| Research Interests
Data
mining
and machine learning: theory,
algorithms, applications, Bioinformatics, Biomedical
Informatics, Health
Informatics: functional genomics,
chemical genomics,
systems
toxicology, human genetics
My research interest
is
to develop and apply data mining and machine learning techniques to
accelerate knowledge discovery in science and engineering disciplines
including medicine.
Our current focus is to advance the understanding of biological systems
at three levels. At
the molecular level, we focus on mapping out biomolecule interactions
at the
whole genome level and identifying the
connections
between biomolecule interactions and clinic endpoints such as disease
diagnostics
and personalized medicine development. At the system level,
we focus on identifying
the dynamic control mechanisms of complex systems to improve the
modeling and engineering of biological systems. At the population
level, we focus on identifying disease biomarkers for
disease diagnostics, prognostics, and treatment. In our
investigations
we
rely on high-throughput and low-throughput experimental data. These
data
include protein sequences and structures, chemical structure-activity
profiles, gene expression profiles, protein-ligand interactions,
biological pathways, DNA copy number variations, single nucleotide
polymorphisms (SNPs), chemical
toxicity, and disease phenotypes. The applications of
our
work could be found in Immunology, Neurology,
Toxicology, and Drug Design, such
as:
- Protein functional annotation, including predicting
enzymatic
functions
and molecular recognition events
- Fold recognition of protein sequences, especially
pathological
genes
- Leads optimization and ADME-Tox prediction in drug
screening
- Chemical probe
identification
in Molecular Libraries Probe
Production
Centers (MLPCN)
- Biological system identification for
chemical toxicity pathway reconstruction
- Disease biomarker discovery
Though the problem set
is diverse, the common threads of our computational work are geometric
and
probabilistic representations
of biomolecules and their interactions, sparse pattern search in vector
and
kernel spaces, and learning generative and discriminative models with
regularization.
Much of our work addresses two core problems in data mining: stable
pattern identification and mining structured data in Non-Euclidian
spaces.
Publications
Recent Publications
Awards
Best Paper Award Runner-Up, ACM CIKM, 2009 (6/123 accepted
papers)
National Science Foundation CAREER
Award
(IIS 0845951, 2009 - 2014)
Current Supports
Teaching
Graduate courses:
EECS 831 Introduction to Systems Biology (Spring
2010)
EECS 800 Pattern Discovery from Data (Spring 2008)
EECS 730 Introduction
to
Bioinformatics (Fall 2007, Fall 2008, Fall 2009)
EECS 800 Mining Biological Data (Fall
2006)
Undergraduate courses:
EECS 647 Introduction to
Database
Systems (Spring 2007, Spring 2008, Spring 2009)
EECS 560 Data Structures (Fall 2008)
EECS 168 Programming I (Spring 2010)
Professional Services
- Program Committee Member, the 26th IEEE International Conference on
Data
Engineering (ICDE'10), Long Beach, CA, March, 2010.
- Program Committee
Member,
the 9th IEEE International
Conference
on Data Mining (ICDM'09), Miami, Florida, December, 2009.
- Program Committee Member, the 18th
ACM
International Conference on Information and Knowledge Management
(CIKM'09), Hong Kong, November, 2009.
- Program Committee
Member,
the IEEE
International
Conference on Bioinformatics & Biomedicine (BIBM'09),
Washington
DC, November, 2009.
- Program Committee Member, the 15th ACM International
Conference
on Knowledge Discovery and Data Mining (SIGKDD'09), Paris, France,
June,
2009.
- Program Committee Member, the 25th IEEE International Conference
on
Data Engineering (ICDE'09), Shanghai, China, April, 2009.
I am co-organizing the 3rd ACM
International
Workshop on Data and Text Mining in Bioinformatics (DTMBIO'09), in
conjunction
with the 18th ACM International Conference on Information and Knowledge
Management (CIKM'09), Hong Kong, November, 2009. Here is the
call
for paper and please follow the url to submit high quality research
papers.
Selected papers will be invited for a special issue of BMC
Bioinformatics.
Intelligent
Informatics Tea is a weekly event in ITTC @ KU for people to
discuss
the latest progresses in broadly defined informatics research. Please
feel free to stop by
if you wan to participate in the discussion.
Past Services
Current
Students
Fei,
Hongliang
|
Ph.D.
Student, CS, Fall 2007-
|
Jia,
Yi
|
Ph.D.
Candidate, CS, Fall 2006-
|
Mishra,
Meenakshi
|
Ph.D. Student, CS,
Spring 2010-
|
Quanz,
Brian
|
Ph.D.
Student, CS, NSF Graduate Research Fellow,
2009-2012, Fall 2007-
|
Smalter,
Aaron
|
Ph.D. Candidate, CS,
Spring 2007-
|
Zhang,
Jingtao
|
Ph.D.
Candidate, Bioinformatics Program, Fall 2007-
|
Jiang,
Ruoyi
|
M.S.
Student,
EE, Fall 2009-
|
Wang, Xiaohong
|
M.S. Student, CS,
Fall
2008-
|
Wu, Michael
|
B.S. Student, CS,
Fall 2009-
|
Alumni
Seak
Lei, Fei
|
M.S.
Student, CS, 2008
|
Peddi,
Abhinav
|
M.S.
Student,
CS, 2008 |
Visiting Scholar
Choi,
Kwang
Nam
|
School
of
Computer Science & Engineering, Chung-Ang University, 2008 - 2009 |
Available positions
Short Bio
Dr. Jun
(Luke) Huan has been an assistant professor in the Electrical
Engineering and Computer
Science department at the University of Kansas since 2006. He is an
affiliated
member of the Information and Telecommunication Technology Center
(ITTC),
Bioinformatics Center, Bioengineering Program, and the Center for
Biostatistics
and Advanced Informatics—all KU research organizations. Dr. Huan
received
his Ph.D. in Computer Science from the University of North Carolina at
Chapel
Hill in 2006. Before joining KU, he worked at the Argonne National
Laboratory
(with Ross Overbeek) and the GlaxoSmithKline plc. (with Nicolas Guex).
Dr. Huan
was
a recipient of the NSF Faculty Early
Career Development (CAREER) Award in 2009. He serves on the
program committees
of leading international conferences including ACM SIGKDD, IEEE ICDE,
ACM
CIKM, IEEE ICDM.
|
