| Research Interests
Data
mining and machine learning: theory,
algorithms, &
applications, Bioinformatics and Biomedical Informatics: functional genomics,
chemical genomics, metabolomics, &
systems
biology
My research
interest
is to develop and apply data mining and machine learning algorithms to
accelerate
knowledge discovery in science and engineering disciplines including
medicine. Our current
focus is on exploring biological systems at two 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. 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 variation, single nucleotide polymorphism (SNP),
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 enzymatic functions and protein-ligand
interactions prediction
- Fold recognition of protein sequences, especially
pathological genes
- Leads optimization and ADME-Tox prediction in drug
screening
- Chemical probe identification
- Biological system identification, including
disease pathway and chemical toxicity pathway reconstruction
Though the problem set
is
diverse, the common threads of our
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
National Science Foundation CAREER
Award (IIS 0845951, 2009
- 2014)
Teaching
Graduate courses:
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)
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
Interational Workshop on Data and Text Mining in Bioinformatics
(DTMBIO'09), in conjuction 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.
Bioinformatics
Tea
is a weekly event in EECS @ KU for people to discuss the latest
progresses in Bioinformatics 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 |
Jia,
Yi
|
Ph.D.
Candidate, CS |
Quanz,
Brian
|
Ph.D.
Student,
CS, NSF Graduate Research Fellow,
2009-2012
|
Smalter,
Aaron
|
Ph.D. Candidate, CS
|
Zhang,
Jingtao
|
Ph.D.
Candidate, Bioinformatics
Program
|
Wang,
Xiaohong
|
M.S.
Student, CS
|
Fernando, Avindra
|
B.S. Student, CS
|
Quillen, Kevin
|
B.S. Student, CS
|
Wu, Michael
|
B.S. Student, CS
|
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
GlaxoSmithKline (with Nicolas Guex). Dr. Huan was a
recipient of the NSF Faculty Early
Career
Development
(CAREER) Award in 2009. He received the Scholar of Tomorrow
Fellowship (2001)
and the Alumni
Fellowship (2005) both from
the
University of North Carolina at Chapel Hill. He serves on the program
committees of leading international conferences including ACM SIGKDD,
IEEE ICDE, ACM CIKM, IEEE ICDM, and IEEE BIBM.
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