Kooksang Moon
Sequence Analysis and Modeling (SeqAM) Lab.
Department of Computer Science
Rutgers, The State University of New Jersey
110 Frelinghuysen Road, Piscataway, NJ 08854
Email: ksmoon@cs.rutgers.edu
Phone: +1 732 445-2001 Ext 9578
URL: http:/www.cs.rutgers.edu/~ksmoon
Research Interests
 Computer vision and pattern recognition
 Machine learning
 Human-computer interaction
Education
Rutgers, the State University of New Jersey, Piscataway, NJ
Ph.D. (expected in Oct.2008), Computer Science
Advisor: Prof. Vladimir Pavlovic
Jan.2002-Present
The State University of New York at Buffalo, Buffalo, NY
M.S., Computer Science & Engineering
Advisor: Prof. Venu Govindaraju
Aug.1999-May.2001
Yonsei University, Seoul, Korea
B.S., Electrical Engineering
Mar.1992-Feb.1996
Experience
 Graduate Research Assistant, Sequence Analysis and Modeling Lab (SeqAM), Rutgers
University, Piscataway, NJ, Aug.2003-Present.
 Intern, Quantitative Research Team, Gargoyle Strategic Investments LLC, Englewood, NJ,
Jun.2008-Present.
 Intern, Intelligent Vision and Reasoning Department, Siemens Corporate Research (SCR), Inc.,
Princeton, NJ, May.2007-Aug.2007.
 Visiting Student, the Laboratory for Language and Media Processing Lab (LAMP), University of
Maryland – College Park, College Park, MD, Aug.2001-Dec.2001.
 Research Assistant, Center of Excellence for Document Analysis and Recognition (CEDAR), the
State University of New York at Buffalo, Buffalo, NY, Dec.1999-Jun.2001.
Research Experience
 Research at SCR
o Filter Learning, 2007.
 Designed a framework for image filtering based on regression with large dataset.
 Used MATLAB and C.
 Research at Rutgers
o Sufficient Dimensionality Reduction for Regression, 2007-present.
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 Developing a kernel dimensionality reduction method preserving information relevant for
a general nonlinear regression. The approach called Gaussian process manifold kernel
dimensional reduction (GPMKDR) is induced by reformulating the manifold kernel
dimensional reduction in the Gaussian process framework and the solution is given by the
maximum eigenvalue-eigenvector solution to a kernelized problem.
 Using MATLAB.
o Monocular 3D Human Motion Tracking, 2006-2007.
 Developed the statistical framework for modeling and tracking of the 3D human figure
motion from monocular video which utilizes the dynamic probabilistic latent semantic
analysis (DPLSA) model describing the mapping of image features to 3D human pose
estimates via latent variable.
 Used MATLAB.
o Estimating and Recognizing 3D Articulated Motion via Uncalibrated Cameras, 20032006.
 Worked as the graduate research assistant for developing human motion tracking system
with monocular camera on a NSF funded project.
 Developed the probabilistic framework for modeling and tracking of the 3D human figure
motion from a sequence of monocular images with subspace embedding, in which the
dynamics in latent space is utilized using Marginal Auto-Regressive (MAR) model and
the embedding model is defined using Gaussian process latent variable model (GPLVM).
 Developed the 2D-based articulated object tracking method which utilizes a layered
representation approximating the true 3D link relationships and a robust parametric
statistical representation of the link appearance.
 Used MATLAB and C.
o Sensor, Database and Algorithm Development for Face and Gait-based Deception
Analysis, 2005-2006.
 Worked as the graduate research assistant for developing gait-based deception analysis
system on a HSARPA (Homeland Security) funded project.
 Designed shoulder tracking module detecting the texture boundary of shoulder and
analyzing the shoulder movement with a Hidden Markov model.
 Used MATLAB and C++ on MS Visual Studio.
 Research at LAMP
o Tracking text in digital video, 2001.
 Designed the edge-based text detection method using morphology-based operation and
density map to supplement the drawback of texture-based text detection.
 Used C on Solaris.
 Research at CEDAR
o Learning binary feature similarity measure for OCR using genetic algorithm, 2001.
 Developed the new distance measure for Gradient, Structural, and Concavity (GSC)
binary features, in which GSC features are regarded as a compounded feature with
different weights representing the various effects of individual feature groups and the
optimal weights can be learned effectively through Genetic algorithm.
 Used MATLAB and C on Solaris.
o Sequential pattern mining in text database, 2001
 Implemented the system to discover the trend in the database of research paper abstracts
by using classical sequential pattern mining technique with an efficient keyword
extraction method.
 Used Perl on Solaris.
o United Kingdom Address Interpretation (UKAI), 1999-2001.
Kooksang Moon
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 Worked as the research assistant for grafting the Handwritten Address Interpretation
(HWAI) system to read handwritten addresses for UK’s Royal Mail on a LockheedMartin funded project.
 Designed stamp mark removal algorithm using density histogram in text line
segmentation module.
 Designed underline detection and removal module for mail piece image preprocessing by
utilizing morphology operation and modified Hough transform.
 Used C on MS Visual Studio.
Publications
1. K. Moon and V. Pavlovic, “Gaussian Process Manifold Kernel Dimensionality Reduction,”
submitted to Conference, 2008.
2. K. Moon and V. Pavlovic, “Monocular 3D Human Motion Tracking Using Dynamic
Probabilistic Latent Semantic Analysis,” Proc. Canadian conference on Compute and
Robot Vision, Ontario, Canada, 2008.
3. K. Moon and V. Pavlovic, “Graphical Models for Human Motion Modeling,” book chapter
in Human Motion Capture: Modeling, Analysis, Animation, Metaxas, Rosenhahn and
Kleete Eds., Springer, 2007
4. K. Moon and V. Pavlovic, “Impact of Dynamics on Subspace Embedding and Tracking of
Sequences,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
New York, USA, 2006.
5. K. Moon and V. Pavlovic, “Robust Tracking of Articulated Layers,” IEEE International
Workshop on Vision for HCI in conjunction with CVPR, San Diego, USA, 2005.
Professional Activities and Affiliations
 Reviewer for IEEE ICPR ’04, ICMI ’04, IEEE ICCV ’05, ECCV ’06, IEEE ICPR ’06, IEEE
ICCV ’07, CVPR’08, CVIU (Computer Vision and Image Understanding) Journal.
 Member of IEEE, 1999-Present.
 Member of Korean Science & Engineering Association (KSEA), 2002-Present
Teaching Experience
 Introduction to Computer Science, undergraduate course, Fall, 2006-Spring, 2008.
o Teaching a recitation class on Java programming language.
Technical Skills
 Language C/C++, MATLAB, Java, Perl, VBA, HTML, Pascal
 Tools MS Visual Studio, MS Office, Eclipse, Alias Maya, Oracle, Macromedia Studio
 Operating System MS Windows 98/2000/NT/XP/Vista, Solaris, Linux
References
Kooksang Moon
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Dr. Vladimir Pavlovic
Assistant Professor
Dept. of Computer Science
Rutgers University
110 Frelinghuysen Road
Piscataway, NJ 08854
Email: vladimir@cs.rutgers.edu
Phone: +1 732 445 2654
Dr. Ilya Muchnik
Research Scientist
DIMACS
Rutgers University
P.O. Box 8018
Piscataway, NJ 08855
Email: muchnik@dimacs.rutgers.edu
Phone: +1 732 445 0073
Dr. Greg Slabaugh
Research Scientist
Intelligent Vision and Reasoning Dept.
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
Email: greg.slabaugh@siemens.com
Phone: +1 609 734 3350
Dr. Sung-Hyuk Cha
Assistant Professor
School of Computer Sc. & Information Sys.
Pace University
861 Bedford Road
Pleasantville, NJ 10570
Email: scha@pace.edu
Phone: +1 914 773 3891