SHUO LI. (817) UTA Boulevard, Engineering Research Building 205, Arlington, TX 76019

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1 EDUCATION SHUO LI (817) UTA Boulevard, Engineering Research Building 205, Arlington, TX Ph.D. in Computer Science & Engineering University of Texas at Arlington, USA Research focus on Computer and Information Sciences. Supervised by Dr. Jean Gao Thesis Topic: Quantitative Analysis of Surface-enhancement Raman Spectra (SERS). Overall GPA: 3.909/4 M.S. in Computer Science Sichuan University, Chengdu, China Research focus on Computer Application, Graphics & Image Processing. Supervised by Dr. Dong C. Liu Thesis: Blood Vessel Tracking and Diameter Measurement Method Based on Skeleton Algorithm. B.S. in Software Engineering Sichuan University, Chengdu, China Thesis: INFY Bank System based on J2EE. RESEARCH INTERESTS My research is on machine learning, image and signal processing, with a particular emphasis on their joint applications in Biomedical and Bioinformatics problem. My research fields are: Machine Learning methods such as classification, regression and clustering methods. Multivariate statistical techniques such as feature extraction or selection and dimension reduction. Statistical learning such as ensemble learning and Bayesian learning. Biomedical images and signals processing such as image segmentation and denoising as well as signal decomposition. Computer vision such as objects detection and objects tracking. PUBLICATIONS Journals Papers S. Li, J.O. Nyagilo, D.P. Dave and J. Gao, Models and Methods for Quantitative Analysis of Raman Spectra, IEEE Transactions on Information Technology in Biomedicine, accepted, S. Li, J.O. Nyagilo, D.P. Dave and J. Gao, Continuous Wavelet Transform based PLS Regression method for Quantitative Analysis of Raman Spectra, IEEE Transactions on Nanobioscience, accepted, S. Li, J.O. Nyagilo, Wei Wang, Baoju Zhang, D.P. Dave and J. Gao, Probabilistic Partial Least Squares Regression for Quantitative Analysis of Raman Spectra, International Journal of Data Mining and Bioinformatics (IJDMB), available online, S. Li, J.O. Nyagilo, D.P. Dave, Baoju Zhang and J. Gao, Eigenspectra, A Robust Regression Method For Multiplexed Raman Spectra Analysis, International Journal of Data Mining and Bioinformatics (IJDMB), acceptance date: 01/06/2011, available online.

2 Conferences Proceedings S. Li, Mingon Kang, J.O. Nyagilo, D.P. Dave and J. Gao, Continuous Wavelet Transform based Continuum Regression for Quantitative Analysis of Surface-enhanced Raman Spectra, The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC), accepted. S. Li, J.O. Nyagilo, D.P. Dave and J. Gao, A New Continuum Regression Method for Quantitative Analysis of Raman Spectrum, 11th International Conference on Machine Learning and Applications (IEEE ICMLA) 2012: S. Li, J. Gao, J.O. Nyagilo and D.P. Dave, CWT-PLSR for Quantitative Analysis of Raman Spectrum, The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2012: (regular paper acceptance rate 19.93%) S. Li, J. Gao, J.O. Nyagilo and D.P. Dave, Probabilistic Partial Least Square Regression: A Robust Model for Quantitative Analysis of Raman Spectroscopy Data, The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2011: (regular paper acceptance rate 19.40%, student travel award 28/109) S. Li, K. Luby-Phelps, B. Zhang, X. Wu, and J. Gao, Subcellular Particles Tracking in Time-lapse Confocal Microscopy Images, The 33th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC) 2011: S. Li, J. Gao, J.O. Nyagilo and D.P. Dave, Eigenspectra, A Robust Regression Method For Multiplexed Raman Spectra Analysis, The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2010: (regular paper acceptance rate 17.2%) H. Iwaki, A. Kosaka, S. Li, J.Gao, Motion Detection for Subcellular Structure Trafficking, The 31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC) Sep. 2, 2009: S. Li and D.C. Liu, Vessel Tracking Algorithm in Ultrasound Imaging, International Conference on Bioinformatics and Biomedical Engineering (IEEE ICBBE), May 16, 2008: RESEARCH EXPERIENCE Quantitative Analysis of Surface-enhanced Raman Spectra (QASERS) In collaboration with Dr. Digant P. Dave, Bioengineer Dept. UTA 8/ now Project Description: With the help of surface-enhanced Raman scattering nanoparticles, Raman spectrum can give fingerprint and quantitative information of particular molecules. Quantitative analysis of Surface-enhanced Raman Spectra (QASERS), which is based on the intensities of Raman signals collected from mixture nano-tags (nanoparticles attached to analyte) to estimate the concentrations of pure nano-tag, provides an effective and efficient methodology in the field of biomedical diagnostics, such as cancer detection. Due to the high dimension, low sample size of Raman signals data set, as well as the impact of instable background intensities and noisy channels, QASERS needs to detect the biomarkers (Raman peaks) based on the correlation or covariance analysis first, then build the regression model and predict the concentrations. Major Contributions: (1) Compared the current quantitative analysis models and methods and analyze the merits and limitations of them when applied to Raman spectra. (2) Designed a new continuous wavelet transform (CWT) based multivariate regression framework that is for removing noise and background of Raman signals and extracting the height information of Raman peaks to do regression with concentrations. (3) Presented a new multivariate regression method named new continuum regression that can more effectively extract Raman peaks and improve the prediction accuracy. (4) Presented a unified probabilistic PLS Regression model and a Bayesian PLS (BPLS) model which is to interpret the internal structure of data and to incorporate prior knowledge into algorithms as well as to avoid the over-fitting problem caused by the high dimension, low sample size problem.

3 Signal Processing Machine Learning Multivariate statistical learning Bayesian Nonparametrics Computer Languate Wavelet transform/decomposion for signal smoothing, baseline correction and peak extraction. Regression methods, feature extraction, data fusion. LDA, PCA/PCR, CCA, PLS, OPLS, CR, RRR, ICA, FA. Bayesian curve fitting, Bayesian PCA. Matlab. eqtl Mapping for Identifying Gene Markers In collaboration with Mingon Kang, Computer Science and Engineering Dept. UTA 11/ now Project Purpose: Find gene markers that affect gene regulation from SNP microarray data and gene expression profiles. Major Contributions: Together with Mingon Kang presented a sparse CCA based regularized eqtl detection method to integrate SNP microarray data and gene expression profiles and look for gene markers. Bioinformatics SNP microarray, gene expression files. Machine Learning sparse coding Multivariate statistical learning CCA, PLS. Intercellular Cell Dynamics Analysis System (ICellDAS) 8/2008-8/2009 In collaboration with Dr. Kate Luby-Phelps, Cell Biology Dept. UT Southwestern Medical Center Project Description: A detailed understanding of subcellular structure motility is critical to understanding how cells regulate the delivery of specific proteins from the site of synthesis to the site of action. ICellDAS is to track the trajectories of subcellular particles from the green fluorescent image sequences. Major Contributions: I designed a particle tracking framework, including: particle detection, particle segmentation, particle feature extraction, particle matching and particle linking. This framework can effectively track particles with translational motion from the whole green fluorescent images and record their trajectories. Image Processing Divergence filter, wavelet image segmentation, Euclidian distance transfrom Computer Vision Particle representation, particle matching by Hungarian algorithm. Computer Languate Matlab. Blood Vessel Tracking in Medical Ultrasound Images 9/2006-8/2008 In collaboration with Dr. Dong C. Liu, CS Dept., Sichuan University, Chengdu, China Project Description: In order to diagnose vascular diseases, the widths of blood vessels at different positions need to be measured from medical images. Major Contributions: Dr. Liu and I presented a framework that first extracts the border and skeleton of vessels from a power-mode ultrasound image, which can provide clear images of blood vessels, and then calculate the vessel diameter from the skeleton points. It is difficult to find the skeletons (the midline) because of the crossings and irregular borders of vessels. I designed a Euclidean distance transform (EDT) based skeletonization algorithm that can find the smooth and accurate skeleton of blood vessels. Image Processing Euclidean distance transform, boundary extracting, image segmentation. Computer Vision Skeletonization algorithms. Computer Language C and C++.

4 RESEARCH COMMUNITY SERVICE AND ACTIVITIES Journal Paper Review International Journal of Data Mining and Bioinformatics (IJDMB), 2013, 1 paper. Journal of X-Ray Science and Technology (JXST), 2012, 1 paper. BMC Bioinformatics, 2011, 1 paper. International Journal of Data Mining and Bioinformatics (IJDMB), 2011, 1 paper. IEEE Transactions on Computational Biology and Bioinformatics (TCBB), 2010, 1 paper. Journal of Microscopy, (JMI), 2009, 1 paper. Conference Paper Review BIBM, 2012, 3 papers. ISBRA, 2012, 1 paper. BIBM, 2011, 1 paper. ACM BCB, 2011, 1 paper. ISBRA, 2011, 1 paper. BIBM, 2010, 2 papers. MICAI, 2009, 1 papers. BIBM, 2009, 2 papers. Technical Talks/Presentations Oral presentation of paper Probabilistic Partial Least Square Regression: A Robust Model for Quantitative Analysis of Raman Spectroscopy Data at the Conference IEEE BIBM Oral presentation of paper A Framework for Personalized Medicine with Reverse Phase Protein Array and Drug Sensitivity at the Conference IEEE BIBM Oral presentation of paper Motion Detection For Sbucellular Structure Trafficking at the Conference IEEE EMBC Poster presentation of paper Tracking Variable Number of Multiple Subcellular Structures in 3D at the Conference IEEE EMBC Poster presentation of paper Vessel Tracking Algorithm in Ultrasound Imaging at the Conference IEEE ICBBE TEACHING AND WORKING EXPERIENCE Teaching assistant 6/2009-8/2009 CSE 2315-Discreted Structure, CSE Dept. UT Arlington. Teaching assistant 1/2009-5/2009 CSE 3315-Theoretical Concepts, CSE Dept. UT Arlington. Teaching assistant 8/ /2008 CSE 1105-Introduction to Computer Science and Engineering, CSE Dept. UT Arlington. Contributions: Giving lectures in the lab sections of this class (3 sections per week). Teaching assistant 2/2008-6/2008 Information System, CS Dept. Sichuan University, Chengdu, China.

5 Internship as a Software Engineer 9/2005-4/2006 in Infosys company, Bangalore & Mysore, India Got the training of Java, J2EE, Design of Database, Oracle; accomplished a project named INFY Bank System, which is a mini online-bank system, based on J2EE, 9/ /2005. Worked in the Communication Service Providers (CSP) Department on a project named Inventory Stager, GUI, which is an information management system of British Telecom based on J2EE, 12/2005-4/2006. Course Project: Stock Information Inquiry System based on J2EE 02/ /2005 Class name: software development practice Project Description: Implement an online system that can obtain stock information from appointed web sites, and display them in a table. Computer Language: Java. OTHERS Honors and Awards Won the Graduate Deans Dissertation Fellowship $6726, 2013 Summer (7 graduate students in UTA win the prize). Won the Student Travel Award of the Conference IEEE BIBM Won the first-class prize of Sichuan Province in China Undergraduate Mathematical Contest in Modeling (CUMCM), Three years Scholarship for Master Studying in Sichuan University, Excellent Student of Sichuan University, First-class Scholarship from Sichuan University, Excellent Student of Sichuan University, Second-class Scholarship from Sichuan University, Technical Strengths Computer Languages: Matlab, C, Java, HTML, Java Script, JSP Databases: MySQL Tools: Latex, Microsoft Office REFERENCE Dr. Jean Gao Associate Professor, Computer Science and Engineering Dept. UT Arlington. Contact gao@uta.edu; phone: Comment: Major thesis survivor, who has the expertise in machine learning, data mining and bioinformatics. Dr. Digent P. Dave Associate Professor, Bioengineering Dept. UT Arlington. Contact aajphir@gmail.com; phone: Comment: Biomedical collaborator, who has the expertise in optical instrument and bioengineering. Dr. Chris Ding Professor, Computer Science and Engineering Dept. UT Arlington. Contact chqding@uta.edu; phone: Comment: Major thesis committee, who has the expertise in machine learning and data mining.