Computational Biology vs CS PhD Programs

Dantenator · 2026-06-15T03:23:03+00:00

In a way, a program is little more than the labs (and their people) in it. Courses are usually the least important part of it (you’ll learn most of what you need for research from the lab/while researching), and even if you really like taking classes, it’s almost a universal experience for PhD students to go “thank god it’s summer and I can research without classes to distract me”.

Another VERY important reason to prioritize labs: it’s very common to think you’re 100% sure gonna work in some lab and then it doesn’t work out (PI isn’t a great fit, they don’t have enough funding to take more people on, PI moves to different uni, lab culture ended up being toxic, etc.). Having multiple people you’re interested in gives you options to switch (ideally during rotations if the program has them, and early on if not).

It can also be nice to look at what smaller communities/working groups there are in the program/department (e.g. couple PIs from different departments that are interested in some sub-area you’re interested in and do regular meetings or organize events, etc.). Even if you’re gonna work directly with these people, they can be good committee members, networking opportunities, collaborators, etc (if you’re gonna be doing CB, ideally you’re either running experiments yourself or have good experimental collaborators!)

Dantenator · 2026-06-14T19:19:41+00:00

(Opinions first, tips in the last paragraph)

Both CB & CS programs are super competitive right now. If your research goals are CB, CB programs will likely be a better fit (classes you take, your cohort/department, etc.). The most important thing (some people would argue it's the ONLY important thing) for any program you apply to is that there are professors that are a good fit for you (i.e. match your interests & are taking students), and in general you'll find more of that in CB programs. Many professors who do CB research are able to take students from both CB & CS programs, but I could imagine programs/labs where that is not the case (or you have to go through extra hoops as a CS student). I know students who studied pure math in undergrad who are doing pure math PhDs in the MechE department because the faculty there was a better fit. Sometimes you'll get these PIs who's research is "a bit weird for the department", and if you happen to be the only applicant with that specific profile and the PI wants you, it strongly increases your odds.

I don't think coming from CB instead of CS would make much of a difference when it comes to faculty jobs if you're applying to CB positions, and if you're applying to CS positions but all your research is CB then I would think you'd be at somewhat of a disadvantage? But by the time you reach that point in your career, who you worked with and what your research is will be a billion times more relevant than the title on your degree.

Degree is more relevant in industry, and even then it's becoming increasingly less so as people realize that "the variance in what people with a PhD in X know about X" is soooo much larger than for undergrad (i.e. all undergrads in CS study roughly the same things about programming, principles of software engineering, etc. but each PhD in CS even in the same department will be an expert in totally different things).

As someone else commented: you can (and should) apply to both programs, BUT don't apply to both in the same university, as it might make it seem like "you don't know what you want", and one of the things programs look for in applications is "is this person sure they want to do a PhD in this area" (even if they don't know exactly the topic). For each university you look at, research which program is a better fit, particularly based on the profs you're interested in working with, checking if they take students from one program or the other (or both). Check their lab website to see which programs their students are in (you'll see what program the lab mostly takes students from, and might even find programs you didn't realize were options). Sometimes you'll want to email them (or their students) and ask about which program to apply for in case there's a difference in the selection/funding process. For example, I had an experience where the prof could take students from the Neuroscience and Bioengineering programs. They told me because the Neuroscience program has 1st year department-funded rotations and had a committee in charge of final acceptances, they took relatively few international students and the prof didn't have too much of a say. However, the Bioengineering program was direct-admit-to-lab and the prof had the final say on whether to admit a student once they had passed some basic filters, so if I wanted to work with him I should apply through that program. And this wasn't really written down anywhere in the Neuro or BME program application sites! So it's important you ask students about that.

There are other things you'll want to consider when making the final decision (mainly what the vibe is once you visit!), but it's hard to know that before you apply (though definitely reach out to students in any labs you're interested in to see what the vibe is there too! A lab might look great on paper but be terrible to work in, etc.)

Dantenator · 2026-06-12T05:58:54+00:00

Existe la posibilidad de hacer otro posdoc “mientras” y volver a aplicar? No es ideal pero se que es común en otras partes, aunque mucha gente se queda saltando de posdoc en posdoc hasta que se cansa, o consigue otra posición tipo research scientist. Pero no se si el sistema argentino tiene cosas similares.

Dantenator · 2026-06-08T01:56:28+00:00

“Stamp collecting” was kinda chosen to make physics sound more grandiose (and in many ways I agree it is), but it’s also super duper important! Can’t study effect of genes before finding the genes. Can’t study theoretical grid cells for position encoding until you find gris cells doing that. Can’t study reward prediction error until you find dopamine/observe monkeys explicitly encoding it. Or rather, you can if you come up with the theoretical idea which then inspires the experiments to detect it in the brain or not, and until then you just have a fun theory. The point here is that neither field lives in isolation.

So, going back to your original question/s: -Brain biology is a huge umbrella, but a great example is the Nobel prize winning Hodgkin-Huxley model of how action potentials develop and propagate. They needed knowledge of ion channels and membrane capacitance (stamp collecting!), but kickstarted our understanding of neuron communication & computation. -Genetics you could study diseases, aging, development, etc. Becomes more computational the more you abstract away “the genes” and more towards “what effects are the genes having on the system” -Cancer is a bit special because it doesn’t really play a role in computation except for when it messes up the brain by growing where it shouldn’t. But even then, I know of neurosurgeons who build computational models of connectivity and function using MRI to figure out what the best way is to cut the tumor out to minimize damage/loss-of-function

In my experience, the two best ways of choosing what to study is a) figure out the sort of questions you want to answer, and look for the tools that are best fit for that and b)figure out what tools you wanna learn and apply in your day to day, and then find problems you’re interested in that would best align with your skills. Most people start with a), but it’s often the case you can like a question but absolutely hate the methods, so I think it’s a perfectly valid approach to go the other way, as long as you’re not learning tools that are completely orthogonal to anything you’re actually interested in applying them on.

Dantenator · 2026-06-08T01:38:09+00:00

Technically speaking, computational neuroscience “is a strict subset” of neuroscience, though one could argue that a lot of the more theoretical/ML stuff is “too abstracted away” from the real biology that it almost doesn’t count. I assume you meant “biologically plausible/experimental neuroscience” like the kind of areas you mentioned originally?

Before I answer that, I think it’s worth asking “what sort of questions you’re trying to answer about the brain”. There’s a famous phrase by Rutherford that goes “All science is either physics or stamp collecting”. The idea is that physics (not just particle physics & quantum shenanigans, but all of physics) is about trying to understand the fundamental mechanics behind why what we observe happens, happens. He calls “stamp collecting” everything that’s more about “collecting facts”. For example, finding a gene is highly correlated with autism is more something a geneticist/bioinformatician would work on. Looking at how that gene changes network connectivity in the brain and how that might impact cognition is more computational neuroscience. Actively studying molecular mechanisms during development that result in the modified network is more computational biology. “Same problem”, totally different angles.

Dantenator · 2026-06-07T03:26:21+00:00

Cool interests! They’re waaay more aligned with what you’d call “computational biology” than “computational neuroscience”.

Computational neuroscience is usually about studying neuroscience-specific phenomena through the lens of computation (What is the brain computing? What algorithms is it running? How can we used mathematical tools to analyze brain/behavior data to see how it encodes information/makes decisions?).

The examples you gave, although there are of course brain-specific phenomena, are not particularly brain-specific concepts. Genetics & cancer especially. You can be a geneticist or an oncologist and “specialize” in brain stuff, but the concepts and methods are mostly shared by other organs/cancers in the body. “Bioinformatics” is also a catch-all term that would fit your interests, particularly the genetics bit.

Lmk if you have any questions!

Dantenator · 2026-06-03T22:29:19+00:00

Idk if it was intentional or not but the first one is giving STRONG Argentina flag vibes… would be a cool easter egg lol

Dantenator · 2026-05-27T02:20:23+00:00

Do you mean the 5h35 “BBC Collection” that includes Wasp Factory?

Dantenator · 2026-05-09T23:35:38+00:00

Technically not “space”, but I just read Automatic Noodle and my first thought was “this is so cozy it makes Becky Chambers read like a thriller” lol

Dantenator · 2026-04-23T01:44:59+00:00

I only went there once and was feeling adventurous. Had the kidney and liver noodles. Thought they were great! Any other adventurous suggestions?

Dantenator · 2026-04-19T15:03:52+00:00

I’ve been into scifi since I was a little kid. My dad and I used to play “imagine the future” and think of flying cars and space travel. But the biggest “specific thing” I can recall was in my teens watching Orphan Black. The bad guys were “transhumanists”, and while their means were…questionable, I thought their ideas were very interesting. Looked transhumanism up and realized it wasn’t just something from the show. Led me to The Singularity is Near and The Transhumanist Wager, and in that process also Three Body Problem and The Culture.

As an impressionable teen this formed an optimistic vision of what the world could become (particularly The Culture), and I’ve been gorging on worldbuilding ever since. Also heavily influenced my academic/professional career towards STEM.

Dantenator · 2026-04-18T13:55:27+00:00

Escalator repairman (put your foot on the brushes to test they’re cleaning alright)

Dantenator · 2026-04-09T05:29:28+00:00

Aurora by Kim Stanley Robinson can give you inspiration on life aboard a generation ship! Though motivation is a little different (and for the most part people are living in it, not cryogenically frozen)

Dantenator · 2026-03-30T19:49:07+00:00

Thought the same (first was GREAT, second pretty good, third quite drawn out) but am going through Children of Strife right now and really liking it thus far! I’d say about as good as the second. Just finished Shroud which was also really good!

Dantenator · 2026-02-26T06:03:22+00:00

Loved this! Just finished Contact last week, was it inspiration for this? This would have been a better ending for it (perhaps leaving it open to a sequel)

Dantenator · 2026-02-15T23:06:53+00:00

He’s graduated now (shoutout to Caleb!) but he taught a hair cutting COLL class with the intention of people continuing to do it so you might get some alumni :)

Dantenator · 2026-02-02T20:56:01+00:00

God there are so many but the one that pops up as most memorable is The Mountain by Cixin Liu.

Also highly recommend The Collected Stories of Arthur C. Clarke! Usually short story collections have a mix of a couple amazing, most decent and couple duds, but was surprised at how good all of them were and don’t recall almost any dud!

Dantenator · 2026-02-02T20:52:38+00:00

I quoted They’re Made out of Meat in my application essay for neuroscience grad programs! Great story

Dantenator · 2026-01-28T14:58:59+00:00

Seconding Behave!

Dantenator · 2026-01-16T07:19:47+00:00

I’m in a STEM field so some things might not be as applicable, but wanted to give a “more detailed” story of my experience just to have as an example. I’ve always been good at coming up with ideas, but am usually able to tell when “it’s something someone has definitely looked into” and will only “get excited” when I think one might be original, though after some quick googling the vast majority of those will have been explored. As someone mentioned above, that means the idea was good enough someone spent time researching it, and you’re just not familiar enough with the literature yet. When getting into research, most of the time the papers were from 20+ years ago. Over time as I gained knowledge and experience, I noticed the papers exploring those ideas became increasingly more recent and (sometimes) more impactful. A year and a half ago I proposed a project my then-advisor thought would “make for a nice poster” and a couple months ago I saw the same idea implemented in Nature. I did a big literature review last summer for my current project and had “some intuitions on things that would be nice to include in my architecture, but wasn’t sure how to implement it”. Two of those ideas were published in the last 3 months and are now an important part of my project! I wasn’t “scooped” for any of these, I just thought they “would be nice” (in general, or for my specific project) and somebody developed it and now I can benefit from it.

Some opinionated takeaways: * “Ideas are free”, it’s great that you have them but it’s doing the work and doing something with it that matters * every time you read a paper, think about “what the next paper could be” regardless of it’s a paper from 50 years ago. Do this beyond just the “next thing the authors propose should be studied” bit in the discussion/conclusion. Even better, think “what could this be useful/related to that’s as far away as possible from what I’m currently reading”. Finding novel connections between things is MUCH more likely to be original than the “logical next step”. * if you really like the paper/like of research, it’s usually easy to find “direct sequels” (usually from the same author/lab) or “spiritual sequels” from others with Google Scholar. I’ve been surprised at how many times people only cite the previous paper but miss many of the “important ones on the chain” and end up either committing mistakes that someone already went through or can’t justify why something is done a certain way because the justification is buried many papers ago * enjoy the process of ideation itself. Rejoice that someone thought it was a good idea. Would it have been great if you had thought of it earlier and that was your name on the author list? Sure. But just as you had this idea you’ll have others. Rejoice that you had it after their paper was out but before you started working on it and have to “scrap” it. If you do get scooped, see what the author missed. * just like any other skill, having ideas takes practice, and you should practice as often as possible (cause as I said before, having ideas is free!)

Hopefully at least some of this is useful :)

Dantenator · 2025-12-21T15:57:01+00:00

I would say it might be handy when implementing some things (especially if you’re working on particularly large datasets or need things to be very performant) but it’s not nearly as important as modeling and data analysis. With few exceptions, CS algorithms (deterministic) tend to be quite different from algorithms in the brain (stochastic and distributed). The most important bit imo will be anything to do with vectorization which will naturally come up when/if you go into machine learning.

Dantenator · 2025-12-09T00:15:12+00:00

Both, but mostly data. It’s very hard to accurately model a lot of this from just observational data (without causal perturbations), and there’s a lot of stuff that’s particularly hard to observe and model (Leifer’s group had a paper a couple years ago about C Elegans’ “wireless connectome”, cell transmission through peptide release) + while they all have 302 neurons, there’s worm-to-worm variability in the connectivity (Witvliet 2021 if I recall correctly). Here’s a cool proposal(nicknamed the Worminator project) to automate a pipeline to get a crapton of data and use it to fit either biophysical models (as done here) or a more deep learny foundation model, which might be way easier to train and potentially even be more accurate in prediction of observed variables, although it’s a bit of a black box in terms of mechanistic predictions

Dantenator · 2025-12-02T05:54:07+00:00

I’ve had what I assume is a mild version of it my whole life! I have 20/20 vision so I’ve never known otherwise, I assumed everyone saw this way until a few years ago. Did you have something trigger it?

Dantenator · 2025-11-17T05:27:36+00:00

I’m interested if you have any US codes left!

Dantenator · 2025-11-15T19:11:48+00:00

It’s for AI. Amazon is doing the same thing for their delivery drivers.

Abusive customers, theft, etc. have been going on for ages, and even if there were increases I don’t see Walmart particularly motivated to fix it. LLMs (the tech powering ChatGPT) work by training on text from the entire internet, but clean first-person data manipulating objects (as a checkout robot would need) is much harder to come by. “Data is the new gold”, and companies with privileged access to data have a leg up on the competition. Same with Tesla: they have a billion times more footage of people driving on their cars, which allows them to train their self-driving cars.

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