Recent PhD graduates Mike Schaekermann, Hong Zhou and Fiodar Kazhamiaka have each received a Cheriton Distinguished Dissertation Award. Established in 2019, the dissertation award was created to recognize excellence in computer science doctoral research. In addition to the prestigious recognition, each awardee receives a cash prize of $1000.
To be considered for the dissertation award, two nomination letters, typically from the student’s doctoral advisor and external examiner, along with a nomination statement are submitted to a selection committee chaired by the School of Computer Science’s Director of Graduate Studies.
“I was very impressed to see the many high quality works done by our grad students, which had made the selection process a pleasant challenge,” said Justin Wan, Director of Graduate Studies at the Cheriton School of Computer Science. “I wish the awardees all the best, and they continue to make us proud through their future endeavours.”
Because of the pandemic, review of submissions was delayed for the 2020 award competition.
2021 winners
Dr. Schaekermann is currently an applied scientist at Amazon AI.
About
Dr.
Schaekermann’s
research
Ambiguity
—
the
quality
of
being
open
to
more
than
one
interpretation
—
permeates
our
lives.
It
comes
in
many
forms,
arises
for
various
reasons,
and
leads
to
disagreements
that
can
be
difficult
or
impossible
to
resolve.
As
artificial
intelligence
(AI)
is
increasingly
infused
into
complex
domains
of
human
decision-making
it
is
crucial
that
AI
mechanisms
support
a
notion
of
ambiguity.
Yet,
existing
AI
approaches
typically
assume
a
single
correct
answer
for
any
given
input.
Dr. Schaekermann’s dissertation shed light on how humans and AI can be effective partners on ambiguous problems. To address this question, he studied group deliberation as a tool to detect and analyze ambiguous cases in data labelling. He presented three case studies that investigate group deliberation in the context of different labelling tasks, data modalities and types of human labelling expertise.
Dr. Zhou is currently a postdoctoral fellow at the Cheriton School of Computer Science, working with Professor Lap Chi Lau.
About
Dr.
Zhou’s
research
Over
the
past
decade,
the
linear
algebraic
perspective
to
solving
graph
problems
has
become
a
powerful
tool
in
designing
fast
graph
algorithms,
where
graph
spectrum
(i.e.,
eigenvalues
and
eigenvectors
of
some
matrices
associated
with
the
given
graph)
plays
a
crucial
role.
In
this
thesis,
Dr.
Zhou
extends
this
spectral
approach
and
brings
new
insights
and
interesting
results
to
well-studied
network
design
and
experimental
design
problems.
2020 winner
Dr. Kazhamiaka is currently a postdoctoral fellow at the Future Data Systems lab at Stanford University, working with Matei Zaharia and Peter Bailis.
About
Dr.
Kazhamiaka’s
research
Dr.
Kazhamiaka’s
research
explored
ways
to
meet
our
growing
energy
needs
with
clean
renewable
energy
sources.
He
tackled
this
problem
by
maximizing
the
economic
value
of
residential
photovoltaic
(PV)
energy
storage
systems
for
homeowners.
He
developed
algorithms
to
optimally
size
PV-storage
systems
while
providing
guarantees
on
system
reliability,
and
control
solutions
for
PV
storage
systems
that
learn
and
adjust
to
changes
in
the
operating
environment
to
adaptively
optimize
control
decisions.
“For
widespread
adoption,
a
PV-storage
system
has
to
make
sense
economically,”
Dr.
Kazhamiaka
said.
“Everyone
understands
money
and
that’s
the
Holy
Grail
of
sustainable
energy
production
—
finding
a
way
to
provide
clean
energy
that
competes
with
or
is
cheaper
than
what’s
available
now.”