I am now an assistant professor at the Hong Kong Polytechnic University.
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Edward Hung is
an assistant professor at the Department of
Computing, the Hong Kong Polytechnic University.
He received his B.Eng. in Computer Engineering and M.Phil. in Computer
Science from the University of Hong Kong in 1998 and 2000 respectively.
He obtained
his M.S. degree
and Ph.D. degree
from the Department of
Computer Science,
the University of Maryland, College Park (UMCP)
in May 2002 and May 2005.
He was supported by
Scholarships from the Croucher Foundation and research assistantship for
his Ph.D. research during
2000 - 2003 and 2003 - 2005
under supervision of Prof. V.S. Subrahmanian.
He
was awarded a number of scholarships and awards, including
Jacob K. Goldhaber Travel Grant Award, Epson
Foundation Scholarship, the Hong Kong and China Gas Company Limited
Postgraduate Scholarship, Stephen Kam-Chuen Cheong Memorial Scholarships,
and the Hong Kong Institution of Engineers Student Prize, etc.
He has
six conference full papers, two journal articles and a book chapter related to
RDF aggregates and view maintenance,
recall improvement in XML queries by ontologies and similarities,
probabilistic semistructured databases,
data warehousing, parallel data
mining and E-commerce.
More
information about his research and publications can be accessed from
http://www.comp.polyu.edu.hk/~csehung
Current collaborators: Prof. V.S. Subrahmanian,
Dr. Lise Getoor,
Dr. Duminda Wijesekera, Dr. David W. Cheung,
Prof. Sarit Kraus,
Yu Deng,
Nazif Cihan Tas,
Octavian Udrea
Previous collaborators (with publication): Prof. Ben Shneiderman, Dr Ben
Kao, Prof. Hongjun
Lu, Dr. T.W. Lam, Dr. H.F. Ting,
Yilong Liang,
Haixia Zhao, Irina Ceaparu, Dina Demner
Honor Membership
Member of the Honor Society of Phi Kappa Phi
Member of Swire Scholar Association
ACM Student Member
G.P.A. in Ph.D in UMCP = 4.0/4.0
M.S. in Computer Science (5/2002).
G.P.A. in M.S. in UMCP = 4.0/4.0
M.Phil. in Computer Science (7/2000).
G.P.A. in M.Phil. in HKU = 4.0/4.0
B.Eng. in Computer Engineering (6/1998).
G.P.A. in B.Eng. in HKU = 3.87/4.0 (overall), 3.89/4.0 (major), 3.89/4.0
(last 2 years).
1 Publication in 2005:
a conference research paper in ICDE
2 Publications in 2004:
a conference research paper in SIGMOD,
a journal article in JIIS (Journal of Intelligent
Information Systems)
2 Publications in 2003:
a conference research paper in ICDT 2003, a conference research paper in
ICDE
2003
2 Publications in 2002:
a journal article in DAPD (Distributed and
Parallel Database),
a book chapter in E-Service
1 Publication in 2000: a conference paper in PAKDD 2000
1 Publication in 1999: a conference paper in IDC'99
7 Papers under review:
3 journal articles,
4 conference papers
Group
DBChat
Data Mining Reading Group
Graph Mining and Link Analysis Reading Group
June 17, 2004: Speaker for the following conference paper:
Edward Hung, Yu Deng, V.S. Subrahmanian,
"TOSS: An Extension of TAX with Ontologies and Similarity Queries",
in the Proceedings of the 23rd ACM SIGMOD International Conference on
Management of Data, Paris, France, June 13-18, 2004.
(powerpoint)
Jun 30, 2003, Invited Talk
in Database Seminar, Department of Computer Science and Information
Systems, University of Hong Kong, Hong Kong.
Title: PXML: A
Probabilistic Semistructured Data Model and Algebra
(powerpoint)
March 8, 2003: Speaker for the
following accepted paper:
Edward Hung, Lise Getoor,
V.S. Subrahmanian, "PXML: A Probabilistic Semistructured Data
Model and Algebra", in the Proceedings of the 19th
International Conference on Data Engineering (ICDE),
Bangalore,
India, March 5-8, 2003. (acceptance rate = 51/378 = 13%)
(ps
format)
(powerpoint)
2:30pm, Jan 10, 2003: Speaker for the following accepted paper:
Sep 24, 2002, Talk in DBChat,
Department of Computer Science, University of Maryland, College Park, US
Title:
PXML: A Probabilistic Semistructured Data Model and Algebra
Abstract:
Despite the recent proliferation of work on semistructured data
models, there has been little work to date on supporting
uncertainty in these models. In this paper, we propose a model for
probabilistic semistructured data (PSD). The advantage of our
approach is that it supports a flexible representation that allows
the specification of a wide class of distributions over
semistructured instances.
We provide two semantics for the model and show that the semantics
are probabilistically coherent. Next, we develop an extension of
the relational algebra to handle probabilistic semistructured data
and describe efficient algorithms for answering queries that use
this algebra. Finally, we present experimental results showing the
efficiency of our algorithms.
Aug 13, 2002, Invited Talk in Database Seminar,
Department of Computer Science and Information System,
University of Hong Kong, Hong Kong
Apr 22, 2002, Invited Talk in Uncertain Reasoning Seminar,
Department of Computer Science, University of Kentucky, Lexington,
Kentucky, US
Title:
PXML: Probabilistic Semistructured Databases
Abstract:
Recent interest in semistructured data has led to a proliferation of
XML-based standards which are applicable in many domains ranging from
multimedia applications and sensor data processing applications to myriads
of other more traditional applications. When semistructured paradigms are
used to store information such as sensor data and multimedia (e.g. image)
data, we need to be able to handle uncertainty as sensor readings and
image processing methods often yield uncertain answers. PXML is a project
we are now working to develop probabilistic semistructured databases. In
this talk, I will describe the concept and explain the semantics of the
model with a number of examples. I will also describe the algebra we are
now developing to extend the relational algebra and expand upon previously
developed algebras for semistructured data (without uncertainty).
