My broad research interest is the use of information technology to
support
better inference and decision making. Within this broad area, I have
worked
on the application of decision theory to intelligent decision and
inference
support systems. I have worked on understanding the proper role of
normative,
behavioral, and computational theories in the modeling and support
of
decision
making. My main areas of interest include:
Knowledge representations for reasoning about uncertain
phenomena
Construction of problem-specific probability and decision
models
from a
generic knowledge base
Development of a large knowledge base for reasoning about the
behavior
of military units using background knowledge and incoming
intelligence
reports
Methods for combining expert knowledge and data to learn both
structure
and parameters of probability models
I teach courses in systems engineering, decision theory, and
decision
support
systems. Education is a lifelong process of bringing out an
individual's
unique potential. Although the ultimate responsibility and the
ultimate
reward lie with the student , a teacher can do much to facilitate or
to
repress a student's joy in learning and willingness to stretch
beyond
the
horizons of his or her current knowledge and worldview. My primary
objective
in teaching is to spark in students the excitement I feel a bout the
subject
matter. I aim to give students the basic knowledge and the
confidence
they
need to be able to follow their curiousity where it leads them. Information
about courses I teach is available online.
My Educational Background:
I received a BS in Mathematics from the University of Pittsburgh, a
Masters
in Mathematics from the University of Michigan, and a PhD in
Statistics
and Public Affairs from Carnegie Mellon University.