What do you do?

[Nathan Poe]

High school teacher -- specialize in American and World Lit.

Master's Degree in Literature; studying Literary theory and mythology as hobbies.

 

[atomweaver]

High school teacher -- specialize in American and World Lit.

Master's Degree in Literature; studying Literary theory and mythology as hobbies.

I had no idea that you were so well-qualified to comment on creationism.

 

[Nathan Poe]

I had no idea that you were so well-qualified to comment on creationism.

It helps to have a working knowledge of fairy tales.

 

[TemperateSeaIsland]

Organic chemist. Currently hiding out in academica until this recession blows over, at the moment I'm doing process development work on natural products that have potential uses as theraputics. Before this I was a researcher looking at organo-catalysts in industry.

My favourate colour is sky blue.

 

[Wiccan_Child]

Studying for an MSci in Physics. I also read up on evolution a lot, and science in general. And I'm a wizz at maths (it's my mathemagicians outfit).

 

[Nathan45]

I'm a computer programmer, i make custom IT software for business...

i code mostly asp.net web applications and windows applications, with a SQL server backend, using C# and SQL.

i do read a lot of science magazines and websites but i'm not an actual scientist

 

[Maxwell511]

I just found myself wondering what everyone in this forum does for a living. A lot of posts seem very well informed, so... Are you a student? A professional scientist? Or is science more of a hobby? What field are you in? What are your special interests?

To start off: I'm a 3rd year student of medicine. Special interests: neuroscience, especially circuits and motor control.

I'm doing a doctorate where I spend most of my time wishing that P=NP.

Basically I study the behaviour of models based on complexity theory of the evolution of electrical power grids.

Reading about neuroscience is a hobby of mine. Here's a paper you might find interesting on brain plasticity

​

; it combines neuroscience, circuits and complexity theory.

 

[JBJoe]

Once upon a time I was a Math/Computer Science undergrad student doing work bioinformatics and computational biology. Then I graduated and got a job at a lab using computers to do statistical models of animal diseases. Then I learned what it means to work for Ph.D. Biologist at a top tier Biology school.

Now I work in R&D for a large semiconductor company. I'd really like to get back to scientific research, but I feel like with each passing year it is only getting more difficult.

 

[lostaquarium]

Student, computer science. I want to go into computational neuroscience.

That's cool We only have a few lectures on computational neuroscience, but already I think it's amazing. The way that people make up theories just based on what makes sense, then find evidence that that's indeed how the brain works

 

[lostaquarium]

I'm doing a doctorate where I spend most of my time wishing that P=NP.

Basically I study the behaviour of models based on complexity theory of the evolution of electrical power grids.

Reading about neuroscience is a hobby of mine. Here's a paper you might find interesting on brain plasticity; it combines neuroscience, circuits and complexity theory.

Thanks, but woah, I obviously don't know enough because I couldn't even understand that abstract Where did you find it?

And what do you mean about P=NP?

 

[Dark_Lite]

I'm a computer programmer, i make custom IT software for business...

i code mostly asp.net web applications and windows applications, with a SQL server backend, using C# and SQL.

i do read a lot of science magazines and websites but i'm not an actual scientist

Well, computing is definitely an interdisciplinary science. The higher-up things like computational complexity theory, study of grammar and various other mathematical underpinnings of computation, quantum computing, swarm intelligence (my personal favorite), and research into various recognition algorithms (speech, image, etc) is all science.

Though, I guess most programmers aren't "scientists" in the purest sense--more like engineers. There are scientific underpinnings but the people are not usually doing academic research.

Although, I'm guessing you probably already agree with all of the above.

 

[Dark_Lite]

And what do you mean about P=NP?

I'm assuming he means polynomial time and nondeterministic-polynomial time. It's the time that it takes to solve certain problems like the traveling salesman problem (which is NP-complete).

http://en.wikipedia.org/wiki/NP_(complexity)

 

[Wiccan_Child]

I'm assuming he means polynomial time and nondeterministic-polynomial time. It's the time that it takes to solve certain problems like the traveling salesman problem (which is NP-complete).

http://en.wikipedia.org/wiki/NP_(complexity)

If I remember correctly (which I probably don't), it's to do with the time it takes a computer to solve a problem, and the time it takes the same computer to work out if there's a solution at all. And P=NP is when those two times are the same.
I think.
Maybe.
Computers confuse me.

 

[Dark_Lite]

If I remember correctly (which I probably don't), it's to do with the time it takes a computer to solve a problem, and the time it takes the same computer to work out if there's a solution at all. And P=NP is when those two times are the same.
I think.
Maybe.
Computers confuse me.

Well according to the page it's the time it takes to solve the problem with a deterministic Turing machine vs a nondeterministic one. Good thing I remembered that from the class I took on that stuff 1 semester ago...

Yeah...

Although, to be fair we never really got that far into it, just mentioned it in passing. Did do quite a bit with Turing machines though...

 

[necroforest]

If I remember correctly (which I probably don't), it's to do with the time it takes a computer to solve a problem, and the time it takes the same computer to work out if there's a solution at all. And P=NP is when those two times are the same.
I think.
Maybe.
Computers confuse me.

Not quite.

P and NP are classes of decision problems (technically, formal languages but we can forget that for now), and a decision problem is a problem with a yes/no answer, i.e "Given a number n, is n prime?".

"P" is the class of decision problems that can be solved efficiently (specifically, in a number of steps that is a polynomial function of the size of the input). A good example of a problem in P would be "Given a number n, is n a perfect square?"

"NP" is the class of decision problems that can verified efficiently: If a particular instance of the problem has a yes answer ("Is 25 a perfect square"), there exists some form of short "proof" or example that the answer is yes ("5x5=25"), and that short "proof" can be checked efficiently (with this example, "5" is the "proof" and the method for checking is by squaring 5 and checking that we get 25)

It's pretty easy to see that everything in P is also in NP, the question is whether everything in NP is also in P. Most people (who know enough to have an opinion) think the answer is no. An example of a problem in NP that is (probably) not in P is the traveling salesman problem: Given a list of n cities and distances between them, and some maximum distance k (in some unit), is there a way to visit each city exactly once and travel less than k units? The obvious way of solving it is to check every possible path, but the number of paths is exponential (2^n), which is not very efficient for large n. Nobody knows a (significantly) faster way of answering the question.

 

[sfs]

Sfs, as a fellow particle physicist, I would be most interested to know how you got into genetics.

I was a staff scientist at SLAC (working for Princeton), and my wife and I wanted to move to the Boston area. I didn't want to travel a lot, and I didn't want to try to work remotely, far from an experiment, so I figured I might as well just find something new to do. The extremely long lead time for experiments, and the intense competitions for analyzing the resulting data, made the decision easier.

Since I had done a lot of programming and software design (HEP experiments have moderately large programs (~1 million lines of code) handling the data) as a physicist, I could plausibly package myself as a software engineer. So I applied for a software engineering position I saw online at the Whitehead/MIT Center for Genome Research and they hired me. I'm still there, only now it's called the Broad Institute of MIT and Harvard. Fortunately, this is a flexible place where anyone can do science if they're up for it, and I started analyzing genetic data (and reading lots of textbooks). After a while they changed my job title to computational biologist and I became a sort of population/statistical geneticist.

The field is much more lively and has a better morale than HEP did, and I've had a lot of fun. Many computational and math types have been moving into genetics and other parts of biology over the last decade or so, as data sets have expanded enormously and statistical and computational methods have flourished. For instance, I share an office with this guy.

(Oh yeah, I forgot to mention that I also have a master's degree in English literature.)

 

[arunma]

Yeah, I know what you mean with HEP. One of the reasons I like particle astro better is because of the slightly smaller timescale as far as experiments go. Anyway, it's good to know what happens to non-professor physicists after grad school, since I've been wondering about my own employment possibilities recently.

 

[Dark_Lite]

It's pretty easy to see that everything in P is also in NP, the question is whether everything in NP is also in P. Most people (who know enough to have an opinion) think the answer is no. An example of a problem in NP that is (probably) not in P is the traveling salesman problem: Given a list of n cities and distances between them, and some maximum distance k (in some unit), is there a way to visit each city exactly once and travel less than k units? The obvious way of solving it is to check every possible path, but the number of paths is exponential (2^n), which is not very efficient for large n. Nobody knows a (significantly) faster way of answering the question.

Ant Colony Optimization! Although technically that is a not really a defined algorithm (in the sense that it leads to the same solution every time) but more of a heuristic-esque thing. It's kind of interesting how the solutions to a lot of more complicated problems are moving towards the (seemingly) heuristic problem-solving nature of natural organisms rather than via defined mathematical algorithms.

Always knew there was a reason nature was here first. We've got it all figured out subconsciously.

 

[necroforest]

Ant Colony Optimization! Although technically that is a not really a defined algorithm (in the sense that it leads to the same solution every time) but more of a heuristic-esque thing.

Right, which is why the problem is still open - ACO and other heuristics (Evolutionary computation, randomized/probabilistic algorithms) don't actually solve them, they just try to find good local optima in a reasonable time frame.

 

[jayem]

M.D., certified in internal medicine, currently practicing occupational medicine. Medical director for an aircraft manufacturing plant.

Interests are health insurance reform and medical ethics.