Why, hello there! I hear you are a potentially interested graduate student, and perhaps you are interested in data structures, and or imaging methods? If so, why you’ve come to the right place! My PI Russ Poldrack recently wrote a nice post to advertise the Poldrack lab for graduate school. Since I’m the first (and so far, only) student out of BMI to make it through (and proudly graduate from the Poldracklab), I’d like to do the same and add on to some of his comments. Please feel free to reach out to me if you want more detail, or have specific questions.

Is graduate school for you?

Before we get into the details, you should be sure about this question. Graduate school is a long, challenging process, and if you don’t feel it in your heart that you want to pursue some niche passion or learning for that long, given the opportunity cost of making a lower income bracket “salary” for 5 years, look elsewhere. If you aren’t sure, I recommend taking a year or two to work as an RA (research assistant) in a lab doing something similar to what you want to do. If you aren’t sure what you want to study (my position when I graduated from college in 2009), then I recommend an RAship in a lab that will maximize your exposure to many interesting things. If you have already answered the harder questions about the kind of work that gives you meaning, and can say a resounding “YES!” to graduate school, then please continue.

What program should I apply to?

Russ laid out a very nice description of the choices, given that you are someone that is generally interested in psychology and/or informatics. You can study these things via Biomedical Informatics (my program), Neuroscience, or traditional Psychology. If you want to join Poldracklab (of which I highly recommend!) you probably would be best choosing one of these programs. I will try to break it down into categories as Russ did.

  1. Research: This question is very easy for me to answer. If you have burning questions about human brain function, cognitive processes, or the like, and are less interested in the data structures or methods to get you answers to those questions, don't be in Biomedical Informatics. If you are more of an infrastructure or methods person, and your idea of fun is programming and building things, you might on the other hand consider BMI. That said, there is huge overlap between the three domains. You could likely pursue the exact same research in all three, and what it really comes down to is what you want to do after, and what kind of courses you want to take.
  2. Coursework: Psychology and neuroscience have a solid set of core requirements that will give you background and expertise in understanding neurons, (what we know) about how brains work, and (some) flavor of psychology or neuroscience. The hardest course (I think) in neuroscience is NBIO 206, a medical school course (I took as a domain knowledge course) that will have you studying spinal pathways, neurons, and all the different brain systems in detail. It was pretty neat, but I'm not sure it was so useful for my particular research. Psychology likely will have you take basic courses in Psychology (think Cognitive, Developmental, Social, etc.) and then move up to smaller seminar courses and research. BMI, on the other hand, has firm but less structured requirements. For example, you will be required to take core Stats and Computer Science courses, and core Informatics courses, along with some "domain of knowledge." The domain of knowledge, however, can be everything from genomics to brains to clinical science. The general idea is that we learn to be experts in understanding systems and methods (namely machine learning) and then apply that expertise to solve "some" problem in biology or medicine. Hence the name "Bio-medical" Informatics.
  3. Challenge: As someone who took Psychology courses in College and then jumped into Computer Science / Stats in graduate school, I can assuredly say that the latter I found much more challenging. The Psychology and Neuroscience courses I've taken (a few at Stanford) tend to be project and writing intensive with tests that mainly require lots of memorization. In other words, you have a lot of control over your performance in the class, because working hard consistently will correlate with doing well. On the other hand, the CS and Stats courses tend to be problem set and exam intensive. This means that you can study hard and still take a soul crushing exam, work night and day on a problem set, get a 63% (and question your value as a human being), and then go sit on the roof for a while. TLDR: graduate courses, especially at Stanford, are challenging, and you should be prepared for it. You will learn to unattach your self-worth from some mark on a paper, and understand that you are building up an invaluable toolbelt to start to build the foundation of your future career. You will realize that classes are challenging for everyone, and if you work hard (go to problem sessions, do your best on exams, ask for help when you need it) people tend to notice these things, and you're going to make it through. Matter of fact, once you make it through it really is sunshine and rainbows! You get to focus on your research and build things, which basically means having fun all the time :)
  4. Career: It's hard to notice that most that graduate from BMI, if they don't continue as a postdoc or professor in academia, get some pretty awesome jobs in industry or what I call "academic industry." The reason is because the training is perfect for the trendy job of "data scientist," and so coming out of Stanford with a PhD in this area, especially with some expertise in genomics, machine learning, or medicine, is a highly sought after skill set, and a sound choice given indifference. You probably would only do better with Statistics or Computer Science, or Engineering. If you are definitely wanting to stay in academia and/or Psychology, you would be fine in any three of the programs. However, if you are unsure about your future wants and desires (academia or industry?) you would have a slightly higher leg up with BMI, at least on paper.
  5. Uncertainty: We all change our minds sometime. If you are decided that you love solving problems using algorithms but unsure about imaging or brain science, then I again recommend BMI, because you have the opportunity to rotate in multiple labs, and choose the path that is most interesting to you. There is (supposed to be) no hard feelings, and no strings attached. You show up, bond (or not) with the lab, do some cool work (finish or not, publish or not) and then move on.
  6. Admission: Ah, admissions, what everyone really wants to know about! I think most admissions are a crapshoot - you have a lot of highly and equally qualified individuals, and the admissions committees apply some basic thresholding of the applications, and then go with gut feelings, offer interviews to 20-25 students (about 1/5 or 1/6 of the total maybe?) and then choose the most promising or interesting bunch. From a statistics point of view, BMI is a lot harder to be admitted to (I think). I don't have complete numbers for Psychology or Neuroscience, but the programs tend to be bigger, and they admit about 2-3X the number of students. My year in BMI, the admissions rate was about 4-5% (along the lines of 6 accepted for about 140-150 applications) and the recently published statistics cite 6 accepted for 135 applications. This is probably around a 5% admissions rate, which is pretty low. So perhaps you might just apply to both, to maximize your chances for working with Poldracklab!
  7. Support: Support comes down to the timing of having people looking out for you during your first (and second) year experiences, and this is where BMI is very different from the other programs. You enter BMI and go through what are called "rotations" (three is about average) before officially joining a lab (usually by the end of year two), and this happens during the first two years. This period also happens to be the highest stress time of the graduate curriculum, and if a student is to feel in lack of support, overworked, or sad, it is most likely to happen during this time. I imagine this would be different in Psychology, because you are part of a lab from Day 1. In this case, the amount of support that you get is highly dependent on your lab. Another important component of making this decision is asking yourself if you are the kind of person that likes having a big group of people to be sharing the same space with, always available for feedback, or if you are more of a loner. I was an interesting case because I am strongly a loner, and so while the first part of graduate school felt a little bit like I was floating around in the clouds, it was really great to be grounded for the second part. That said, I didn't fully take advantage of the strong support structure that Poldracklab had to offer. I am very elusive, and continued to float when it came to pursuing an optimal working environment (which for me wasn't sitting at a desk in Jordan Hall). You would only find me in the lab for group meetings, and because of that I probably didn't bond or collaborate with my lab to the maximum that I could. However, it's notable to point out that despite my different working style, I was still made to feel valued and involved in many projects, another strong indication of a flexible and supportive lab.


How is Poldracklab different from other labs?

Given some combination of interest in brain imaging and methods, Poldracklab is definitely your top choice at Stanford, in my opinion. I had experience with several imaging labs during my time, and Poldracklab was by far the most supportive, resource providing, and rich in knowledge and usage of modern technology. Most other labs I visited were heavily weighed to one side - either far too focused on some aspect of informatics at a detriment to what was being studied, or too far into answering a specific question and relying heavily on plugging data into opaque, sometimes poorly understood processing pipelines. In Poldracklab, we wrote our own code, we constantly questioned if we could do it better, and the goal was always important. Russ was never controlling or limiting in his advising. He provided feedback whenever I asked for it, brought together the right people for a discussion when needed, and let me build things freely and happily. We were diabolical!

What does an advisor expect of me?

I think it’s easy to have an expectation that, akin to secondary school, Medical School, or Law School, you sign up for something, go through a set of requirements, pop out of the end of the conveyor belt of expectation, and then get a gold star. Your best strategy will be to throw away all expectation, and follow your interests and learning like a mysterious light through a hidden forest. If you get caught up in trying to please some individual, or some set of requirements, you are both selling yourself and your program short. The best learning and discoveries, in my opinion, come from the mind that is a bit of a drifter and explorer.

What kind of an advisor is Russ?

Russ was a great advisor. He is direct, he is resourceful, and he knows his stuff. He didn’t impose any kind of strict control over the things that I worked on, the papers that I wanted to publish, or even how frequently we met. It was very natural to meet when we needed to, and I always felt that I could speak clearly about anything and everything on my mind. When I first joined it didn’t seem to be a standard to do most of our talking on the white board (and I was still learning to do this myself to move away from the “talking head” style meeting), but I just went for it, and it made the meetings fun and interactive. He is the kind of advisor that is also spending his weekends playing with code, talking to the lab on Slack, and let’s be real, that’s just awesome. I continued to be amazed and wonder how in the world he did it all, still catching the Caltrain to make the ride all the way back to the city every single day! Lab meetings (unless it was a talk that I wasn’t super interested in) were looked forward to because people were generally happy. The worst feeling is having an advisor that doesn’t remember what you talked about from week to week, can’t keep up with you, or doesn’t know his or her stuff. It’s unfortunately more common than you think, because being a PI at Stanford, and keeping your head above the water with procuring grants, publishing, and maintaining your lab, is stressful and hard. Regardless, Russ is so far from the bad advisor phenotype. I’d say in a heartbeat he is the best advisor I’ve had at Stanford, on equal par with my academic advisor (who is also named Russ!), who is equally supportive and carries a magical, fun quality. I really was quite lucky when it came to advising! One might say, Russ to the power of two lucky!

Do I really need to go to Stanford?

All this said, if you know what you love to do, and you pursue it relentlessly, you are going to find happiness and fulfillment, and there is no single school that is required for that (remember this?). I felt unbelievably blessed when I was admitted, but there are so many instances when opportunities are presented by sheer chance, or because you decide that you want something and then take proactive steps to pursue it. Just do that, and you’ll be ok :)

In a nutshell

If you pursue what you love, maximize fun and learning, take some risk, and never give up, graduate school is just awesome. Poldracklab, for the win. You know what to do.



Suggested Citation:
Sochat, Vanessa. "Poldracklab, and Informatics." @vsoch (blog), 25 Aug 2016, https://vsoch.github.io/2016/poldracklab/ (accessed 28 Nov 24).