorly-conceived projects, and it can leave people with little time to think about and actually conduct high-quality research.  And it can lead to p-hacking and other questionable research practices, or even outright fraud.  I think we’re way beyond the point at which the level of competition is beneficial.

Now let’s talk about solutions.  Should we increase the number of tenure-track faculty positions at research universities? I would argue that any solution of this nature is doomed to failure in the long run.  Increasing the number of position is an increase in K, and this just postpones the point at which the job market becomes saturated.  It would certainly help the people who are seeking a position now, but the problem will come back eventually. There just isn’t a way for the number of positions to increase exponentially forever.

We could also try to limit the number of students we accept into PhD programs.  This would be equivalent to decreasing r, the rate of “reproduction.”  However, for this to fully solve the problem, we would need the “birth rate” (number of new PhDs per year in a field) to equal the “death rate” (the number of retirements per year in the field).  Here’s another way to look at it: if the number of positions in a field remains constant, a given faculty member can expect to place only a single student in a tenure-track position over the course of the faculty member’s entire career.  Is it realistic to restrict the number of PhD students so that faculty can have only one student per career?  Or even one per decade?  Probably not.

I have only one realistic idea for a solution, which is to create more good positions for PhDs that don’t involve “reproduction” (i.e., training PhD students).  For example, if there were good positions outside of academia for a large number of PhDs, this would reduce the demand for tenure-track positions and decrease r, the rate of reproduction (assuming that there would be fewer people “spawning” new students as a result).  Tenure-track positions at teaching-oriented institutions have the same effect (as long as these institutions don’t decide to start granting PhDs).   I don’t think it’s realistic to increase the number of these teaching-oriented positions (except insofar as they increase with overall changes in population size).  However, in many areas of the mind and brain sciences, it appears that the availability of industry positions could increase substantially.  Indeed, we are already seeing many of our students and postdocs take jobs at places like Google and Netflix.

Many faculty in research-oriented universities think that success in graduate school means getting a tenure-track faculty position in a research-oriented university.  However, if I’m right that the current K-limited growth curve—and the associated hypercompetition—is a major problem, then we should place a much higher value on industry and teaching positions.  The availability of these positions will mean that we can continue to have lots of bright graduate students in our labs without dooming them to work as Uber drivers after they get their PhDs.  And teaching positions are intrinsically valuable: A great teacher can have a tremendous positive impact on thousands of students over the course of a career.

This doesn’t mean that we should focus our students’ training on teaching skills and data science skills, especially when these are not our own areas of expertise.  Excellent research training will be important for both industry positions and teaching-oriented faculty positions.  But we should encourage our students to think about getting some significant training in teaching and/or data science, which will be important even if they take positions in research-oriented universities.  And we should encourage some of our students to take industry internships and get teaching experience.  But mostly we should avoid sending the implicit or explicit message to our students that they are failures if they don’t pursue tenure-track research university positions.  If, as a field, we increase the number of our PhDs who take positions outside of research universities, this will make life better for everyone

An old-school approach to science: "You've got to get yourself a phenomenon"

Given all the questions that have been raised about the reproducibility of scientific findings and the appropriateness of various statistical approaches, it would be easy to get the idea that science is impossible and we haven't learned a single thing about the mind and brain. But that's simply preposterous.  We've learned an amazing amount over the years.

In a previous blog post (and follow-up), I mentioned my graduate mentor's approach, which emphasized self-replication. In this post, I go back to my intellectual grandfather, Bob Galambos, whose discoveries you learned about as a child even if you didn't learn his name. I hope you find his advice useful. It's impractical in some areas of science, but it's what a lot of cognitive psychologists have done for decades and still do today (even though you can't easily tell from their journal articles).  I previously wrote about this in the second edition of An Introduction to the Event-Related Potential Technique, and the following is an excerpt. I am "recycling" this previous text because the relevance of this story goes way beyond ERP research.


My graduate school mentor was Steve Hillyard, who inherited his lab from his own graduate school mentor, Bob Galambos (shown in the photo).  Dr. G (as we often called him) was still quite active after he retired.  He often came to our weekly lab meetings, and I had the opportunity to work on an experiment with him.  He was an amazing scientist who made really fundamental contributions to neuroscience.  For example, when he was a graduate student, he and fellow graduate student Donald Griffin provided the first convincing evidence that bats use echolocation to navigate.  He was also the first person to recognize that glia are not just passive support cells (and this recognition essentially cost him his job at the time).  You can read the details of his interesting life in his autobiography and in his NY Times obituary.

Bob was always a font of wisdom.  My favorite quote from him is this: “You’ve got to get yourself a phenomenon” (he pronounced phenomenon in a slightly funny way, like “pheeeenahmenahn”).  This short statement basically means that you need to start a program of research with a robust experimental effect that you can reliably measure.  Once you’ve figured out the instrumentation, experimental design, and analytic strategy that allows you to reliably measure the effect, then you can start using it to answer interesting scientific questions.  You can’t really answer any interesting questions about the mind or brain unless you have a “phenomenon” that provides an index of the process of interest.  And unless you can figure out how to record this phenomenon in a robust and reliable manner, you will have a hard time making real progress.  So, you need to find a nice phenomenon (like a new ERP component) and figure out the best ways to see that phenomenon clearly and reliably.  Then you will be ready to do some real science!