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Shouyi Wang

Name

[Wang, Shouyi]
  • Assistant Professor, Industrial,Manufacturing,&Systems Engineering
  • Assist Professor

Biography

Dr. 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. He is the core faculty member of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS). His current research interests include data mining, machine learning, AI and deep learning technologies,  healthcare/medical decision-making systems, computational neuroscience, multivariate time-series modeling and forecasting. 

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
    • Feb 2013 to Present Association for the Advancement of Artificial Intelligence (AAAI)
    • Oct 2009 to Present Institute for Operations Research and the Management Sciences (INFORMS)
    • Sept 2009 to Present Institute of Industrial Engineers (IIE)
    • Sept 2008 to Present Institute of Electrical and Electronics Engineers (IEEE)
  • council member
    • Oct 2017 to Present INFORMS Section on Data Mining

Awards and Honors

    • Oct  2016 Best Paper Award Runner Up sponsored by International Conference on Brain Informatics 2016
    • Jul  2014 Featured by Physics in Medicine and Biology sponsored by Physics in Medicine and BiologyPhysics in Medicine and Biology, 2014
    • Apr  2014 SAVANT Center PSFR Colloquium Award sponsored by University of Texas at Arlington
    • Oct  2012 Best Student Paper Award on Data Mining sponsored by Institute for Operations Research and the Management Sciences (INFORMS)
    • Dec  2011 Best Student Paper & Travel Award sponsored by IEEE International Conference on Bioinformatics & Biomedicine 2010

News Articles

Other Activities

    • Editorial Activities
      • Feb 2018 Editor - Springer Book: Data Analytics - Models and Applications in Python
      • Conference Organizer
        • Nov 2018 Organization Chair - The 11th International Conference on Brain Informatics (BI 2018), Arlington, TX, USA
        • Editorial Activities
          • Aug 2016 Guest Editor - Annals of Operations Research, special volume on Applied Optimization and Data Mining
          • Conference Organizer
            • Oct 2016 Organization Chair - The First International Workshop on Big Data Neuroimaging Analytics for Brain and Mental Health (in joint with BIH 2016), Omaha, NE, 2016.
            • Feb 2016 Workshop Chair - The International Conference on Brain Informatics & Health (BIH), Omaha, NE, 2016.
            • Conference Organizer
              • Nov 2017 Organization Chair - The 2nd International Workshop on Big Data Neuroimaging Analytics for Brain & Mental Health, Beijing, China, Nov 15-16, 2017.
              • Oct 2017 Session Chair - Data Analytics & Modeling in Medical Imaging Analysis & Decision Making, INFORMS Data Mining Section, Houston, Oct 21-24, 2017.
              • Feb 2017 Publicity Chair - The 10th International Conference on Brain Informatics & Health (BIH), Beijing, China, 2017.
              • Conference Organizer
                • Oct 2015 Session Chair - Data Analytics and Statistical Learning Session in Data Mining Cluster, INFORMS Annual Conference, Philadelphia, PA, 2015.
                • Conference Organizer
                  • Oct 2014 Session Chair - Optimization and Modeling in Radiation Therapy Treatment Planning Session, Data Mining Cluster, INFORMS Annual Conference, San Francisco, 2014.
                  • Award Committee
                    • Feb 2014 Committee Member
                    • Conference Organizer
                      • Oct 2013 Session Chair - Data Analytics and Modeling for Healthcare Management I & II Sessions, Data Mining Cluster, INFORMS Annual Conference, Minneapolis, MN, 2013.
                      • Conference Organizer
                        • Nov 2012 Session Chair - Data Mining and Human Factors in Healthcare Systems, OR/MS in Medicine and Healthcare Cluster, International INFORMS Conference, Beijing, China, 2012.
                        • Nov 2012 Session Chair - Decision Making and Planning: Methods and Applications Session, Data Mining Cluster, INFORMS Annual Conference, Phoenix, AZ, 2012.

Research and Expertise

  • Ongoing Research Directions

    I am interested in interdisciplinary research that is critical to address 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, AI, operations research, statistics, bioinformatics, and neuroscience. I have a great interest to develop innovative data mining, machine learning, and AI solutions for complex engineering and science problems. My current active research areas are:

    1. Advanced Data Mining and Machine Learning Research, include data fusion, data representation, feature extraction, feature selection, classification, optimization, pattern recognition, and sparse learning, adaptive online learning algorithms.

    2. Advanced Artificial Intelligence Techniques and Applications, including Deep Learning Techniques, Deep Reinforcement Learning, Interactive Intelligent Robotics Systems, and AI-driven technolgoies. 

    3. Brain Informatics and Computational Neurosciences Research, areas include multi-modal neuroimaging analyatics EEG/fMRI/fNIRS, EEG Imaging reconstruction & Brain Mapping, Brain-Computer Interfaces, and Brain-Inspired AI Technilogies, Brain Disorder Diagnosis, human-machine communication.   

    4. Intelligent Decision-Making Models for Complex Systems Modeling, areas include 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.

    5. Medical Imaging Analysis and Healthcare Applications. Research areas include medical imaging analysis and modeling, discriminative pattern recognition & classification, dianogstic tools for neuroimaging research,  pattern anslysis of PET/CT Scan, and pattern recognition using functional MRI (fMRI).

    6. 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

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)

    • 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)

    • 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)

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

      Talk at Annual Meeting of Cognitive Neuroscience Society (CNS)

    • 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)

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

      Invited Talk and Session Chair at INFORMS 2014

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

      Talk at Brain KDD 2014. 

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

      Invited Talk at UTA CSE Colloquiums, Arlington, TX.

    • 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

This data is entered manually by the author of the profile and may duplicate data in the Sponsored Projects section.
    • Nov 2017 to Present Developing Data-Driven Assistive Technologies to Improve Human Performance and Quality Control for Large-Scale Manufacturing sponsored by  - $53000
    • Mar 2017 to Present Big Data Analytics and Smart Information Fusion System to Improve Efficiency and Decision Making for Apple iPhone Production sponsored by  - $240000
    • 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
    • May 2015 to Present GPU-Accelerated Big Data Computing for Brain Informatics Research sponsored by  - $10000
    • Aug 2015 to July 2016 A System for Neuro-Feedback Anger Management to Prevent Domestic Violence sponsored by  - $20000
    • 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

Patents

    • Feb 2018 D2015-0020  ACT-IONM: Automatic Classification and Translation of Intraoperative neurophysiological monitoring (IONM) waveforms during spine surgery

      ACT-IONM: Automatic Classification and Translation of Intraoperative neurophysiological monitoring (IONM) waveforms during spine surgery”, co-investor J.C. Chiao. Invention disclosure submitted in August 2015, track code: D2015-0020. 

Other Research Activities

Students Supervised

  • Doctoral
    • Present
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      Sparse Learning Methods for Brain Imaging Analytics and Modeling
    • Present
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      Thesis Topic: 1)  Integration of Data Miningand Optimization Techniques for Large Scale Data Mining, 2) Deep Learning and Intelligent Robotics
    • Present
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      Data Analysis and Structured Feature Selection for High Dimensional Data Mining and Knowledge Discovery, The University of Texas at Arlington. 
    • Present
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      Data Analytics and Text Mining for Finance Data & Management
    • May 2015
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      PhD Thesis: Nonstationary and Complex Time Series Modeling and Prediction. Data Scientist at CSX  https://www.linkedin.com/in/kin-ming-jerry-kam-a0a97531/ 
  • Master's
    • Present
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      Data Mining & Analytics for 1) Computational Neuroscience,  2) Smart Manufacturing Systems 
    • Present
      Statistical Quality Control and Data Analysis for Advanced Manufacturing Systems. Quality Control Engineer, Foxconn International, Fort Worth. 
    • May 2016
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      M.S. Thesis: Discriminating Parkinson’s Disease Using Functional Connectivity And Brain Network Analysis. Manufacturing Engineer at Texstars, Inc.

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.