Probabilistic Reasoning using Graphical
Models
In probabilistic
reasoning, our research focuses on three aspects: active inference, efficient
inference, and model learning. Specifically, in active inference, we focus on
developing algorithms and techniques that can identify the most informative
evidences to use in order to perform effective inference in an efficient and
timely manner. For efficient inference, our research studies the issue of how
to perform efficient belief propagation of the effects of the observed
evidences. For model learning, our current research focuses on learning the
graphical models by combining quantitative and qualitative data. We are also
developing a unified probabilistic framework based on combining the directed
and undirected graphs through the factor graph model. In application, we are
interested in applying graphical models to information fusion, decision making,
situation awareness, and various computer vision applications.
Current
projects include
1.
Dynamic
and Active Information Fusion for
Decision Making Under Uncertainty (Sponsored by AFOSR and ARO)
2.
Efficient Effects-based
Military Planning (sponsored by ARO)
4.
Software