Resume (Short CV)
Download the pdf version of this resume. My CV (longer version) can be found here.
(latest updated: August 12. 2012)
KITTIPAT "BOT" KAMPA
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, 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.
CURRENT PROJECTS
- “Mind-reader”: A framework to predict object classes/categories from functional MRI (fMRI) time series recorded from a human subject’s brain when presented with an image of an object. I developed a feature extraction methodology and a supervised learning framework that yields >90% accuracy which is the state of the art.
- “Functional brain atlas”: A novel brain map derived from functional similarity of fMRI voxels in the brain alone. I develop robust and fast statistical measures to categorize brain voxels according to their class-specific responses and apply unsupervised learning algorithms on them resulting in the functional brain atlas, which is different from the conventional anatomical brain atlas.
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)
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
PROGRAMMING SKILLS
Programming: Python, Java and proficient in MATLAB®;
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
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.