The Statistical Society of Canada (SSC) awarded Peijun Sang as the winner of the 2019 Pierre Robillard Award of the Statistical Society of Canada. This prize recognizes the best PhD thesis in probability or statistics defended at a Canadian university in a given year. Peijun’s thesis, entitled “New Methods and Models in Functional Data Analysis" was written while he was a doctoral student at the Simon Fraser University, working under the supervision of Jiguo Cao.
His
current
research
interests
are
focused
on
functional
data
analysis
methods.
Data
from
electroencephalogram
signals,
function
magnetic
resonance
imaging
and
diffusion
tensor
imaging
are
important
examples.
He
is
interested
in
applying
functional
data
analysis
techniques
to
study
functional
connectivity
between
imaging
data
collected
from
different
regions
of
the
brain.
He
is
concerned
with
large
sample
properties
of
high
dimensional
functional
regression
models
that
have
been
proposed
for
this
type
of
data.
He
is
also
interested
in
dependence
modelling
with
copulas
for
discrete
and
time-to-event
outcomes.
This work is an excerpt from the SSC website, written by Gordon Fick.