From Santa Fe Institute Events Wiki

Revision as of 23:59, 9 June 2009 by Watson (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Hey there curious human, I'm watson. I hail from California, where I've been for the last 5 years or so. I moved to Davis (near Sacramento) in 2007 from San Francisco to start a PhD in Applied Mathematics.

My academic interests are in dynamical networks, particularly ones that resemble our brains in some abstract way. I studied neuroscience at MIT as an undergrad, motivated by the prospects of replicating intelligence in machines and understanding a bit better this very machine that is currently constructing this silly self-referential sentence.

Here's a few beliefs I have;

  • We have a lot to learn from nature, and biology in specific. It's had several billion years to get its act together and its latest presentation is quite impressive.
  • Brains are only one example of a wide class of complex networks which can compute things or otherwise act "intelligently". Nature found one path, but it doesn't mean there aren't a whole host of other ones. What's common between various classes of these networks?
  • By studying the neurophisiologicafunctional basis of animal intelligence, I think humans can hope to help humanity in at least a couple of ways:
    • Contributing to our own self knowledge. There are some pretty simple lessons that have had deep impacts on my life when looked at through certain glasses. Let me give you an example. Donald Hebb one of the first greats in theoretical neuroscience proposed that "neurons that fire together wire together." Over the last 60 years this has been repeatedly verified empirically. If you think about it, what it means is that whatever perceptions or behaviors you are partaking in at the same time will later become more likely to occur simultaneously. This is enormously powerful once we understand it. It means we can work to break ourselves of old habits and form new patterns of behavior that serve our lives.
    • Building machines that do menial or repetitive tasks for us. Face it, you want a robot to drive you around. Or to go find you a bunch of cool new music that you probably will like.

I've enjoyed reading about the specifics of people's academic work on here, so why don't I just plagiarize myself briefly and copy over a paragraph from my CSSS application which summarizes my academic self as well as anything:

I want to understand intelligent perception and behavior by building, analyzing and finding patterns in and characteristics of complex dynamical networks. The central question I am interested in addressing is: given a mathematical description for internal as well as intra-node dynamics, how do patterns in the structural topology of a dynamical network predictably give rise to patterns in its functional behavior. Or more compactly: how does structure map to function of a network?

This is a recent project I completed on studying the ways in which networks of spiking neurons can generate interesting patterns. It probably raised more questions for me than I answered, but perhaps thats a good thing?

This is my academic home on the web. My contact information is here in addition to a few interesting and fun links.

Here is the picture I stole from that page:

Outside of school I like to:

  • snowboard and surf
  • climb
  • soak up California's beautiful bounty of outdoor treasures
  • dance
  • play Capoeira
  • take care of myself (meditation, yoga)
  • enrich the experiences of others
  • collect music and DJ
  • write software which creates music in new ways
  • play games (current favorites: Set, Go, Blokus, Backgammon, Euchre and most recently Liar's Dice)
  • explore lands far far away (Indonesia, Africa, Europe...)
  • read

Please be in touch! Or leave some feedback on the "discussion" section of this page. (see the red link at the top) You can find my email here. Can't wait to spend a month with you all...

By the way I'll be driving from Davis to Santa Fe. If you're interested in coming along for part of that let me know.