Lappoon R. Tang
Assistant Professor of Computer Sciences,
University of Texas at Brownsville
Ph.D. in Computer Sciences, University of Texas at Austin, 2003
M.S. in Computer Sciences, University of Texas at Austin, 1998
B.S. in Computer Sciences, University of Texas at Austin, 1995
Email: my_first_name.my_last_name@utb.edu 8-)
Phone: 1-956-882-6771
Research Interests
Professor Tang's area of research is in Machine Learning. It concerns building systems
that can improve their performance with experience. He is particularly interested
in learning in logic (e.g. first order logic). Learning in such a framework
allows the use of background knowledge and learning is incremental.
However, learning in full fledged first-order logic (FOL) is generally very
inefficient. Classification in first order logic is also undecidable in
general since FOL is undeciable (despite that it is arguable one can
limit his learning to a restricted subset of FOL that is decidable).
On the other hand, Horn-clause first order logic offers a much more
attractive and viable alternative -- it has a non-trivial amount of
expressive power but yet learning is generally more feasible (PAC-learning
wise). His interest in learning in this framework has led him to contributing
(however little) to a still growing subfield in Machine Learning
called Inductive Logic Programming (ILP).
His previous works in ILP include its application to Natural Language Understanding
for automated construction of natural language interfaces (NLI) for databases, and
Link Discovery for building counter-terrorism applications (a huge data mining
problem).
He is currently working on building reliable NLI (i.e. one that is guaranteed to
deliver only correct answers to the user) using a hybrid approach that combines
the strength of machine learning and non-machine learning approaches. He has
recently become a member of the LIGO Scientific Collaboration and is involved in
data mining of astronomical data. His involvement in the project led him to
developing new clustering algorithms. His current focus is in tackling the
problem of cluster validation using a homogeneity detection approach (i.e. one that
detects if a cluster is homogeneous) instead of traditional approaches that
employ a cluster validation index where the problem is then basically treated
as an optimization problem.
His other areas of interest also include Genetic Programming, and Functional Genomics
in Bioinformatics.
Here is his research statement (somewhat outdated at the
moment but it will be updated soon, sorry for any inconvenience). :-)
Job Openings
If you are interested in obtaining research assistantship from Dr. Tang, please
contact him as soon as possible by email.
- Project on Funds for Improvement of Post-Secondary Education (FIPSE),
U.S. Department of Education:
There is currently one position available for an
undergraduate research assistant.
- Project on Centers of Research Excellence in Science and Technology (CREST),
National Science Foundation:
There is currrently one position available for a
graduate research assistant
and one for an
undergraduate research assistant.
Courses
COSC 4346: Software Engineering I