Cool, Science-y Masters Programs For Software Devs?

An anonymous reader writes "I'm an early-30s software engineer with 10 years of development experience, and a BA in computer science from a top university. I've been working for several years at a national lab in bioinformatics, but I'm starting to wonder what other interesting directions there are to go for people in my boat: computer science majors with software development experience. The goal would be to find a position that could leverage my development skills, but also include a strong research component, without the need for a Ph.D. (I would be happy to get a masters for the right job.) I'm actually getting some of those things in my current job, but I'm ready to move on to new or different areas of research. Possible fields that seem interesting so far: neuroscience, economics/sociology, and AI. I'm happy to work in a team in support of Ph.D.s, but would like an active part in the research end of things as well as the tool-making end."

Comments

Computational Physics

[diewlasing]

I'll cast my vote for computational physics. As a physics grad student myself, I find myself writing and reviewing code for simulations. And you don't need a phd to do this.

If you get any sort of training in computational physics you could be invaluable. Computational physicists are in demand in almost all fields: nuclear, atomic (simulating system-bath interactions), high energy, biophysics (protein folding sims), astrophysics, etc.

In my department, we have collaborated with the cs department in writing software for some of our sims.

Re:Computational Physics

[zeroRenegade]

I also came from a top computer science school, but I only worked for a year, before having it out with a new senior developer (who wanted me to hold his hand when it was not my job to train asshole senior developers). A professor happened to offer me a masters program the same week everything exploded at work. The happenstance of it was uncanny, so I took the opportunity without a second glance, and quit my job. My topic is hydrodynamics engineering. Numerical simulations for fluid dynamics is one of the most satisfying fields of research. It can be very graphically oriented, or purely math based. If I were you, I would email a few professors in fields that interest you. Find their email address es by combing over the faculty lists at schools that interest you, and check out their personal webpages, since they will list a lot of the research they are currently involved in. The other step is to check out major conferences like chi or siggraph (and some minor ones). Check out videos online, read some papers and presentations that interest you, and then contact the individuals involved.

It depends

[hoytak]

As a Ph.D. student in statistics with a masters in CS (mainly machine learning and AI), here's my few words of advice:

First, some masters programs are aimed at research masters, and encourage you to incorporate a strong research component to your degree, and some are more "predictable" and classroom based with smaller, more defined projects. The master's program I did at UBC - - University of British Columbia -- was heavy on the research; we took 1 year of classes and then 1 year of research. They also have a strong machine learning and AI program, which I thought was very neat. If you pursue that direction, contact me directly and I'll give you the inside scoop. Other programs may have similar research tracks, but many don't.

Second, it would really be the particular professors you end up working with that will shape your experience and how much you develop your software skills. You can learn about what a particular research group or working group is like from the websites of the professors involved and what sorts of paper and software they've published recently. I would highly encourage you to contact such professors before you apply to the university; the university admissions process is more about keeping bad people out than making sure the absolute best get in, so there's a lot of randomness in the admissions. Having a professor say "I'd like to work with this person, he'd be a big help to my research, can you let him in" usually means you get in unless the department doesn't think you could succeed. And, frankly, any professor would love to have a great coder on their team; many people without job experience can be bad coders.

Finally, if you are math inclined, and want something that could vastly help you in the job market, I'd consider doing a statistics degree. Statistics is pretty ubiquitous -- machine learning, AI, etc. are really just sexy names for statistics (yes, there's some more algorithms thrown in the mix, but the underlying theory is all statistics), and it also comes up in pretty much every other field as well. If you go to a strong research university, it's likely that you'll have opportunity to do research in a ton of different fields; I'm now at the university of washington in the stats department, and half the professors are joint with another department like economics, sociology, biology (there's a strong biostats department too), etc. I joke that it's the degree program for indecisive people, since it doesn't really limit what field you end up studying in. (Of course, not all stats programs are like this, but UW is).

You'd be surprised

[Kupfernigk]

My family all seem to be engineers, computer scientists or lawyers. There really isn't that much difference whether you're checking available APIs and algorithms and using them to build software, checking technologies and codes and using them to design a building, or checking law and precedent to build an argument. They all involve abstract thought, concrete outcomes, and an ability to guess in advance how people will screw up, and try to mitigate it. Law pays more, engineering gives you greater variety of work, that's about it.