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by _delirium 4487 days ago
> the problem is that fundamental research requires funding

In biology, yeah, although I think what a lot of people here are doing is reading stories about high-capital-cost fields (experimental physics, biochem, etc.) and then applying it to their own field, which on HN is mostly computer science. CS research really does not require a lot of funding, outside of specific areas (mostly hardware and robotics stuff). Also, because of a robust industry hiring many people away, the supply/demand situation in CS academia is not as bad, and you can always join them and go to Google/Palantir/Microsoft/whatever if things don't work out. I don't bring in much in the way of grants and I get by just fine, because computers don't cost a lot in 2014, and I don't do the kind of research that requires armies of minions. If I need a 10-computer cluster to run something computationally intensive for a few days, cloud costs are so low nowadays that I can just pay for that out of pocket, never mind trying to figure out how to get it paid by a grant.

Getting a decent job in CS academia where you have some time and freedom to actually pursue research is not at all like winning the lottery. Especially if your focus is not just the top 20 universities and being a famous MIT professor with a big lab. There are many, many places with small to medium-sized CS departments, which will pay you a modest salary and let you do whatever you want.

2 comments

The financial situation in CS is not at all typical of academia. I'm not sure what your point is. Surely you're not suggesting that all academics should become CS academics?
I'm suggesting that if someone already is in computing, as most HN posters are, then the situation of biology academia isn't really relevant to your own industry vs. academia decision. If you're deciding between academia and a tech startup, you should probably look at conditions in CS academia as the relevant comparison.

Now if you're a biologist, the conditions in biology academia are the relevant comparison. But my impression, based on looking at what YC companies are doing, is that most people deciding between YC and grad school are in either CS or business, not the natural sciences.

The biggest cost of my computational grants are funding for people. They don't go away just because you can pay for AWS on a credit card.
Well in my case it's easy: I don't need people. :)

Or to be more precise, I don't need employees. I do work with other people, but they aren't my staff. I do some collaborative projects with colleagues, work with masters students doing their masters theses if they're interested (usually 3-5/semester are interested in working on either my projects, or projects I'm interested in), and also work with some people in industry.

Some kinds of research require an army of minions, but I don't really need employees to do mine. In fact generally I prefer having a smaller number of collaborators so I can really be a researcher doing research and writing papers myself, not a research manager, the kind of professor who's the last author on papers written by their students and postdocs. The institution I'm at doesn't expect American-R1-style large labs, so I can do that. Fortunately there are a pretty wide range of institutions with CS departments with different expectations, so there is quite a bit of choice.

If your goal is just to producing quality papers, this is perfectly great. If your research involves systems and implementations, it sucks to have to write every bits of code yourself. I don't think CS research is just about theory, at least not the line of research I am doing, I am all about making it readily available to others to use.
Yeah, it certainly varies. I'm in AI, which is a bit different from systems. A lot of the work is based on existing open-source code, since you don't need to (probably shouldn't) be constantly reinventing the wheel. In my case it's usually the modeling, data analysis, and insights gained from the data that's new, not the underlying software (e.g. I wouldn't write an SMT solver myself).

When I do produce code, I produce prototype code myself or working together with masters students. When the goal is to produce robust, end-user-ready software, I prefer one of two approaches: 1) work within an existing open-source codebase, contributing improvements upstream; or 2) collaborate with a company to turn a prototype into something polished and end-user-ready. Even if I had a bunch of funding, I don't think I'm in a good position to produce and maintain polished end-user-ready software. Academic software has a habit of going unmaintained when the PhD student graduates or the NSF/EU project ends, and research funding isn't really aligned with production needs. John Regehr talks a bit about that here: http://blog.regehr.org/archives/1058. So I tend to stick with either one-off prototypes, or find a way to collaborate with someone (open-source community, company) that is better positioned to maintain software.

Other decisions are also perfectly valid, just given resources and interests I don't see maintaining essentially a software-production company within academia, with paid staff, as feasible for me personally.

True, but that's not necessarily the case in biology, where the people are no cheaper, but the experiments are a lot more expensive. Assuming $N of grant is as easy to get in CS as biology (ha!), you'll be able to fund way more people in CS.
Agreed (I'm not a C.S. person, I'm a computational epidemiologist). This was mostly addressing the notion that because you don't need the LHC, or banks of PCR machines, or to enroll a couple thousand patients, that C.S. is somehow cheap, has zero costs, and zero pressure to get grant money.

Postdocs, and computing time, and grad students, and your salary are all things that need to be supported by grant money.

It might be easier, but to assert it's easy is flawed.

I'm curious, what is the job market for computational epidemiologists?

The reason I'm asking is my gf is doing her PhD in that area, and I don't think her job prospects are very clear.