|
Home
Organized by:
Co-organized by:
In
Cooperation with:
The
Japanese Society for Artificial Intelligence
|
|
|
PAKDD
School
Tentative Programme
Date: |
Saturday, April 8, 2006 |
|
|
Venue: |
Microsoft Singapore Pte. Ltd.
1 Marina Boulevard
22 Floor, One Marina Boulevard
Singapore 018989 |
8.00 am - 8.30 am |
Registration |
8.30 am - 10.00 am |
Introduction to Data Mining (Koh Hian
Chye) |
10.00 am - 10.30 am |
Tea Break |
10.30 am - 12.00 noon |
Hands on session (Koh Hian Chye) |
12.00 noon - 1.00 pm |
Lunch & Posters |
1.00 pm - 2.00 pm |
Advanced Topic I (David Hand) |
2.00 pm - 3.00 pm |
Advanced Topic II (Wang Ke) |
3.00 pm - 3.30 pm |
Tea Break & Posters |
3.30 pm - 4.30 pm |
Advanced Topic III (Barry Shepherd- tentative) |
4.30 pm - 6.00 pm |
Panel Discussion (All speakers) |
Biography
Koh, Hian Chye
Dr Koh Hian Chye is an Associate Professor and
Dean of the School of Business at SIM University
(UniSIM). He has published widely in international
and regional journals and conferences on topics
related to accounting and auditing, business
and management, statistical modelling and data
mining. His current interest is in the business
applications of data mining. Dr Koh frequently
acts as a consultant to private companies, large
organisations and government agencies. His recent
consultancy projects involve data mining applications.
He also serves as a trainer in workshops and
executive programmes. Dr Koh has recently written
a book entitled "Data Mining Applications
for Small and Medium Enterprises", which
is available at branches of the National Library
in Singapore.
Hand, David
David Hand is Professor of Statistics and Head
of the Statistics Section at Imperial College
London. He has published over twenty books on
statistics and related areas, including Principles
of Data Mining. He launched the journal Statistics
and Computing, and served a term of office as
editor of Journal of the Royal Statistical Society,
Series C. He was awarded the Thomas L. Saaty
Prize for Applied Advances in the Mathematical
and Management Sciences in 2001, the Royal Statistical
Society's Guy Medal in Silver in 2002, the IEEE
International Conference on Data Mining award
for Outstanding Contributions in 2004, and was
elected Fellow of the British Academy in 2003.
He acts as a consultant to a wide range of organisations,
including governments, banks, pharmaceutical
companies, manufacturing industry, and health
service providers.
Shepherd, Barry
Dr Shepherd has over twenty years of software
development and consulting experience within
the IT industry. He began his career in the
UK developing real-time software for GEC-Marconi
and conducting research in applied Artificial
Intelligence (AI) at Edinburgh University and
the Turing Institute. In 1993, he joined ISS
as manager of the Knowledge Engineering Program
and helped design and launch the Masters degree
in Knowledge Engineering as well as lecture
and consult in data mining, knowledge-based
systems and optimisation techniques. In 2000,
he became Vice President of technical marketing
at Cygron, a Singapore-based company developing
data mining tools and solutions. He now operates
as an independent consultant conducting projects
and training in customer analytics, data mining
and intelligent systems. He works with numerous
partners and clients across S.E. Asia and the
UK. His recent projects include customer segmentation,
building propensity models for direct marketing
campaigns and sales data analysis
Wang, Ke
Ke Wang received Ph.D from Georgia Institute
of Technology. He is currently a professor at
School of Computing Science, Simon Fraser University.
Before joining Simon Fraser, he was an associate
professor at National University of Singapore.
He has taught in the areas of database and data
mining.
Ke Wang's research interests include database
technology, data mining and knowledge discovery,
machine learning, and emerging applications,
with recent interests focusing on the end use
of data mining. This includes explicitly modeling
the business goal (such as profit mining, bio-mining
and web mining) and exploiting user prior knowledge
(such as extracting unexpected patterns and
actionable knowledge). He is interested in combining
the strengths of various fields such as database,
statistics, machine learning and optimization
to provide actionable solutions to real life
problems. Ke Wang has published in database,
information retrieval, and data mining conferences,
including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT,
SIGKDD, SDM and ICDM. He is an associate editor
of the IEEE TKDE journal and has served program
committees for international conferences including
DASFAA, ICDE, ICDM, PAKDD, PKDD, SIGKDD and
VLDB.
|
|
|
|