Navigation and Evaluation of Latent Structure in High-Dimensional Data
In
the
modern
data
analysis
paradigm,
fitting
models
is
easy,
but
knowing
how
to
design
or
evaluate
them
is
difficult.
In
this
talk,
we
will
adapt
insights
from
graphical
statistics
and
goodness-of-fit
testing
to
modern
problems,
illustrating
them
with
applications
to
microbiome
genomics
and
climate
systems
science.
For
the
microbiome,
we
show
how
linking
complementary
displays
can
make
it
easy
to
query
structure
in
raw
data.
We
also
find
novel
visual
summaries
that
inform
model
criticism
more
deeply
than
data
splitting
strategies
alone.
We
then
describe
how
artificial
intelligence
can
be
used
to
accelerate
climate
simulations,
and
introduce
techniques
for
characterizing
goodness-of-fit
of
the
resulting
models.
Viewed
broadly,
these
projects
provide
opportunities
for
human
interaction
in
the
automated
data
processing
regime,
facilitating
(1)
streamlined
navigation
of
data
and
(2)
critical
evaluation
of
models.