Research group’s website: http://ai.uwaterloo.ca
Group’s contact person: Gautam Kamath
Group members
- Shai Ben-David
- Yuri Boykov
- Dan Brown
- Wenhu Chen
- Robin Cohen
- Yuntian Deng
- Kimon Fountoulakis
- Ali Ghodsi (Department of Statistics and
Actuarial Science) - Maura R. Grossman
- Jesse Hoey
- Gautam Kamath
Overview
Artificial intelligence and machine learning are broad research areas within computer science that encompass a number of topics related to the design of computer systems that perform tasks conventionally associated with human intelligence. These areas overlap with several other research areas both within and beyond computer science, including algorithm design, information theory, statistics, optimization, scientific computation, human-computer interaction, and more. Some specific focuses of group members are listed below.
Machine
learning
Machine
learning
is
concerned
with
the
analysis
and
development
of
methods
to
explore,
discover,
visualize,
and
model
structure
in
data
as
well
as
to
make
predictions
and
decisions
based
on
that
structure.
Data
is
often
incomplete,
noisy,
non-homogeneous
in
structure
and
large
in
size
(e.g.,
large
number
of
observations
or
dimensions,
or
both).
Special
attention
is
paid
to
the
development
of
computationally
efficient
(with
respect
to
time
and
memory
usage)
algorithms.
Research
includes
the
mathematical
and
computational
analysis
of
the
statistical
methodology,
the
development
of
new
techniques,
algorithms,
and
software,
and
the
application
of
these
to
complex
problems
from
other
areas.
Intelligent
user
interfaces
Integrating
natural
language
processing
models
and
user
models
to
produce
more
effective
human-computer
interaction.
This
includes
designing
interfaces
that
allow
for
mixed-initiative
interaction.
Applications
include
interface
agents,
electronic
commerce
and
recommender
systems.
Multi-agent
systems
Studying
how
computational
limitations
influence
strategic
behaviour
in
multi-agent
systems,
as
well
as
developing
approaches
to
overcome
computational
issues
that
arise
in
practical
applications
of
mechanism
design
and
game
theory.
Designing
systems
of
collaborative
problem-solving
agents,
with
an
emphasis
on
issues
of
communication
and
co-ordination
for
applications
of
multi-agent
systems
to
the
design
of
effective
electronic
marketplaces
and
adjustable
autonomy
systems.
Modelling
trust,
reputation
and
incentives
in
multi-agent
systems,
including
the
use
of
social
networks.
Natural
language
processing
The
exploration
of
statistical
and
linguistic
techniques
to
automate
the
analysis
of
natural
text,
the
synthesis
of
clusters
of
documents,
the
retrieval
of
information
from
unstructured
documents
and
the
development
of
methods
and
software
tools
for
computational
rhetoric.
Application
domains
include
personalized
mobile
health
and
web
analysis.
Constraint
programming
Investigating
methodologies
for
solving
difficult
combinatorial
problems
by
emphasizing
modelling
and
the
application
of
general
purpose
search
algorithms
that
use
constraint
propagation.
Current
projects
include
instruction
scheduling,
constraint
propagators
for
global
constraints,
and
applying
machine
learning
techniques
to
devise
heuristics.
Computational
vision
Developing
computational
theories
of
perception,
based
on
Bayesian
inference,
preference
rules,
and
qualitative
probabilities,
and
applying
such
methods
to
problems
in
object
recognition,
motion
estimation,
and
learning.
Other
work
includes
computational
perception
of
scene
dynamics,
with
applications
in
event
recognition,
human
computer
interaction,
and
robotics,
the
analysis
and
categorization
of
image
motion,
particularly
in
densely
cluttered
scenes,
and
the
recognition
of
human
behaviours
in
natural
environments
with
application
to
assistive
technology.
Decision-theoretic
planning
and
learning
Design
of
algorithms
to
optimize
a
sequence
of
actions
in
an
uncertain
environment.
The
emphasis
is
on
probabilistic
and
decision-theoretic
techniques
such
as
fully
and
partially
observable
Markov
decision
processes
as
well
as
reinforcement
learning.
Applications
include
assistive
technology
for
persons
with
physical
and
cognitive
disabilities
and
spoken-dialogue
systems.
Affective
computing
Studying
how
intelligent
systems
can
be
improved
by
reasoning
about
emotions.
Investigating
theories
of
culturally
shared
affective
sentiments
during
human-machine
interaction.
Application
areas
include
tutoring,
sentiment
analysis,
assistive
technologies
and
computational
social
science.
Human-in-the-loop
intelligent
systems
Across
many
AI-related
research
areas,
various
models
combine
human
and
machine
intelligence
to
solve
computational
problems,
including
human
computation
(e.g.,
crowdsourcing),
learning
by
demonstration,
mixed
initiative
systems,
active
learning
from
human
teachers,
interactive
machine
learning,
etc.
In
these
systems,
humans
are
a
critical
part
of
the
computational
process
—
they
serve
as
teachers
and
collaborators
to
the
AI
system,
providing
feedback
and
corrections,
or
performing
computational
tasks
that
are
difficult
for
existing
algorithms.
This
area
of
research
is
at
the
intersection
of
AI,
Human-Computer
Interaction
(HCI)
and
EconCS,
involving
the
design
of
interfaces,
algorithms
and
incentive
mechanisms
to
harness
human
processing
power
to
tackle
challenging
computational
problems.