darb.ketyov.com

Caveat lector: This blog is where I try out new ideas. I will often be wrong, but that's the point.

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25.1.10

What does success look like?

This is a really good question that I came across recently. What are the events that would need to transpire in my professional life for me to consider myself successful? In its original incarnation this question was in the context of one's career, however it got me thinking about many aspects of my life and what I think "success" means in those different contexts. I'll focus here on what I think success is in terms of my professional life; otherwise this post would become far too long...

This is an important to me, partly because thinking about it will give me goals to steer toward (and pitfalls to avoid), as well as put me in a mindset of paying attention to my thinking and work habits, which in turn might help me avoid certain workaholic routines.

When I was first starting out on my path to becoming a scientist I was happy just to get a job in a research lab as an undergraduate research assistant (RA). Actually, I was glad just to not get kicked out of school... but in terms of my neuroscience career specifically, an RA position was great. Then I got my bachelor's degree. And then I got a full-time research job. And then I got into a PhD program. And that's where things got weird, because--as my advisor so bluntly stated when I asked him how the hell I got into graduate school--I was a "fuck-up, but a fuck-up with potential". Which gave me something to work toward, if nothing else, but put me a bit off balance early on.

But getting back to the point at hand, what is success? Is it just getting the PhD? I judge from my own use of the word "just" there--a word that sneaked in without me really meaning for it to be there--that the PhD in and of itself is not my metric for professional success.

So maybe it's in terms of publications? But that doesn't quite feel right, either. Maybe success is determined by high-impact publications (e.g., Science, Nature, Neuron)? Given how close I recently came to getting that, I must admit it's a bit frustrating to have not gotten that, but it's also nice to know that I got that close so early in my career. But honestly, that doesn't feel quite like "success" to me either.

Really, I think right now I'm running with a few separate metrics of success: one is more of a desire to "help people" in the sense that I hope my research will some day be useful for medical professionals and help guide treatment. This goal requires good, persistent, and well-conducted hypothesis-driven research to understand brain disease and recovery. This requires a lot of lab work and fits with my work style very nicely, and segues into my second desire, which is to really understand how this lump of tissue gives rise to our perception and consciousness. This feeds well into my workaholic tendencies, as there are all kinds of ways of conducting experiments, analyzing data, etc. that really ensure that I will never run out of interesting datasets to play play with (read, analyze).

My other goal is along a similar line of wanting to "help", but is a bit more dangerous and fallible and requires more PR and social engineering and networking. I believe that scientific thinking and logic can benefit political and humanitarian efforts, but that scientific findings (and the scientific process) can be distorted, misused, and easily misunderstood (see the current global warming debate). This goal is more abstract and ill-defined, and one I really need to spend more time thinking about before I really can talk about it further.

14.1.10

Google Tech Talk is up

So the title of my talk was "Computational Analysis Methods and Issues in Human Cognitive Neuroscience" but I ended up scaling back on the technical side to talk more about basic issues and methods in neuroscience. Like I said at the beginning, I was more hoping to open up a dialog with a smart group of people who have access to a set of technical and mathematical skills that could be of use to neuroscientists. Often I feel like--in our analyses--we're reinventing the wheel and tackling problems that probably already have solutions.

So I never really get into the technical issues (though if anyone is curious I'd be happy to share more details on what those issues are) but I did manage to capture the interest of a few engineers there and we might be working on a few projects together. I'll write more about that if later if anything comes of it.

Anyway, here's the talk. I'm a bit nervous about posting it, since it's not as good as I'd hoped, but hey. That's life.