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Dr. Jean X. Gao

Name

[Gao, Dr. Jean X.]
  • Professor, Department of Computer Science & Engineering

Professional Preparation

    • In Progress Ph.D. in Electrical and Computer EngineeringPurdue University, West Lafayette, IN
    • In Progress M.S. in Biomedical EngineeringRose-Hulman Institute of Technology
    • In Progress B.S. in Biomedical EngineeringShanghai Medical University

Appointments

    • Sept 2014 to Present Professor
      University of Texas at Arlington
    • Sept 2009 to Aug 2014 Associate Professor
      University of Texas at Arlington
    • 2003 to Aug 2009 Assistant Professor
      University of Texas at Arlington

Publications

      Journal Article 2013
      • S. Li, J. Nyagilo, W. Wang, B. Zhang, D. Dave, and J. Gao, “Probabilistic partial least squares regression for quantitative analysis of Raman spectra” International Journal of Data Mining & Bioinformatics, accepted, 2012.
        {Journal Article }

      Conference Paper 2012
      •  M. Kang, D. Kim, and J. Gao, “A novel multivariate quantification strategy for complex masspectrometry data,” Proceedings of International Conference on Bioinformatics and Computational Biology (BICoB), Las Vegas, NV, March 12-14, 2012.
        {Conference Paper }
      2012
      • M. Nguyen and J. Gao, “A mean shift clustering based algorithm for multiple alignment of LC-MS data,”Proceedings of International Symposium on Bioinformatics Research and Applications (ISBRA)( poster), pp. 43-46, Dallas, TX, May 21-23, 2012.
        {Conference Paper }
      2012
      • S. Li, J. Nyagilo, D. Dave, and J. Gao, “CWT-PLSR for quantitative analysis of Raman Spectrum,” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) (19.93 % regular paper acceptance rate), pp. 318-323, Philadelphia, PA, Oct 4-7, 2012.
        {Conference Paper }
      2012
      • S. Li, J. Gao, J. Nyagilo, and D. Dave, “A new continuum regression method for quantitative analysis of Raman spectrum,” Proceedings of IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), pp. 667-670, Boca Raton, FL, Dec 12-15, 2012.
        {Conference Paper }

      Journal Article 2012
      • G. Li, H. Tang, D. Kim, J. Gao, and L. Lin, “Employment of frame accumulation and shaped function for upgrading low-light-level image detection sensitivity,” Optics Letters (ISI impact factor: 3.318), vol. 37, no. 8, pp. 1361-1363, 2012.
        {Journal Article }
      2012
      • S. Li, J. Nyagilo, W. Wang, B. Zhang, D. Dave, and J. Gao, “Probabilistic partial least squares regression for quantitative analysis of Raman spectra” International Journal of Data Mining & Bioinformatics, accepted, 2012
        {Journal Article }
      2012
      • D. Kim, X. Wang, C. Yang, and J. Gao, “A framework for personalized medicine: prediction of drug sensitivity in cancer by proteomic profiling,” Proteome Science, 10(Suppl 1):S13, 2012. (impact factor: 2.33)
        {Journal Article }
      2012
      • S. Li, J. Nyagilo, D. Dave, B. Zhang, and J. Gao, “Eigenspectra, a robust regression method for multiplexed surface enhanced Raman spectra analysis,” International Journal of Data Mining & Bioinformatics, in press, 2012.
        {Journal Article }

      Conference Paper 2011
      • S. Li, J. Nyagilo, D. Dave, and J. Gao, “Probabilistic Partial Least Square Regression: A Robust Model for Quantitative Analysis of Raman Spectroscopy Data,” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) (19.4 % regular paper acceptance rate), pp. 526-531, Atlanta, GA, Nov 12-15, 2011.
        {Conference Paper }
      2011
      • D. Kim, X. Wang, C. Yang, and J. Gao, “A Framework for Personalized Medicine with Reverse Phase Protein Array and Drug Sensitivity and Drug Sensitivity,” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) (19.7 % short paper acceptance rate), pp. 426-429, Atlanta, GA, Nov 12-15, 2011.
        {Conference Paper }
      2011
      • M. Kang, J. Gao, and L. Tang, “Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration,” Proceedings of IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), pp. 501-504, Hawaii, Dec 18-21, 2011.   
        {Conference Paper }
      2011
      • S. Li, K. Luby-Phelps, B. Zhang, X. Wu, and J. Gao, “Subcellular Particles Tracking in Time Lapse Confocal Microscopy Images,” Proceedings of IEEE Annual International Conference on Engineering in Medicine and Biology Society (IEEE EMBC), pp 5973-5976, Boston NY, Aug 30-Sept 3, 2011.
        {Conference Paper }
      2011
      • D. Kim, C. Yang, X. Wang, B. Zhang, X. Wu, and J. Gao, “Discovery of Lung Cancer Pathways using Reverse Phase Protein Microarray and Prior-Knowledge based Bayesian Networks,” Proceedings of IEEE Annual International Conference on Engineering in Medicine and Biology Society (IEEE EMBC), pp 5543-5546, Boston NY, Aug 30-Sept 3, 2011.
        {Conference Paper }

      Journal Article 2011
      • N. Thakoor and J. Gao, “Branch-and-bound for model selection and its computational complexity,” IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 5, pp. 655-668, 2011.
        {Journal Article }
      2011
      • J.H. Oh and J. Gao, “Fast kernel discriminant analysis for classification of liver cancer mass spectra,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. g, 1522-1534, 2011.
        {Journal Article }

      Conference Paper 2010
      • S. Li, J. Gao, J. Nyagilo, and D. Dave, “Eigenspectra, a robust regression method for multiplexed Raman spectra analysis,” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) (17.2 % regular paper acceptance rate), pp. 525-530, Hong Kong, China, Dec 18-21, 2010.
        {Conference Paper }
      2010
      • M. Kang,  J. Gao, and L. Tang, “Computational modeling of phagocyte transmigration during biomaterial-mediated foreign body responses,” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) (20.3 % short paper acceptance rate), pp. 609-612, Hong Kong, China, Dec 18-21, 2010.
        {Conference Paper }

      Journal Article 2010
      • Thakoor, N. and J. Gao. "Branch-and-bound for model selection and its computational complexity." IEEE Transactions on Knowledge and Data Engineering (2010).
        {Journal Article }
      2010
      • Kim, X. Wang, C. Yang, and J. Gao. "Learning biological network using mutual information and conditional independence." BMC Bioinformatics 11.3 (2010).
        {Journal Article }
      2010
      • Thakoor, J. Gao and V. Devragen. "Multibody structure-and-motion segmentation by branch-and-bound model selection." IEEE Transactions on Image Processing 19.6 (2010): 1393-1402.
        {Journal Article }
      2010
      • Kim, C. R. Yang and J. Gao. "Functional proteomic pattern identification under low dose ionization radiation." Artificial Intelligence in Medicine 49.3 (2010): 177-85.
        {Journal Article }

      Journal Article 2009
      • Wen, K. Luby Phelps and J. Gao. "Tracking multiple subcellular structures using a sequential Monte Carlo approach." International Journal of Data Mining & Bioinformatics 3.3 (2009): 314-332.
        {Journal Article }
      2009
      • Oh, J. H. and J. Gao. "Fast kernel discriminant analysis for classification of liver cancer mass spectra." IEEE/ACM Transaction on Computational Biology and Bioinformatics 13.2 (2009): 195-206.
        {Journal Article }
      2009
      • Oh, Y. Lotan, K. Rosenblatt, and J. Gao. "Prostate cancer biomarker discovery using high performance mass spectral serum profiling." Computer Methods and Programs in Biomedicine 96.1 (2009): 33-41.
        {Journal Article }
      2009
      • Oh, J. H. and J. Gao. "A kernal-based approach for detecting outliers in high-dimensional biological data." BMC Bioinformatics 10.4 (2009): 33-41.
        {Journal Article }
      2009
      • Oh, A. Nandi, L. Knowles Gurnani, K. Rosenblatt Schorge, and J. Gao. "An extended Markov blanket approach to proteomic biomarker detection from high-resolution mass spectrometry data." IEEE Transactions on Information Technology in Biomedicine 13.2 (2009): 195-206.
        {Journal Article }

      Journal Article 2008
      • Oh, Y. Kim, K. Rosenblatt Gurnani, and J. Gao. "biomarker selection and sample prediction on multi-category disease on MALDI-TOF data." Bioinformatics 24.6 (2008): 1812-1818.
        {Journal Article }
      2008
      • Thakoor, J. Gao and V. Devragen. "Multi-stage branch-and-bound merging for planar surface segmentation in disparity space." IEEE Transactions on Image Processing 17.11 (2008): 2217-2226.
        {Journal Article }
      2008
      • Pan, J. Rush, D. Galasko Peskind, J. Quinn Chung, J. %Mname Jankovic, C. Pan Zabetian, J. Oh Wang, J. Zhang Gao, J. Zhang, and T. Montine. "Application of targeted quantitative proteomics analysis in human cerebrospinal fluid using an LC MALDI TOF/TOF platform." Journal of Proteome Research 7.2 (2008): 720-730.
        {Journal Article }
      2008
      • Thakoor, J. Gao and S. Jung. "Embedded planar surface segmentation system for stereo images." Machine Vision and Applications (2008).
        {Journal Article }
      2008
      • Gopinath, Q. Wen, K. %Mname Thakoor, and J. Gao. "A statistical approach for intensity loss compensation of confocal microscopy images." Journal of Microscopy 230 (2008): 143-159.
        {Journal Article }
      2008
      • Thakoor, N. and J. Gao. "Automatic video object extraction with camera in motion." International Journal of Image and Graphics 8.4 (2008): 573-600.
        {Journal Article }
      2008
      • Shu, Q. Liang and J. Gao. "Wireless sensor network lifetime analysis using interval type-2 fuzzy logic systems." IEEE Transactions on Fuzzy Systems 16.2 (2008): 416-427.
        {Journal Article }
      2008
      • Thakoor, J. Gao and V. Devarajan. "Multi-hypothesis prior for segmentation of stereo disparity." IEEE Signal Processing Letters 15 (2008): 613-616.
        {Journal Article }

      Journal Article 2007
      • Shu, Q. Liang and J. Gao. "Distributed sensor network deployment using fuzzy logic systems." International Journal on Wireless Information Networks 14.3 (2007): 163-173.
        {Journal Article }
      2007
      • Gao, J. "Object shape delineation by dual-region growing during tracking process." International Transactions on Computer Science and Engineering 36.1 (2007): 27-34.
        {Journal Article }
      2007
      • Thakoor, J. Gao and S. Jung. "Hidden Markov model based weighted likelihood discriminant for 2D shape classification." IEEE Transactions on Image Processing 16.11 (2007): 2707-2719.
        {Journal Article }
      2007
      • Xue, G. Arbique, M D Saint-Cyr, M D Brown, Dan Hatef, and J. Gao. "Four-dimensional vasular tree reconstruction using moving grid deformation." Academic Radiology 14.12 (2007): 1540-1553.
        {Journal Article }

      Book Chapter 2007
      • Michalak, Y. Kim and J. Gao. "Computational challenges of microarray analysis." Computational Genomics: Current Methods. Hethersett, UK: Horizon Scientific Press, 2007.
        {Book Chapter }

      Journal Article 2006
      • Najima, S. Brown, J. Janis Acikel, T. Abulezz Arbique, Q. Wen Gao, and R. Rohrich Kurihara. "Defining vascular supply and territory of thinned perforator flaps: Part II. Superior gluteal artery perforator flap." Plastic and Reconstructive Surgery 118.6 (2006): 1338-1348.
        {Journal Article }
      2006
      • Thakoor, N. and J. Gao. "Occlusion resistant shape classifier based on warped optimal path matching." International Transactions on Communication and Signal Processing 9.1 (2006): 31-40.
        {Journal Article }
      2006
      • Oh, J. and J. Gao. "A two-way parallel searching for peptide identification via tandem mass spectrometry." Special Issue on Bioinformatics, Engineering Letters 13.3 (2006): 344-354.
        {Journal Article }
      2006
      • Oh, A. Nandi, L. Knowles Gurnani, K. Rosenblatt Schorge, and J. Gao. "Identifying biomarkers to predict early relapse in ovarian cancer." Journal of Bioinformatics and Computational Biology (JBCB) 4.6 (2006): 1159-1179.
        {Journal Article }
      2006
      • J. Gao. "New discoveries of image display size on observer performance," Academic Radiology (Elsevier), vol. 13, no. 4, 2006.
        {Journal Article }

      Journal Article 2005
      • Gao, A. Kosaka and A. Kak. "A deformable model for human CT liver extraction." Academic Radiology 12.9 (2005): 1178-1189.
        {Journal Article }
      2005
      • A. K. Gao and A. Kak. "A Multi-Kalman Filtering Approach for Video Tracking of Human-Delineated Objects in Cluttered Environments," Computer Vision and Image Understanding (CVIU) (Elsevier publisher), vol. 99, no. 1, pp. 1-57, 2005.
        {Journal Article }

      Conference Paper 2005
      • Q. Wen and J. Gao. "Shape-Based 3D Vascular Tree Extraction for Perforator Flaps," presented at Proc. of SPIE Medical Imaging, San Diego, CA, February 12, 2005.
        {Conference Paper }
      2005
      • J. G. Wen, H. I. Kosaka, K. Luby-Phelps, and D. Mundy. "A particle filter framework using optimal importance function for protein molecules tracking," presented at Proc. IEEE International Conference on Image Processing (IEEE ICIP), Genova, September 11, 2005.
        {Conference Paper }
      2005
      • J. G. Oh, P. G. Nandi, J. S. Knowles, and K. Rosenblatt. "Multicategory classification using extended SVM-RFE and Markov blanket on SELDI-TOF mass spectrometry data," presented at Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, CA, November 2005.
        {Conference Paper }
      2005
      • N. Thakoor and J. Gao. "Shape classification based on generalized Gaussian descent method with hidden Markov model descriptor," presented at Proceedings of IEEE International Conference on Computer Vision (ICCV) (blind review, acceptance rate: 20.4%, 245 out of 1200), Beijing, China, October 2005.
        {Conference Paper }
      2005
      • N. Thakoor and J. Gao. "Automatic Video Object Shape Extraction and its Classification with Camera in Motion," presented at Proc. IEEE International Conference on Image Processing (IEEE ICIP), Genova, September 11, 2005.
        {Conference Paper }
      2005
      • Y. Kim and J. Gao. "A New Semi-Supervised Subspace Clustering Algorithm on Fitting Mixture Models," presented at Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, CA, November 14, 2005.
        {Conference Paper }
      2005
      • J. H. Oh and J. Gao. "Peptide identification by tandem mass spectra: an efficient parallel searching approach," presented at Proc. IEEE Symposium on Bioinformatics and Bioengineering (BIBE) (full paper acceptance rate 18%), Minneapolis, MN, October 19, 2005.
        {Conference Paper }

      Conference Paper 2004
      • J. H. Oh, Y.B. Kim, and J. Gao, "A Two-Way Searching Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry," Proceedings of International Conference on Bioinformatics and its Applications (ICBA), 2004.
        {Conference Paper }
      2004
      • Y. B. Kim, J. H. Oh, and J. Gao, "Emerging Pattern based Subspace Clustering of Microarray Gene Expression using Mixture Models," Proceedings of International Conference on Bioinformatics and its Applications (ICBA), 2004.
        {Conference Paper }

      Conference Paper 2003
      • J. Gao. "Self-Occlusion Immune Video Tracking of Objects in Cluttered Environments," presented at Proceeding of IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS03), Miami, FL, July 2003.
        {Conference Paper }

      Journal Article 2001
      • J. Gao and A. Kosaka, "Multi-Visit of Kalman Filtering for Semantic Object Tracking," Proceedings of IEEE International Conference on Multimedia and Expo (IEEE ICME), pp. 837-840, Japan, August, 2001.
        {Journal Article }

      Journal Article 1999
      • J. Gao, A. Kosaka, and A. Kak, “Interactive Color Image Segmentation Editor Driven by Active Contour Model,” Proceedings of  IEEE International Conference on Image Processing (IEEE ICIP, acceptance rate 45%), vol. 3, pp. 245-9, Kobe, Japan, 1999.
        {Journal Article }

      Journal Article 1998
      • A. K. Gao and A. Kak. "A Deformable Model for Human CT Liver Extraction," Academic Radiology (Elsevier), vol. 12, no. 9, 1998.
        {Journal Article }

      Journal Article 1996
      • Gao, J. and L. Waite. "Patellofemoral joint study via image processing." Biomedical Sciences Instrumentation 32 (1996): 151-160.
        {Journal Article }

Courses

      • CSE 2315-001 DISCRETE STRUCTURES

        Course Description:

        This course presents materials to augment the students’ theoretical foundation for computer science in the subject areas of formal logic, mathematical proof techniques, sets, combinatorics, functions and relations, trees and graphs, and graph algorithms.

        Course Objective:

        To introduce and assist the students to gain a basic grasp of formal fundamental theories and discrete mathematical concepts employed in problem abstraction and representation needed in the study of modern computer science and computer engineering.

        Prerequisites:

        Passing grade in Intermediate Programming (CSE 1320) and Calculus I (Math 1426) or an equivalent before attending this course.

        Required Textbook:

        Judith L. Gersting, Mathematical Structures for Computer Science, 6th edition, W.H. Freeman and Company, 2007.

        Reference Book (optional) :

        Kenneth Rosen, Discrete Mathematics and Its applications, 6the edition,McGraw Hill Publishing Company, 2007.
        Spring - Regular Academic Session - 2013 Download Syllabus 1 Link
      • CSE 5367-001 PATTERN RECOGNITION

        Course Description:

        Pattern recognition - the act of taking raw data and making decisions based on the categories of the pattern - has applied to such diverse areas as character recognition, data mining, medical diagnosis, image processing, computer vision, bioinformatics, speech recognition, fraud detection, and stock market prediction.  This course will provide underlying of principles and various approaches of pattern recognition and decision making processes.  The topics include diverse classifier designs, evaluation of classifiability, learning algorithms, and feature extraction and modeling.   The goal of this course is to introduce students to the fundamental models of decision making in order to prepare them for applying the associated concepts to information processing.

        Textbook:

        Richard Duda, Peter Hart, and David Stork, Pattern Classification, John Wiley & Sons, Second edition, 2000.

        Tentative Major Topics to Be Covered:

        1. Introduction
           A. Problems in decision making processes
           B. Mathematical formulation

        2. Pattern recognition and learning machines
           A. Review of probability theory and linear algebra
           B. Hypothesis test (Bayesian test)
           C. Parametric classifier design (LDA, QDA)
           D. Non-parametric classifier design (KNN, Parzen window)
           E. Estimation of classifiability
           F. Classifier evaluation
           G. Learning algorithms

        3. Data analysis
           A. Feature extraction for signal representation (PCA)
           B. Feature extraction for classification (FSS)
           C. Clustering
           D. Modeling and validity tests

        Spring - Regular Academic Session - 2013 Download Syllabus 1 Link
      • CSE 5370-001 Bioinformatics
        Normal 0 false false false EN-US ZH-CN X-NONE MicrosoftInternetExplorer4

        Course Description:

        Biological sciences are undergoing a revolution in how they are practiced. In the last decade, a vast amount of data (DNA sequences, protein sequences, etc.) has become available, and computational methods are playing a fundamental role in transforming this data into scientific understanding. Bioinformatics involves developing and applying computational methods for managing and analyzing information about the sequence, structure and function of biological molecules and systems.  Topics will include understanding the evolutionary organization of genes (genomics), the structure and function of gene products (proteomics), and the dynamics for gene expression in biological processes (transcriptomics).

        Objectives:   

        To provide students an understanding of the fundamental computational problems in molecular biology and genomics, and a core set of widely used algorithms in computational biology.  The proposed course is intended to help students have a working knowledge of a variety of publicly available data and computational tools important in bioinformatics, and a grasp of the underlying principles of contemporary bioinformatics. 

        Fall - Regular Academic Session - 2012 Download Syllabus 1 Link