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I'm a Phd student at the Bristol Centre for Complexity Sciences.
I'm looking at how you can go from the information produced by a system,
whether that system could be the Twittersphere,
international news media, Facebook, or any number of different systems.
How can you go from information that's produced by that system
to work out what the structure of that system is?
The dark-side application of this is -
you could learn how to manipulate the news media or the Twittersphere
but the flip side of that
is you can learn how to better protect these systems from being manipulated.
My research is in the broad area of theoretical ecology.
In particular, I'm trying to apply techniques from statistical physics
to animal movements and interactions.
So lots of animals form territories, which is one thing I'm looking at,
and they do that by moving and interacting.
It's this kind of complex system which is where complexity science comes in
and we try and understand how those territories arise.
One thing that's certainly likely to come out of this research
is to do with epidemiology. Another thing
is conservation, so understanding how much area
an animal, or a population, needs to live in is very important.
I work in the area of economics of renewable energy,
so I look at the way that the wind industry has grown up recently,
try and learn the lessons from that
to inform how other renewable-energy technologies will come up.
I look at convergance between technologies,
I look at the effect of incentive schemes within those technologies
and using that, we try and come up with good government policy
to help these technologies grow up.
I am working with some neuroscientists.
They've got data that they've recorded from rats
who are doing decision making in a maze
and what myself and my supervisors are interested in
is whether brainwave activity that's recorded
during this decision-making task
is indicative of mechanisms that the brain is using
to communicate between different regions in the brain
and share information.
There are diseases
where you get malfunctions in brainwave activity,
for example, in epilepsy and Parkinson's,
so understanding what oscillatory activity does in the brain might lead
to improved treatment for those sorts of diseases.
My research is to do with human-cell conversion.
Recently, we discovered that it's possible to move
from different human cell types,
say, from liver to neuron,
but we don't really know
a good way of predicting the right transcription factors
to use to facilitate these changes.
We're hoping that by understanding cells that have broken down
their normal procedures,
we could coax them back into their normal state.
So I guess one of the things about complexity
is that a lot of real-world systems tend to be complex.
There's a lot of interacting parts and it's difficult to predict the outcome
just on face value, so a lot of our research
is to do with being able to understand these larger systems
in a way that we can make a tangible prediction about the future.
A quite useful example of a complex system is a termite colony.
If you look at an individual termite, it doesn't know anything about air conditioning
or structural engineering,
but if you put enough of them together in the right conditions,
they'll build an air-cooled, ten-foot-high termite mound.
Looking at individual components,
you won't know how the whole thing will behave.
Interestingly, why complexity science is the hot topic now
is because a lot of the approaches
to dealing with these problems involve vast amounts of data
and it's only recently that these data sets have been available
and that we've had the computing power to tackle them.
That's why this is the time.