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Shouyi Wang

Name

[Wang, Shouyi]
  • Assist Professor

Biography

Shouyi Wang received the B.S. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2003 and the M.S. degree in systems and control engineering from Delft University of Technology, Delft, the Netherlands, in 2005 and the Ph.D. degree in Industrial and Systems Engineering from Rutgers University, Piscataway, NJ in 2012. From 2011-2013, he was a research scientist in the Department of Industrial and Systems Engineering and the Integrated Brain Imaging Center at School of Medicine, University of Washington, Seattle. Currently, he is an Assistant Professor of Industrial and Manufacturing Systems Engineering at University of Texas at Arlington, Arlington, TX. His current research interests include big data analytics, data mining, machine learning, pervasive computing, healthcare/medical decision-making systems, multivariate time-series modeling and forecasting, real-time monitoring and early warning systems. 

Professional Preparation

    • 2012 PhD in Industrial & Systems EngineeringRutgers, the State University of New Jersey
    • 2005 MS in Systems and Control EngineeringDelft University of Technology
    • 2003 BS in Systems and Control EngineeringHarbin Institute of Technology

Appointments

    • Aug 2013 to Present Assist Professor
      University of Texas at Arlington
    • Sept 2011 to Aug 2013 Research Scientist
      University of Washington   Integrated Brain Imaging Center

Memberships

  • Membership
    • Oct 2012 to Present University of Texas at Arlington  Business Administration  Association for Information Systems
    • Sept 2009 to Present Institute of Industrial Engineers (IIE)
    • Oct 2009 to Present Institute for Operations Research and the Management Sciences (INFORMS)
    • Sept 2008 to Present Institute of Electrical and Electronics Engineers (IEEE)

Awards and Honors

    • Aug  2013 Finalist of INFORMS 2012 Data Mining Best Student Paper Award sponsored by Institute for Operations Research and the Management Sciences (INFORMS)
    • Dec  2011 BIBM Student Travel Award sponsored by 201 0 IEEE International Conference on Bioinformatics & Biomedicine

News Articles

Research and Expertise

  • Ongoing Research Directions

    I am interested in interdisciplinary research that is critical to address many complex real-world problems. In my research activity, I have been actively collaborating with researchers and scientists in the areas of data mining, machine learning, operations research, statistics, bioinformatics, medical imaging, and neuroscience. I have a great interest to develop innovative theoretical and technical solutions for complex data and engineering problems. My current active research areas are:
    1. Theoretical and Methodology Research for Biomedical Data Mining Problems with Big Data Sets. Research areas include data representation, feature extraction, feature selection, classification, optimization, pattern recognition, and adaptive online learning algorithms.
    2. Intelligent Online Monitoring, Event Detection, and Early Warning/Prediction Systems. Applications include personalized epileptic seizure prediction, early diagnosis of brain diseases, and abnormality detection from any interested biomedical time series data.
    3 EEG-based Brain-Computer Interface and Human-Machine Systems. Research areas include mental state detection in real driving environment, EEG-based information retrieval system during web searching, and reliable signal processing systems for EEG-based human-machine communication.
    4 Diagnostic Imaging Analysis and Applications. Research areas include respiratory motion pattern analysis to achieve personalized PET/CT Scan for different patient groups, and pattern recognition of cognitive activities using functional MRI (fMRI).
    5 Personalized Healthcare System with Wearable Body Sensor Networks. Now it is possible to apply mobile monitoring devices to each individual patient and provide personalized diagnostic, medical and treatment plans. I am greatly interested to develop effective information systems for multi-sensory data fusion, and personalized monitoring and feedback healthcare information systems based on tracking time series data.

Publications

      Journal Article Forthcoming
      • S. Wang, W. Chaovalitwongse and S. Wong. A Probabilistic Pattern Learning Framework for Personalized Epileptic Seizure Prediction. IEEE Transactions on Neural Networks and Learning Systems.

        {Peer Reviewed} [Non-refereed/non-juried]
      • Forthcoming
        • S. Wang and W. Chaovalitwongse. Piecewise Linear Segmentation of Time Series Using a Data- Driven Threshold With Guaranteed Accuracy. Pattern Recognition Letters.

          {Peer Reviewed} [Non-refereed/non-juried]
        • Forthcoming
          • S. Wang and W. Chaovalitwongse. An Efficient and Robust Approach for Automated Online Segmentation of Time Series Streams. IEEE Transactions on Knowledge and Data Engineering.

            {Peer Reviewed} [Non-refereed/non-juried]

            Conference Proceeding 2016
            • S. Wang, K. Kam, C. Xiao, S. Bowen, and W. Chaovalitwongse. An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion Prediction. The 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, Feb. 12-17, 2016. (Acceptance Rate: 26%)

              {Conference Proceeding} [Refereed/Juried]

            • Journal Article 2016
              • Shan Liu, S. Wang, W. Art Chaovalitwongse , Stephen R. Bowen. Cost-effectiveness of Patient-Specific Motion Management Strategy in Lung Cancer Radiation Therapy Planning. Engineering Economist, Special Issue on Engineering Economic Models in Healthcare Systems, 2016 (In Press)

                {Journal Article} [Refereed/Juried]

                Conference Proceeding 2015
                • K. Kam, S. Wang, S. Bowen, and W. Chaovalitwongse. Pattern-Based Variant-Best-Neighbors Prediction By Using Orthogonal Polynomials Approximation. The Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, Jan. 25-29, 2015. (To Appear, Acceptance Rate: 26%)

                  {Conference Proceeding} [Refereed/Juried]
                • 2015
                  • Kinming Puk, Shouyi Wang, Cao (Danica) Xiao, Tara Madhyastha, Thomas Grabowski, W. Art Chaovalitwongse. Discriminating Parkinson’s Disease (PD) Using Functional Connectivity and Brain Network Analysis, The 5th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2015), Stanford, CA, June 2015. 

                    {Conference Proceeding} [Refereed/Juried]

                  • Journal Article 2015
                    • Cao Xiao, Shouyi Wang, Liying Zheng and Xudong Zhang and W. Art Chaovalitwongse. A Patient-Specific Model for Predicting Tibia Soft Tissue Insertions from Bony Outlines Using a Spatial Structure Supervised Learning Framework. IEEE Transactions on Human Machine Systems, Volume PP, Number 99, Pages 1-9, 2015.

                      {Journal Article} [Refereed/Juried]
                    • 2015
                      • S. Wang, J. Gwizdka, W. Chaovalitwongse. Using Wireless EEG Signals to Assess Memory Workload In the n-Back Task. IEEE Transactions on Human Machine Systems, Volume: 46, Issue: 3, Pages 424-435, 2015.  

                        {Journal Article} [Refereed/Juried]

                        Conference Paper 2014
                        • S. Wang, W. Chaovalitwongse, and S. Wong. A Novel Probabilistic Framework to Personalize Online Epileptic Seizure Prediction. BrainKDD: International Workshop on Data Mining for Brain Science, New York, NY, Aug 24-28, 2014. 

                          {Conference Paper} [Refereed/Juried]

                        • Journal Article 2014
                          • S. Wang, S. Bowen, W. Chaovalitwongse. Respiratory Trace Feature Analysis for Prediction of Respiratory-Gated PET/CT Quantification. Physics in Medicine and Biology, Volume 59, Number 4, Pages 1027-1045, 2014.  (*Featured in MedicalPhysicsWeb.org, PET/CT: Will Respiratory Gating Help? Mar 19, 2014. Web: http://medicalphysicsweb.org/cws/article/research/56616)

                            {Peer Reviewed} [Refereed/Juried]
                          • 2014
                            • S. Wang, Y. Zhang, C. Wu, F. Darvas and W. Chaovalitwongse. Online Prediction of Driver Distraction Based on Brain Activity Patterns. IEEE Transactions on Intelligent Transportation Systems, Volume PP, Number 99, Pages 1-15, 2014. 

                              {Journal Article} [Refereed/Juried]
                            • 2014
                              • S. Wang, W. Chaovalitwongse, and S. Wong. A Gradient-Based Adaptive Learning Framework for An Online Seizure Prediction. International Journal of Data Mining and Bioinformatics. Volume 10, Number 10, Pages 49-64, 2014. (*Featured in eHealth: The Enterprise of Healthcare, “Software for Seizure Prediction”; ScienceNewsline: Biology, “Getting to Grips with Seizure Prediction”)

                                {Journal Article} [Refereed/Juried]

                                Journal Article 2013
                                • S. Wang, W. Chaovalitwongse, and S. Wong. Online Seizure Prediction Using an Adaptive Learning Approach. IEEE Transactions on Knowledge and Data Engineering, Volume 25, Issue 12, Pages 2854-2866, 2013(*Featured   in   ScienceDaily,  “Better   Prediction   for   Epileptic   Seizures   Through   Adaptive Learning Approach”, Nov 2013; BioNewsTexas, “UT Arlington Researcher Part of Emerging Technology for Better Understanding Seizure Activity”, Nov 2013)

                                  {Journal Article} [Refereed/Juried]

                                  Journal Article 2012
                                  • S. Wang, W. Chaovalitwongse, and R. Babuska. Machine Learning Algorithms in Bipedal Robot Control. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Volume: 42, Issue: 5, Pages: 728-743, 2012.  

                                    {Peer Reviewed} [Refereed/Juried]

                                    Book Chapter 2011
                                    • S. Wang and W. Chaovalitwongse. Evaluating and Comparing Forecasting Models. Encyclopaedia of Operations Research and Management Science, Wiley & Sons, 2011.

                                      {Peer Reviewed} [Refereed/Juried]
                                    • 2011
                                      • S. Wang, O. Seref and W. Chaovalitwongse. Operations Research in Data Mining. Encyclopaedia of Operations Research and Management Science, Wiley & Sons, 2011.

                                        {Peer Reviewed} [Non-refereed/non-juried]

                                      • Journal Article 2011
                                        • S. Wang, C.J. Lin, C. Wu, and W. Chaovalitwongse. Early Detection of Numerical Typing Errors Using Data Mining Techniques. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Volume: 41, Issue: 6, Pages: 1199-1212, 2011.

                                          {Peer Reviewed} [Refereed/Juried]
                                        • 2011
                                          • W. Chaovalitwongse, R.S. Pottenger, S. Wang, Y.J. Fan, and L.D. Iasemidis. Pattern-Based and Network-Based Classification Techniques for Multichannel Medical Data Signals to Improve Brain Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Volume: 41, Issue: 5, Pages: 977-988, 2011.

                                            {Peer Reviewed} [Refereed/Juried]
                                          • 2011
                                            • W. Chaovalitwongse, R.S. Pottenger, S. Wang, Y.J. Fan, and L.D. Iasemidis. Pattern-Based and Network-Based Classification Techniques for Multichannel Medical Data Signals to Improve Brain Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Volume: 41, Issue: 5, Pages: 977-988, 2011

                                              {Peer Reviewed} [Refereed/Juried]

                                              Conference Proceeding 2010
                                              • S. Wang, W. Chaovalitwongse, and S. Wong. A Novel Reinforcement Learning Framework for Online Adaptive Seizure Prediction. Proceedings of IEEE International Conference on Bioinformatics & Biomedicine, pp. 499-504, 2010, Hong Kong, China. (Accept rate 17%, and Won Travel Award)

                                                {Conference Proceeding} [Non-refereed/non-juried]

                                                Conference Proceeding 2006
                                                • S. Wang, J. Braaksma, R. Babuska, and D. Hobbelen. Reinforcement learning control for biped robot walking on uneven surfaces. Proceedings of 2006 International Joint Conference on Neural Networks, pp. 4173-4178, 2006, Vancouver, Canada

                                                  {Peer Reviewed} [Refereed/Juried]

Presentations

    • February  2016
      An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion Prediction

      Talk at The 30th AAAI Conference on Artificial Intelligence (AAAI 2016)

    • January  2015
      Pattern-Based Variant-Best-Neighbors Respiratory Motion Prediction Using Orthogonal Polynomials Approximation

      Talk at The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015)

    • March  2015
      Classification of EEG signals of memory between musicians and non-musicians

      Talk at Annual Meeting of Cognitive Neuroscience Society (CNS)

    • June  2015
      Discriminating Parkinson’s Disease (PD) Using Functional Connectivity and Brain Network Analysis

      Talk at The 5th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2015)

    • November  2015
      A CT-Imaging-Based Structural Learning Framework to Recover Natural Anatomy of Soft Tissue Insertions for Knee Reconstruction Surgery

      Invited Talk at The 3rd International Workshop on Persistent and Photostimulable Phosphors (PPP2015)

    • November  2015
      Big Data Analytics and Supercomputing for Healthcare

      Demonstration at International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)

    • March  2014
      Automated Time Series Modeling and Pattern Learning for Personalized Healthcare Decision-Making Systems

      Invited Talk at UTA CSE Colloquiums, Arlington, TX.

    • August  2014
      An Adaptive Learning Framework for Personalized Online Epileptic Seizure Prediction.

      Talk at Brain KDD 2014. 

    • November  2014
      Using Physiological Signals to Assess Mental Workload on Human-Computer Interaction Tasks

      Invited Talk and Session Chair at INFORMS 2014

    • October  2013

      Enhancing Clinical Utility of Respiratory-gated PET/CT Imaging using Patient Classification

      A respiratory-gated (RG) method allows patients with lung tumors to breathe normally in PET/CT scan while minimizing respiratory motion effects. However, not all patients can benefit from the RG method. We constructed a prediction model to predict the effectiveness of the RG method on each individual patient only using features extracted from respiratory motion traces. The technique can be used to classify patient groups and assign the most appropriate strategy for each patient in PET/CT scan.

    • October  2012

      A Probabilistic Prediction Framework for Personalized Online Prediction of Epileptic Seizures,
      INFORMS, Phoenix, AZ, October 2012.

    • October  2012

      Online Monitoring and Prediction of Complex Time Series Events. Phoenix, AZ, USA, Oct 2012.

    • June  2012

      An Efficient Approach for Automated Online Segmentation of Time Series, International INFORMS, Beijing, China, June 2012.

    • November  2011

      A Gradient-Based Adaptive Learning Framework for an Online Seizure Prediction, INFORMS, Charlotte, NC, November 2011.

    • December  2010

      A Novel Reinforcement Learning Framework for Online Adaptive Seizure Prediction, IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, December 2010.

Projects

  • 2014
    • June 2014 to Present Respiratory Motion Pattern Analysis for a Personalized PET/CT Scan Strategy

      Patients with lung tumors generally cannot bear a breath-holding process in PET/CAT Scan. They have to breathe normally during a scan and an expensive way has to be applied to process the PET/CT imaging. To minimize respiratory motion artifacts an efficient quiescent period gating (QPG) method was developed at UW medical center to extract PET/CT data from the end-expiration quiescent period. However, it has been found that some portion of subjects cannot benefit from the QPG method. In this project, we studied the respiratory pattern effects on the PET/CT imaging quality, made extensive statistical pattern analysis and multivariate regression analysis, and finally constructed an important clinical recommendation system to discriminate the benefit and non-benefit subjects for the QPG scan approach. The paper on this work has been submitted to Physics in Medicine and Biology in June 2013. 

      Role: Principal Investigator PI: Shouyi Wang
  • 2013
    • Sept 2013 to Present Multivariate Time Series Data Mining

      This research is to develop new mathematical models to predict events from nonstationary multivariate time series data. Time series data accounts for a large fraction of the world’s supply of data, and have very broad applications in engineering, economy, industrial manufactory, finance, management and many other fields. However, most of the current time series methods make an assumption of stationarity. They are unable to identify complex temporal patterns from nonstationary time series that are nonperiodic, nonlinear, irregular, and chaotic. Thus this research is motivated to develop new efficient approaches to analyze nonstationary time series patterns and predict critical events. Three original and fundamental contributions have been made in the research.
      1. A novel piecewise-linear approximation algorithm for time series data using a data-driven decomposition strategy. The algorithm is capable of obtaining a piecewise-linear model for any arbitrary time series (stationary or nonstationary) efficiently and robustly at a very low computational cost. This work has been submitted to Pattern Recognition Letters.
      2. An efficient online monitoring and segmentation framework for time series temporal patterns. The algorithm is an extension of the piecewise-linear approximation algorithm. It is capable of processing and segmentation of online time series streams in time O(1) using a set of closed-form mathematical formulas.
      3. An adaptive learning framework for online pattern discovery and event prediction in multivariate time series data. The framework integrates time series segmentation, temporal pattern extraction, adaptive learning, and probability theory into an online pattern discovery and prediction system. This is a general pattern discovery framework, and has been evaluated on two challenging EEG prediction problems: epileptic seizure prediction and mental-state prediction. The proposed approach generated the most promising prediction results in both problems compared the current existing approaches.

      Role: Principal Investigator PI: Shouyi Wang
    • Sept 2013 to Present Online Monitoring and Abnormality Prediction of Mental States and Cognitive Activities

      The link between mental states/cognitive activities and brainwave recordings is of paramount importance with practical scientific and clinical implications. However, EEG-based brainwave patterns are extremely difficult to decode due to the nonstationary and chaotic properties. We conducted three related research projects on this problem category. In the first project, we successfully developed a data-mining framework to analyze EEG patterns and identify the mental state prior to erroneous keystrokes during a human typing experiment in critical tasks. This work has been published in IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans. In the second project, an adaptive pattern-learning framework was constructed to detect the mental states from drivers in a simulated driving environment. This work is to be submitted to IEEE Transactions on Intelligent Transportation Systems in Aug. 2013. The third project is a Google-funded project. We constructed an information retrieval system to identify cognitive and memory workload in different levels of task engagement using an EEG-based brain-computer interface.

      Role: Principal Investigator PI: Shouyi Wang
  • 2012
    • June 2012 to Present Personalized Early Diagnosis and Prediction System for Epileptic Seizures

      Due to the great inter-individual variability, there has been a desperate need for personalized seizure prediction for patients with epilepsy using Electroencephalogram (EEG) recordings. However, it has been an unsolved challenging problem for long since it is extremely difficult to capture predictive patterns for each individual from chaotic multi-channel EEG time series. I made an important methodology breakthrough in this area by constructing an online adaptive pattern-learning framework. The proposed new approach combines feature extraction, feature selection, pattern modeling and identification, adaptive online learning, and feedback control theory to achieve personalized pattern learning and prediction. This study is among the pioneer studies to investigate adaptive learning mechanisms for epileptic seizure prediction, and has achieved very promising prediction performance on 10 patients with epilepsy. This work has been accepted for publication in IEEE Transactions on Knowledge and Data Engineering. This work also won the Finalist of INFORMS 2012 Data Mining Best Student Paper Award

      Role: Principal Investigator PI: Shouyi Wang

Support & Funding

    • June 2016 to Present Directed Brain Network Modeling to Improve Pre-Surgical Evaluation of Brain Disorders sponsored by  - $10000
    • Sept 2015 to Present Collaborative: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning sponsored by  - $250000
    • Aug 2015 to July 2016 A System for Neuro-Feedback Anger Management to Prevent Domestic Violence sponsored by  - $20000
    • May 2015 to Present GPU-Accelerated Big Data Computing for Brain Informatics Research sponsored by  - $10000
    • Oct 2014 to Present Networking Infrastructure: Campus Networking for Transformative Exascale Research. sponsored by  - $500000
    • June 2014 to Dec 2014 Predictive Modeling of Respiratory Motion for Patients with Lung Cancer sponsored by  - $2500

Students Supervised

  • Doctoral
    • May 2015
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      PhD Thesis: Nonstationary and Complex Time Series Modeling and Prediction, The University of Texas at Arlington, 2015. 

    • Present
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      F2014 - Present, Ph.D. Thesis Topic: Integration of Data Miningand Optimization Techniques for Large Scale Data Mining, The University of Texas at Arlington. (Co-Chaired with Dr. Rosenberger)

    • Present

      S2014 - Present,  Ph.D. Thesis Topic: Data Analysis and Variable Selection for High Dimensional Data using Sparse Learning, The University of Texas at Arlington. 

  • Master's
    • May 2016
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      M.S. Thesis: Discriminating Parkinson’s Disease Using Functional Connectivity And Brain Network Analysis, The University of Texas at Arlington.

Courses

      • IE 5317-001 Introduction to Statistics

        This course covers descriptive statistics, random variables, set theory, probability distributions, mathematical expectation, confidence interval estimation, hypotheses testing, simple linear regression, design and analysis of computer experiments, multiple linear regression. 

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • IE 5300-001 Data Analytics and Modeling

        This course provides an in-depth introduction to data mining and pattern recognition. The basic theories, algorithms, key technologies in data analytics will be discussed. Topics include data representation, feature extraction, feature selection, correlation analysis, classification, pattern recognition, supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning), and algorithm independent machine learning models. The course will discuss many case studies and real-world applications. You will learn how to process massive data, and apply the most effective data mining and machine learning techniques to solve challenging engineering and scientific problems. You will gain the practical know-how needed to quickly and powerfully apply these techniques to solve data mining and knowledge discovery problems.  

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • IE 5317-004 INTRODUCTION TO STATISTICS

        The course topics include descriptive statistics, random variables, set theory, probability distributions, mathematical expectation, confidence interval estimation, hypotheses testing, simple linear regression, design and analysis of computer experiments, multiple linear regression.  Student Learning Outcomes: At the end of this course students should be able to (1) understand the basic concepts of probability theory, hypothesis testing, and linear regression, (2) apply those concepts to solve numerical problems, and (3) perform descriptive and inferential statistical analyses of data. 

        Fall - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • IE 5300-001 Topics in Industrial Engineering

        Topics in Industrial Engineering: Data Mining and Analytics

        This course provides a broad introduction to data mining, machine learning and statistical pattern recognition. The basic theories, algorithms, key technologies in data analytics will be discussed. Topics include data representation, feature extraction, feature selection, correlation analysis, classification, pattern recognition, supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning), and reinforcement learning. The course will discuss many case studies and real-world applications. You will learn how to process massive data, and apply the most effective data mining and machine learning techniques to solve challenging engineering and scientific problems. You will gain the practical know-how needed to quickly and powerfully apply these techniques to solve data mining and knowledge discovery problems.  

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours1 Document
      • IE 5300-001 Topics in Industrial Engineering

        This course provides a broad introduction to data mining, machine learning and statistical pattern recognition. The basic theories, algorithms, key technologies in data analytics will be discussed. Topics include data representation, feature extraction, feature selection, correlation analysis, classification, pattern recognition, supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning), and reinforcement learning. The course will discuss many case studies and real-world applications. You will learn how to process massive data, and apply the most effective data mining and machine learning techniques to solve challenging engineering and scientific problems. You will gain the practical know-how needed to quickly and powerfully apply these techniques to solve data mining and knowledge discovery problems.  

        Spring - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • IE 3312-001 Engineering Economy

        This class provides the student with the basic decision making tools required to analyze engineering project alternatives in terms of their worth and cost, an essential element of engineering practice.  The student is introduced to the concept of the time value of money and the methodology of basic engineering economy techniques.  It is also a goal to provide the student with the background to enable them to pass the Engineering Economy portion of the Fundamentals of Engineering exam.  This is also a class which has many applications in personal life.

        Fall - Regular Academic Session - 2013 Download Syllabus Contact info & Office Hours

Service to the University

  • Appointed
    • Sept 2013 to  Present IMSE Research Committee

      Industrial and Manufacturing Systems Engineering Research Committee, The University of Texas at Arlington. 2013–present

    • Sept 2013 to  Present COSMOS Research Member

      Faculty of the Center On Stochastic Modeling, Optimization, & Statistics (COSMOS). 2013-present

Service to the Profession

  • Appointed
    • June 2014 to  Present Guest Editor

      Guest Editor, Annals of Operations Research, Special Volume: Applied Optimization and Data Mining: Theory and Applications (2014 – Present), to be published in 2016.  

    • June 2015 to  Present Editor

      Editor for the new Data Mining Book “Data Analytics - Models and Applications in Matlab” by Springer, planned book publication in 2017. 

  • Elected
    • June 2016 to  Present Organization Committee

      Organization Committee, the International Conference on Brain Informatics & Health (BIH), Omaha, NE, 2016.   

    • June 2016 to  Present Organizer BDBM 2016

      International Workshop on Big Data Neuroimaging Analytics for Brain and Mental Health, Omaha, NE, 2016.