The pdf version of the cv is here. (latest updated: August 12, 2012)
kittipat@gmail.com ⋅ https://sites.google.com/site/kittipat/projects ⋅ http://kittipatkampa.wordpress.com
OBJECTIVE
To gain employment with a company or an institute where my technical and entrepreneurial skills, experience, creativity and knowledge, especially in the area of mathematics, machine learning, data mining and programming, can be utilized at the fullest.
AREAS OF EXPERTISE
Statistical machine learning, predictive model, classification, regression, unsupervised learning, information-theoretic learning (ITL) [1, 2] and probabilistic graphical models. Recent applications include neuroscience [3, 4], computer vision[5, 6], data fusion [7, 8, 9, 10] and data mining.
WORK EXPERIENCE
Integrated Brain Imaging Center (IBIC), University of Washington Medical Center, WA. (2011 to present)
Postdoctoral associate and machine learning scientist.
Develop machine learning systems, mathematical and predictive models to understand more about human brain functionality in order to .nd a cure for brain disease.
Department of Electrical and Computer Engineering, University of Florida, FL. (2004 to 2011)
Graduate research assistant (Master and PhD).
Develop machine learning systems, focusing on probabilistic graphical models and information-theoretic learning, for natural image segmentation, 3D LiDAR infographic analysis, data fusion in SONAR, outlier detections.
USDA-ARS-Southwest Watershed Research Center (SWRC), AZ. (Fall Intern 2006)
Machine learning scientist and software developer
Automatic 3D-LiDAR point cloud processing, filtering and recognition,
Adaptive signal processing on hydrological data,
Integrated-circuit Design and Applications Research (IDAR), Bangkok, Thailand. (2001 to 2003)
Embedded system R&D Engineer
Develop hardware and firmware for digital high-accuracy measuring instruments
Intronics Co, Ltd., Bangkok, Thailand. (Summer Intern 2000)
Embedded system R&D Engineer
Develop hardware and firmware for testing electronics device in manufacturing processes
SKILLS
Programming: Python, Java, proficient in MATLAB®
Mathematics and statistics: numerical/meta-heuristic/biologically-inspired optimization, Bayesian theory, information theory, graph/social network theory
Remote sensing: airborne/terrestrial 3D LiDAR laser scanning, multi-spectral image, GIS software/tools
EDUCATION
University of Florida, Gainesville
Ph.D. Electrical and Computer Engineering (2011)
M.S. Electrical and Computer Engineering (2006)
Chulalongkorn University, Bangkok, Thailand
B.S. Electrical and Computer Engineering (2001)
EDUCATIONAL SERVICES
My machine learning projects/hobbies Wikipage – https://sites.google.com/site/kittipat/projects
Free math/science/machine learning tutorial channel “ChaLearn Academy”:
Homepage: https://sites.google.com/site/chalearnacademy/
Facebook page: https://www.facebook.com/ChaLearnAcademy
SELECTED GRADUATE-LEVEL COURSES
Machine Learning and Computer Vision: Advanced Topics on Machine Learning, Information-Theoretic Learning (ITL), Mathematics for Intelligence Systems, Computer Vision and Image Processing, Pattern Recognition.
Mathematics and Statistics: Numerical Optimization, Numerical Linear Algebra, Meta-Heuristic Optimization, Introduction to Probability Theory I and II, Spatial Statistics.
TEACHING EXPERIENCE
Running the whole course, University of Florida
Spring 2010 Pattern Recognition (graduate-level): theory and applications of pattern recognition.
Teaching assistant and guest lecturer, University of Florida
Spring 2006 to 2009 Pattern Recognition (graduate-level): theory and applications of pattern recognition.
Fall 2007 Airborne Sensors and Instrumentation (graduate-level): fundamental remote sensing applications, data processing using pattern recognition approach.
Teaching assistant and guest lecturer, Chulalongkorn University
Spring 2001 Introduction to Microprocessor (undergrad-level)
Math tutor:
Special program for high school mathematics Olympiad at Triam Udom Suksa (1996)
Special program for US high school mathematics competitions in Gainesville, FL (2011)
Free tutorial website “ChaLearn Academy” for motivation to learn mathematics, science and machine learning. (2012-present)
ACHIEVEMENTS
2005 Member of Eta Kappa Nu (HKN) honor society
2003 Received a Distinguished Alumni Award from Chulalongkorn University
2001 Invention was broadcast to the public on several nation-wide TV programs
2001 Awarded a grant for the second prize in Thailand Innovation Award
2000 Received a Prince of Thailand research scholarship from the Engineering Institute of Thailand
1996 Attained the highest score in Physics in the National Examination
1996 Received a Silver Medal Award in the Thailand Mathematics Olympiad
COLLABORATIONS
Computational NeuroEngineering Laboratory (CNEL)
Adaptive Signal Processing Laboratory (ASPL)
Geosensing Engineering and Mapping Center (GEM)
National Center for Airborne Laser Mapping (NCALM)
PROFESSIONAL SERVICE – Reviewer (* indicates assistant reviewer)
IEEE Signal Processing Letters (SPL)
IEEE Transactions on Image Processing Journal (TIP)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)*
The International Conference on Artificial Neural Networks (ICANN 2010, 2011)
IEEE International Conference on Systems, Man, and Cybernetics (SMC 2009)
IEEE International Conference on Computer Vision (ICCV 2009)
IEEE Transactions on Signal Processing
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004, 2008, 2009, 2010)
IEEE International Conference on Image Processing (ICIP 2005 - 2007)
IEEE Geoscience and Remote Sensing Society (IGARSS 2006)
RECENT PUBLICATIONS
[1] K. Kampa, E. Hasanbelliu, and J. C. Principe, “Closed-form cauchy-schwarz pdf divergence for mixture of gaussians,” in Proc. of the 2011 International Joint Conference on Neural Networks (IJCNN), 2011.
[2] E. Hasanbelliu, K. Kampa, J. Principe, and J. T. Cobb, “Surprise metric for online learning,” in Proc. SPIE Defense and Security Symposium, vol. xxxx-xx, Orlando, FL, Apr. 2011.
[3] K. Kampa, C. Chou, S. Mehta, R. Tungaraza, W. Chaovalitwongse, and T. Grabowski, “Enhancement of fmri pattern classification with mutual information,” Radiology Research Symposium, vol. x, pp. xxx–xxx, 2012.
[4] C. Chou, K. Kampa, S. Mehta, R. Tungaraza, W. Chaovalitwongse, and T. Grabowski, “Information theoretic based feature selection for multi-voxel pattern analysis of fmri data,” Brain Informatics, vol. x, pp. xxx–xxx, 2012.
[5] K. Kampa, D. Putthividhya, J. Principe, and A. Rangarajan, “data-driven tree-structured bayesian network for image segmentation,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. x, 2012, pp. xxx–xxx.
[6] K. Kampa, D. Putthividhya, and J. Principe, “Irregular tree-structured bayesian network for image segmentation,” in Proceedings of the 2011 International Workshop on Machine Learning for Signal Processing (MLSP 2011), vol. x, 2011, pp. xxx–xxx.
[7] K. Kampa, K. Slatton, and J. Cobb, “Dynamic trees for sensor fusion,” in IEEE International Conference on Systems, Man and Cybernetics (SMC), 11-14 2009, pp. 2751 –2756.
[8] K. Kampa, J. C. Principe, and K. C. Slatton, “Dynamic factor graphs: A novel framework for multiple features data fusion,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2010.
[9] K. Kampa, J. Principe, J. T. Cobb, and A. Rangarajan, “Deformable bayesian networks for data clustering and fusion,” in Proc. SPIE Defense and Security Symposium, vol. 8017-25, Orlando, FL, Apr. 2011.
[10] K. Kampa, E. Hasanbelliu, J. Cobb, J. Principe, and K. Slatton, “Deformable bayesian network: A robust framework for underwater sensor fusion,” Oceanic Engineering, IEEE Journal of, vol. 37, no. 2, pp. 166–184, 2012.
RECENT AND SELECTED PROJECTS
UNPUBLISHED PROJECTS
INVITED TALKS AND GUEST LECTURERS
2012
“Overview of machine learning for multi-voxel pattern analysis (MVPA): Gaussian Naive Bayes, LDA, logistic regression and SVM” MVPA interest-group, Integrated Brain Imaging Center, University of Washington.
2011
“Data-driven tree-structured Bayesian network for unsupervised image segmentation”, University of Florida
2010
“Probabilistic Graphical Models: Hidden Markov Models, Bayesian networks, Markov Random Fields and Conditional Random Fields” , University of Florida
“Basic Optimization Methods for Machine Learning” , University of Florida
“Generative and Discriminative Models for Classification” , University of Florida
“Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)”, University of Florida
“Bayesian and Beyond”, University of Florida
2009
“Dimensionality Reduction for Machine Learning”, University of Florida
“Graphical Models: Message Passing Algorithms in Discrete and Continuous Cases”, University of Florida
“Graphical Models: Modeling by Bayesian Networks”, University of Florida
“Hidden Markov Model and Its Applications”, University of Florida
“Principal Component Analysis: An Introduction to Dimensionality Reduction and Manifold Learning”, University of Florida
2008
“Statistical Graphical Models and Inference Algorithms”, University of Florida
“Conditional Entropy and Mutual Information for Feature Extraction and Clustering”, University of Florida
2007
“Vegetation Filter on 3D LiDAR Data”, University of Florida
2006
“Bayesian Networks: Interpolating missing ALSM data”, University of Florida
2005
“Machine Learning and LiDAR Processing Algorithms”, Remote Sensing Workshop, Chulalongkorn University, Bangkok, Thailand
“Stem Cell Research Meets Machine Learning”, Mahidol University, Bangkok, Thailand
“Mathematics, AI, Science, and Your Future!!!”, Triam Udom Suksa High School, Bangkok, Thailand
“Machine Learning and Pattern Recognition in Remote Sensing Applications” , University of Florida
MATLAB®; Programming Techniques for Electrical and Computer Engineering Students, HKN Tutorial Session, University of Florida
LIST OF ALL PUBLICATIONS
JOURNAL ARTICLES
K. Kampa, E. Hasanbelliu, J. Cobb, J. Principe, and K. Slatton, “Deformable bayesian network: A robust framework for underwater sensor fusion,” IEEE Journal of Oceanic Engineering, vol. 37, no. 2, pp. 166184, 2012.
K. Kampa and K. Clint Slatton, “Information-Theoretic Hierarchical Segmentation of Airborne Laser Swath Mapping Data,” IEEE Transactions in Geoscience and Remote Sensing, (in preparation).
Andrew B. Kennedy, K. Clint Slatton, Tian-Jian Hsu, Michael J. Starek, K. Kampa, “Ephemeral sand waves in the hurricane surf zone,” International Journal of Marine Geology, Geochemistry and Geophysics, vol. 250, May 2008, pp. 276 – 280.
Andrew B. Kennedy, K. Clint Slatton, Michael Starek, K. Kampa, and Hyun-Chong Cho, “Hurricane Response of Nearshore Borrow Pits from Airborne Bathymetric LiDAR,” Journal of Coastal Engineering .
K. Clint Slatton, Hyun-chong Cho, Kittipat Kampa, Bidhya Yadav, “Automated Detection and Quantitative Measurements of Forest Streams Using High-Resolution ALSM Data,” Geophys. Rsrch. Ltrs., Special issue: New perspectives on the earth from airborne laser swath mapping, 2007, (in revision).
K. Clint Slatton, H. Lee, K. Kampa, H. Jhee, "Segmentation of ALSM Point Data and the Prediction of Subcanopy Sunlight Distribution," Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), invited, Jul., 2005.
CONFERENCE PAPERS (* = invited)
C. Chou, K. Kampa, S. Mehta, R. Tungaraza, W. Chaovalitwongse, and T. Grabowski, Information theoretic based feature selection for multi-voxel pattern analysis of fmri data, Brain Informatics, vol. x, pp. xxxxxx, 2012.
K. Kampa, D. Putthividhya, J. C. Principe and A. Rangarajan, “Data-driven Tree-structured Bayesian Network for Image Segmentation,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.
K. Kampa, D. Putthividhya and J. C. Principe, “Irregular Tree-Structured Bayesian Network for Image Segmentation,” In Proc. of the 2011 International Workshop on Machine Learning for Signal Processing (MLSP 2011).
K. Kampa, E. Hasanbelliu and J. C. Principe, “Closed-form Cauchy-Schwarz pdf Divergence for Mixture of Gaussians,” In Proc. of the 2011 International Joint Conference on Neural Networks (IJCNN 2011). (oral presentation)
K. Kampa, J. Principe, J. Tory Cobb and A. Rangarajan, “Deformable Bayesian Networks for Data Clustering and Fusion,” Proc. SPIE 2011 Defense and Security Symposium, vol. 1, no. 8017-25, 2011. (oral presentation)
E. Hasanbelliu, K. Kampa, J. Principe and J. Tory Cobb “Surprise metric for online learning,” Proc. SPIE 2011 Defense and Security Symposium, vol. —-, no. —–, 2011. (oral presentation)
K. Kampa, H. Lee, K. C. Slatton, A. Rangarajan, J. C. Principe, “A New Framework for Segmentation of Aerial Images Using Dynamic Trees,” IEEE Geoscience and Remote Sensing Society (IGARSS), 2010. (oral presentation—but not attending)
K. Kampa, Jose C. Principe and K. C. Slatton, “Dynamic Factor Graphs: A Novel Framework for Multiple Features Data Fusion,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010.
K. Kampa, K. C. Slatton and J. T. Cobb, “Dynamic Trees for Sensor Fusion,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2009. (oral presentation)
K. Kampa and K. C. Slatton, "An Adaptive Multiscale Filter for Segmenting Vegetation in ALSM Data," Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), vol. 6, Sep. 2004, pp. 3837 - 3840.
Andrew B. Kennedy, Clint Slatton, Tian-Jian Hsu, Michael Starek, and K. Kampa, “Large Scale Sand Waves in the Hurricane Surf Zone,” International Conference on Coastal Engineering (ICCE), 2008, Hamburg, Germany.
Hyun-chong Cho, K. Kampa, K. Clint Slatton, “Morphological Segmentation of LiDAR Digital Elevation Models to Extract Stream Channels in Forested Terrain,” Proc. IEEE 2007 International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 23-27, Jul. , 2007, pp.3182 – 3185, doi: 10.1109/IGARSS.2007.4423521.
J. Tory Cobb, K. Kampa, K. Clint Slatton, “Using Bayesian Networks to Estimate Missing Airborne Laser Swath Mapping (ALSM) Data,” Proc. SPIE 2006 Defense and Security Symposium, vol. 6234, no. 6234-04, 2006.
* K. Clint Slatton, H. Lee, K. Kampa, H. Jhee, "Segmentation of ALSM Point Data and the Prediction of Subcanopy Sunlight Distribution," Proc. IEEE 2005 International Geoscience and Remote Sensing Symposium (IGARSS), invited, vol. 1, Jul., 2005, pp. 525 – 528.
K.C. Slatton, K. Kampa, and H. Lee, “Estimating Leaf Area Index in Forests Using Airborne Laser Swath Mapping Data,” Eos Trans. AGU, 85(47), Fall Meet. Suppl., Abstract H13C-0445, Dec., 2004.
K.C. Slatton, H. Lee, K. Kampa, K. Nagarajan, V. Aggarwal, “Estimation of Optimal Transportation Paths and Optical Gaps in Forested Terrain,” 24th Army Science Conference, November 29 - December 2, 2004.
BOOK CHAPTERS
Assisting in revising and editing chapter2 of an upcoming book “Information Theoretic Learning: Renyi’s Entropy and Kernel Perspectives” by Jose Principe (ISBN-13: 978-1441915696).