2006 Graduate Catalog and 2005 Annual R & D Report - Sirindhorn ...

2006 Graduate Catalog and 2005 Annual R & D Report
Sirindhorn International Institute of Technology (SIIT)
(2) Text Interpretation: Information Retrieval,
Categorization and Information Extraction
In the past, most online information stored in
databases or spreadsheets. At the present time, the
majority of online information is text-based, e.g., e-
mail, news, journal articles, reports, books,
encyclopedias. These information sources are worth
but there is too much information available, and not
enough time to sort through it. Text interpretation
techniques are helpful for categorizing, filtering and
extracting information from text. Three types of text
interpretation are information retrieval, categorization,
and information extraction. We are interested in
developing of efficient methods to various tasks of
text interpretation.
Knowledge Science and Engineering
(1) Knowledge Data Discovery in Database
Knowledge Data Discovery (KDD) is a rapidly growing
interdisciplinary field that merges together databases,
statistics, machine learning and other AI technologies
in order to extract useful knowledge from a largescaled
collection of data. The problems in this field
are of two general categories: (1) prediction and (2)
knowledge discovery. Knowledge discovery is a stage
prior to prediction, where information is insufficient for
prediction, such as clustering, association rules, text
mining and so on. Our study aims at finding and
implementing efficient, robust and scalable methods
in real-world situation where databases are complex,
voluminous, noisy and non-stationary. Some
interesting applications include computer-aided
education (CAI), decision support systems, and
management information systems.
(2) Intelligent Decision Support Systems
In business, government, and other organizations,
decision making plays an important part in
determining the landscape of tomorrow’s world.
Computer systems that assist decision-making
process are called decision support systems (DSSs).
Intelligent decision support systems (IDSSs) are
DSSs that make use of techniques emerging from the
field of artificial intelligence (AI). Our research focuses
on studying new techniques in both (1) model-driven
support systems, which are based on strong theory or
model, and (2) data-driven support systems, which
are based on database technologies and statistical
methods.
School of Management Technology
Faculty Members and Research Interests, 2006
Dr. Chawalit Jeenanunta
Lecturer
B.S. in Computer Science, University of Maryland, USA
B.S. in Mathematics, University of Maryland, USA
M.S. in Management Science, University of Maryland, USA
Ph.D. in Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, USA
Research Areas: Linear programming, Integer programming, Network optimization, Simulation.
Research Interest:
Large-Scale Simulation and Optimization
Many problems in the real world are large and
complex. Researchers in this field are trying to
improve the algorithm and utilize available
computational technology such as parallelism or grid
computing to solve such problems where their
resulting models are also very large. This technology
also enables researchers to have a detail model
which is close to the real world problem. Some
examples of these problems are transportation
problem in the urban area (where there consist of
millions of people driving on thousands of streets),
financial simulation, and bioinformatics.
Dr. Jittima Thong U-rai
Lecturer (Joined SIIT in June 2006)
B.B.A. in International Business Management, Assumption University, Thailand
B.Econ. in Monetary Economics, Ramkhamhaeng University, Thailand
M.Econ. in International Finance, Oita University, Oita, Japan
Ph. D. of Commerce in Development Finance, Osaka City University, Osaka, Japan
Areas of Specialization: Financial Institutions and Markets, Financial Liberalization, International Capital Flow,
and Foreign Exchange.
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