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In-Depth Information
When I started my PhD work in the early 1990s, I was working on the
style of modeling that a physicist does, which is to look for simple problems
where simple models can reveal insight. The relationship between physics
and biology was growing but limited in character, because really the style
of modeling of a physicist is usually about trying to identify a problem that
is the key element, the key simplified description, which allows fundamental
modeling. Suddenly dropping a phonebook on the table and saying, “Make
sense of this,” is a completely different way of understanding it. In some
ways, it's the opposite of the kind of fundamental modeling that physicists
revered. And that is when I started learning about learning.
Fortunately, physicists are also very good at moving into other fields. I had
many culture brokers that I could go to in the form of other physicists who
had bravely gone into, say, computational neuroscience or other fields where
there was already a well-established relationship between the scientific domain
and how to make sense of data. In fact, one of the preeminent conferences
in machine learning is called NIPS,
4
and the
N
is for “neuroscience.” That
was a community which even before genomics was already trying to do what
we would now call “data science,” which is to use data to answer scientific
questions.
By the time I finished my PhD, in the late 1990s, I was really very interested
in this growing literature of people asking statistical questions of biology. It's
maddening to me not to be able to separate wheat from chaff. When I read
these papers, the only way to really separate wheat from chaff is to start
writing papers like that yourself and to try to figure out what's doable and
what's not doable. Academia is sometimes slow to reveal what is wheat and
what is chaff, but eventually it does a very good job. There's a proliferation
of papers and, after a couple of years, people realize which things were gold
and which things were fool's gold. I think that now you have a very strong
tradition of people using machine learning to answer scientific questions.
Gutierrez:
What in your career are you most proud of?
Wiggins:
I'm actually most proud of the mentoring component of what
I do. I think I, and many other people who grow up in the guild system of
academia, acquire a strong appreciation for the benefits of the way we've all
benefited from good mentoring. Also, I know what it's like both to be on
the receiving end and the giving end of really bad and shallow mentoring. I
think the things I'm most proud of are the mentoring aspects of everything
I've done.
