|
My main research focus is on
dealing with complex domains that involve large amounts of
uncertainty. My work builds on the framework of probability
theory, decision theory, and game theory, but uses techniques
from artificial intelligence and computer science to allow us
to apply this framework to complex real-world problems.
Most of my work is based on the use of probabilistic
graphical models such as Bayesian networks, influence
diagrams, and Markov decision processes. Within that topic,
my work touches on many areas: representation, inference,
learning, and decision making. One main focus has been the
extension of the representational power of the probabilistic
graphical modeling language, to encompass a much richer set of
domains. For example, work in my group includes:
- incorporating hierarchical and object-relational
structure in our object-oriented Bayesian networks (OOBNs) and
probabilistic relational models (PRMs);
- extensions to temporal domains using dynamic Bayesian networks;
- hybrid Bayesian networks involving both discrete and continuous
variables;
- factored MDPs that represent sequential decision problems in a
factored way;
- structured representations for utility functions;
- multi-agent influence diagrams for representing
multi-agent
decision problems with incomplete information;
and more.
I believe that a good representation must also support
effective inference and learning algorithms. Hence, the work
done in my group is also highly focused on these topics. We
have worked on exact and approximate inference algorithms for these
representations, and on approaches for learning these models
from data. On the inference side, we have done a lot of work
on inference in dynamic Bayesian networks, inference in hybrid Bayesian networks,
decision making in factored
MDPs, and inference for large scale models such as those
generated by a PRM or an OOBN. On the learning side, we have
done a lot of work on learning probabilistic models from
relational databases, on active
learning of probabilistic models (where the learner can
query for particular types of instances), and on learning utility functions from data.
Our work spans the range from concepts to theory to
applications. Some of our work is conceptual: defining new
representation schemes and exploring their expressive power.
Some of it is theoretical and algorithmic: designing new
inference and learning algorithms and proving that they
achieve certain properties. And some is applied:
experimenting with our approaches on both synthetic and real
problems. Some of the applications that we are particularly
interested in right now are: learning models from rich
heterogenous biomedical databases, which can include clinical,
genomic, genetic, and epidemiological data; fault diagnosis
for complex hybrid systems; and tracking at the
symbolic level from low-level visual data.
Some of the main funding sources for my work include:
|
