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
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- 2012 PhD in Industrial & Systems Engineering , Rutgers, the State University of New Jersey
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- 2005 MS in Systems and Control Engineering , Delft University of Technology
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- 2003 BS in Systems and Control Engineering , Harbin Institute of Technology
Appointments
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Aug 2013 to
Present
Assist Professor
University of Texas at Arlington
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Aug 2013 to
Present
Assist Professor
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Sept 2011 to
Aug 2013
Research Scientist
University of Washington Integrated Brain Imaging Center
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Sept 2011 to
Aug 2013
Research Scientist
Memberships
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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)
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council member
- Oct 2017 to Present INFORMS Section on Data Mining
Awards and Honors
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- Oct 2016 Best Paper Award Runner Up sponsored by International Conference on Brain Informatics 2016
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- 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
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- Oct 2012 Best Student Paper Award on Data Mining sponsored by Institute for Operations Research and the Management Sciences (INFORMS)
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- Dec 2011 Best Student Paper & Travel Award sponsored by IEEE International Conference on Bioinformatics & Biomedicine 2010
News Articles
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June 2016 UTA Researchers Look To Solve Anger Issues « CBS Dallas / Fort Worth
CBS News Reported our Brain Computer Interface and Emotion Management Research Project
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Mar 2014 PET/CT: will respiratory gating help? - MedicalPhysicsWeb
PET/CT provides a valuable tool for defining target tumour volumes and assessing therapy response. Its quantitative accuracy, however, is limited by respiratory-induced tumour motion. While respiratory gating can help compensate for such motion, its effectiveness varies greatly between individual patients. But could information from a patient's breathing trace help predict whether they could benefit from gated PET/CT?
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Nov 2013 Better prediction for epileptic seizures through adaptive learning approach
A UT Arlington assistant engineering professor has developed a computational model that can more accurately predict when an epileptic seizure will occur next based on the patient's personalized medical information.
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Other Activities
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Editorial Activities
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Feb 2018 Editor - Springer Book: Data Analytics - Models and Applications in Python
Conference Organizer
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Nov 2018 Organization Chair - The 11th International Conference on Brain Informatics (BI 2018), Arlington, TX, USA
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Editorial Activities
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Aug 2016 Guest Editor - Annals of Operations Research, special volume on Applied Optimization and Data Mining
Conference Organizer
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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.
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Feb 2016 Workshop Chair - The International Conference on Brain Informatics & Health (BIH), Omaha, NE, 2016.
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Conference Organizer
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Nov 2017 Organization Chair - The 2nd International Workshop on Big Data Neuroimaging Analytics for Brain & Mental Health, Beijing, China, Nov 15-16, 2017.
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Oct 2017 Session Chair - Data Analytics & Modeling in Medical Imaging Analysis & Decision Making, INFORMS Data Mining Section, Houston, Oct 21-24, 2017.
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Feb 2017 Publicity Chair - The 10th International Conference on Brain Informatics & Health (BIH), Beijing, China, 2017.
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Conference Organizer
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Oct 2015 Session Chair - Data Analytics and Statistical Learning Session in Data Mining Cluster, INFORMS Annual Conference, Philadelphia, PA, 2015.
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Conference Organizer
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Oct 2014 Session Chair - Optimization and Modeling in Radiation Therapy Treatment Planning Session, Data Mining Cluster, INFORMS Annual Conference, San Francisco, 2014.
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Feb 2014 Committee Member
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Conference Organizer
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Oct 2013 Session Chair - Data Analytics and Modeling for Healthcare Management I & II Sessions, Data Mining Cluster, INFORMS Annual Conference, Minneapolis, MN, 2013.
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Conference Organizer
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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.
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Nov 2012 Session Chair - Decision Making and Planning: Methods and Applications Session, Data Mining Cluster, INFORMS Annual Conference, Phoenix, AZ, 2012.
Research and Expertise
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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
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{Peer Reviewed }
R. Hosseini, B.Walsh, F.Tian, and S. Wang. Hemodynamic pattern discovery and classification with children who stutter using functional near-infrared spectroscopy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018.
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{Peer Reviewed }
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)
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{Peer Reviewed }
F. Liu, J. Qin, S. Wang, J. Rosenberger, J. Su. Supervised EEG Source Imaging with Graph Regularization in Transformed Domain. Proceedings of International Conference on Brain Informatics 2017 Nov 16 (pp. 59-71). Download
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{Conference Proceeding }
J. Qin, F. Liu, S. Wang, and J. Rosenberger. "EEG Source Imaging based on Spatial and Temporal Graph Structures." Proceedings of International Conference on Image Processing Theory, Tools and Applications, IPTA 2017. Download Preprint
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{Peer Reviewed }
F. Liu, S. Wang, J. Rosenberger, J. Su, and H. Liu. A Sparse Dictionary Learning Framework to Discover Discriminative Source Activations in EEG Brain Mapping. The 31th AAAI Conference on Artificial Intelligence, San Francisco, pp. 1431-1437, Feb., 2017. (Acceptance Rate: 26%). Cite: BibTex EndNote RefMan Download from aaai.org
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{Peer Reviewed }
F. Liu, R. Hosseini, J. Rosenberger, S. Wang, J. Su. Supervised discriminative EEG brain source imaging with graph regularization. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2017 Sep 10 (pp. 495-504). (Acceptance Rate: 25%). Cite: BibTex EndNote RefMan, Download
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{Peer Reviewed }
K. Tsiakas, M. Papakostas, M. Theofanidis, M. Bell, R. Mihalcea, S. Wang, M. Burzo, F. Makedon. An Interactive Multisensing Framework for Personalized Human Robot Collaboration and Assistive Training Using Reinforcement Learning. Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments 2017 Jun 21 (pp. 423-427). cite: BibTex EndNote RefMan Google Cite
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{Peer Reviewed }
J. Gwizdka, R. Hosseini, M. Cole, S. Wang. Temporal Dynamics of Eye-tracking and EEG During Reading and Relevance Decisions. Journal of the Association for Information Science and Technology, 68(10), 2299-2312, 2017. Cite: BibTeX EndNote RefMan Preprint
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{Peer Reviewed }
F. Liu, J. Rosenberger, Y. Lou, R. Hosseini, J. Su, S. Wang. Graph Regularized EEG Source Imaging with in-Class Consistency and out-Class Discrimination. IEEE Transactions on Big Data, Volume: PP, Issue: 99, pages 1-10, 2017. Cite: BibTeX EndNote RefMan Preprint
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{Peer Reviewed }
C. Xiao, S. Wang, L. Iasemidis, S. Wong, W. Chaovalitwongse. An Adaptive Pattern Learning Framework to Personalize Online Seizure Prediction. IEEE Transactions on Big Data, Volume: PP, Issue: 99, pages 1-12, 2017. Cite: BibTeX EndNote RefMan Download
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{Peer Reviewed }
C. Xiao, S. Wang, J. Tsai, W. Chaovalitwongse, and T.J. Grabowski. A Novel Mutual-Information-Guided Sparse Feature Selection Approach for Epilepsy Diagnosis Using Interictal EEG Signals. Proceedings of International Conference on Brain and Health Informatics, pp. 274-284, Springer International Publishing, October 2016 (Best Paper Runner Up). Download
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{Peer Reviewed }
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. Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 2159-2165, Phoenix, AZ, Feb. 12-17, 2016. (Acceptance Rate: 26%) Cite: BibTeX EndNote RefMan Download
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{Peer Reviewed }
K. Kam , J. Schaeffer, S. Wang, and H. Park. A Comprehensive Feature and Data Mining study on Musician Memory Processing using EEG Signals, Proceedings of International Conference on Brain and Health Informatics, pp. 274-284, Springer International Publishing, October 2016. Cite: BibTeX EndNote RefMan Download
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{Peer Reviewed }
K. Puk, K. Gandy, S. Wang, and H. Park. Pattern Classification and Analysis of Memory Processing in Depression Using EEG Signals, Proceedings of International Conference on Brain and Health Informatics, pp. 274-284, Springer International Publishing, October 2016. Cite: BibTeX EndNote RefMan Preprint
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{Peer Reviewed }
F. Liu, W. Xiang, S. Wang, and Lega, B. Prediction of Seizure Spread Network via Sparse Representations of Over-complete Dictionaries, Proceedings of International Conference on Brain and Health Informatics, pp. 274-284, Springer International Publishing, October 2016. Cite: BibTeX EndNote RefMan Preprint
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{Peer Reviewed }
C. Xiao, S. Wang, J. Bledsoe, S. Mehta, M. Semrud-Clikeman, T. Grabowski, and W. Art Chaovalitwongse. An Integrated Feature Ranking and Selection Framework for ADHD Diagnosis. Brain Informatics, Volume 3, Issue 3, pages145–155, 2016. Cite: BibTeX EndNote RefMan Download Manuscripts
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{Peer Reviewed }
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, 2016. Cite: BibTeX EndNote RefMan Download Manuscripts
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{Peer Reviewed }
C. Xiao, S. Wang, L. Zheng and X. 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: 46, Issue: 5, pages 638 - 646, 2016. Cite: BibTeX EndNote RefMan Download
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{Conference Proceeding }
K. Kam, S. Wang, S. Bowen, and W. Chaovalitwongse. Pattern-Based Variant-Best-Neighbors Prediction By Using Orthogonal Polynomials Approximation. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 1364-1370, Austin, TX, Jan. 25-29, 2015. (Acceptance Rate: 26%) Cite: BibTeX EndNote RefMan Download Manuscripts
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{Conference Proceeding }
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.
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{Journal Article }
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. Cite: BibTeX EndNote RefMan Download Manuscripts
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{Journal Article }
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”) Cite: BibTeX EndNote RefMan PDF Manuscripts
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{Peer Reviewed }
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). Cite: BibTeX EndNote RefMan Download Manuscripts
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{Journal Article }
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). Cite: BibTeX EndNote RefMan Download Manuscripts
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{Peer Reviewed }
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. Cite: BibTeX EndNote RefMan Download Manuscripts ResearchGate
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{Peer Reviewed }
S. Wang and W. Chaovalitwongse. Evaluating and Comparing Forecasting Models. Encyclopaedia of Operations Research and Management Science, Wiley & Sons, 2011. Cite: BibTeX EndNote RefMan Download Manuscripts
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{Peer Reviewed }
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. Cite: BibTeX EndNote RefMan Download Manuscripts
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{Peer Reviewed }
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. Cite: BibTeX EndNote RefMan Download Manuscripts
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{Conference Proceeding }
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 (BIBM), pp. 499-504, 2010, Hong Kong, China. (Accept rate 17%, and Best Student Paper & Travel Award) Cite: BibTeX EndNote RefMan Download Manuscripts (or see document below)
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{Peer Reviewed }
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. Cite: BibTeX EndNote RefMan Download Manuscripts (or download document below)
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Journal Article
2018
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Conference Proceeding
2017
Journal Article 2017
Book 2017
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Conference Proceeding
2016
Journal Article 2016
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Conference Proceeding
2015
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Journal Article
2014
Conference Paper 2014
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Journal Article
2013
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Journal Article
2012
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Book Chapter
2011
Journal Article 2011
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Conference Proceeding
2010
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Conference Proceeding
2006
Presentations
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February 2016
An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion PredictionTalk at The 30th AAAI Conference on Artificial Intelligence (AAAI 2016)
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November 2015
A CT-Imaging-Based Structural Learning Framework to Recover Natural Anatomy of Soft Tissue Insertions for Knee Reconstruction SurgeryInvited Talk at The 3rd International Workshop on Persistent and Photostimulable Phosphors (PPP2015)
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November 2015
Big Data Analytics and Supercomputing for HealthcareDemonstration at International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)
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June 2015
Discriminating Parkinson’s Disease (PD) Using Functional Connectivity and Brain Network AnalysisTalk at The 5th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2015)
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March 2015
Classification of EEG signals of memory between musicians and non-musiciansTalk at Annual Meeting of Cognitive Neuroscience Society (CNS)
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January 2015
Pattern-Based Variant-Best-Neighbors Respiratory Motion Prediction Using Orthogonal Polynomials ApproximationTalk at The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015)
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November 2014
Using Physiological Signals to Assess Mental Workload on Human-Computer Interaction TasksInvited Talk and Session Chair at INFORMS 2014
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August 2014
An Adaptive Learning Framework for Personalized Online Epileptic Seizure Prediction.Talk at Brain KDD 2014.
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March 2014
Automated Time Series Modeling and Pattern Learning for Personalized Healthcare Decision-Making SystemsInvited Talk at UTA CSE Colloquiums, Arlington, TX.
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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.
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October 2012
A Probabilistic Prediction Framework for Personalized Online Prediction of Epileptic Seizures,
INFORMS, Phoenix, AZ, October 2012.
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October 2012
Online Monitoring and Prediction of Complex Time Series Events. Phoenix, AZ, USA, Oct 2012.
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June 2012
An Efficient Approach for Automated Online Segmentation of Time Series, International INFORMS, Beijing, China, June 2012.
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November 2011
A Gradient-Based Adaptive Learning Framework for an Online Seizure Prediction, INFORMS, Charlotte, NC, November 2011.
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December 2010
A Novel Reinforcement Learning Framework for Online Adaptive Seizure Prediction, IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, December 2010.
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Projects
- 2014
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June 2014 to Present Respiratory Motion Pattern Analysis for a Personalized PET/CT Scan StrategyRole: Principal Investigator PI: Shouyi Wang
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.
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- 2013
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Sept 2013 to Present Multivariate Time Series Data MiningRole: Principal Investigator PI: Shouyi Wang
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. -
Sept 2013 to Present Online Monitoring and Abnormality Prediction of Mental States and Cognitive ActivitiesRole: Principal Investigator PI: Shouyi Wang
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.
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- 2012
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June 2012 to Present Personalized Early Diagnosis and Prediction System for Epileptic SeizuresRole: Principal Investigator PI: Shouyi Wang
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
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Support & Funding
This data is entered manually by the author of the profile and may duplicate data in the Sponsored Projects section.-
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Nov 2017 to
Present
Developing Data-Driven Assistive Technologies to Improve Human Performance and Quality Control for Large-Scale Manufacturing sponsored by
S & B Industry Research (Foxconn International Fort Worth) - $53000
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Mar 2017 to
Present
Big Data Analytics and Smart Information Fusion System to Improve Efficiency and Decision Making for Apple iPhone Production sponsored by
Foxconn Research/Apple iPhone Manufacturing Department - $240000
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Nov 2017 to
Present
Developing Data-Driven Assistive Technologies to Improve Human Performance and Quality Control for Large-Scale Manufacturing sponsored by
S & B Industry Research (Foxconn International Fort Worth) - $53000
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June 2016 to
Present
Directed Brain Network Modeling to Improve Pre-Surgical Evaluation of Brain Disorders sponsored by
UTA Research Enhancement Award - $10000
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June 2016 to
Present
Directed Brain Network Modeling to Improve Pre-Surgical Evaluation of Brain Disorders sponsored by
UTA Research Enhancement Award - $10000
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Sept 2015 to
Present
Collaborative: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning sponsored by
NSF CMMI - $250000
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May 2015 to
Present
GPU-Accelerated Big Data Computing for Brain Informatics Research sponsored by
NVIDIA - $10000
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Aug 2015 to
July 2016
A System for Neuro-Feedback Anger Management to Prevent Domestic Violence sponsored by
UTA Interdisciplinary Research Program - $20000
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Sept 2015 to
Present
Collaborative: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning sponsored by
NSF CMMI - $250000
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Oct 2014 to
Present
Networking Infrastructure: Campus Networking for Transformative Exascale Research. sponsored by
NSF ACI - $500000
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Sept 2014 to
Aug 2019
I/UCRC Phase I: iPerform - I/UCRC for Assistive Technologies to Enhance Human Performance sponsored by
NSF - $622290
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Oct 2014 to
Sept 2016
Networking Infrastructure: Campus Networking for Transformative Exascale Research sponsored by
NSF - $500000
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June 2014 to
Dec 2014
Predictive Modeling of Respiratory Motion for Patients with Lung Cancer sponsored by
UTA SAVANT CENTER - $2500
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Oct 2014 to
Present
Networking Infrastructure: Campus Networking for Transformative Exascale Research. sponsored by
NSF ACI - $500000
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Aug 2018 to
Aug 2022
ALFA-IoT: ALliance For Smart Agriculture in the Internet of Things Era sponsored by
Department of Agriculture (USDA) - $990000
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Aug 2018 to
July 2019
Decision Analytics for Joint Optimization of Urban Life Environments (JOULE) sponsored by
UTA - $20000
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Aug 2018 to
Aug 2022
ALFA-IoT: ALliance For Smart Agriculture in the Internet of Things Era sponsored by
Department of Agriculture (USDA) - $990000
Patents
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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.
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Other Research Activities
- 2019
- Student Recruiting
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Feb 2019 PhD Openings on Data Mining & Machine Learning & AI
Ph.D. positions are open for Fall 2019 on Data Mining & Machine Learning & AI directions. Graduate students with strong quantitative background and programming skills are encouraged to apply. The research directions include Data Science, Machine Learning, Advanced Analytics, Artificial Intelligence & Deep Learning. If you areinterested, please send your CV to Dr. Shouyi Wang at shouyiw@uta.edu.
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- Student Recruiting
Students Supervised
- Doctoral
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Present
Feng Liu STEM GRADUATE RESEARCH ASSISTAfeng.liu@mavs.uta.edu | (817) 203-1631Sparse Learning Methods for Brain Imaging Analytics and Modeling -
Present
Kin Ming Puk Research Assistant, Office of International Educationkinming.puk@mavs.uta.edu | (817) 272-3169Thesis Topic: 1) Integration of Data Miningand Optimization Techniques for Large Scale Data Mining, 2) Deep Learning and Intelligent Robotics -
Present
Rahilsadat Hosseini Data Analysis and Structured Feature Selection for High Dimensional Data Mining and Knowledge Discovery, The University of Texas at Arlington. -
Present
Yuan Song yuan.song@uta.edu | (682) 408-1176Data Analytics and Text Mining for Finance Data & Management -
May 2015
Kin M Kam PhD Thesis: Nonstationary and Complex Time Series Modeling and Prediction. Data Scientist at CSX https://www.linkedin.com/in/kin-ming-jerry-kam-a0a97531/
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- Master's
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Present
Aditya Sheth Research Assistant, Industrial,Manufacturing,&Systems EngineeringData Mining & Analytics for 1) Computational Neuroscience, 2) Smart Manufacturing Systems -
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Thiru Elangho Raj, Arvind Srinivas Statistical Quality Control and Data Analysis for Advanced Manufacturing Systems. Quality Control Engineer, Foxconn International, Fort Worth. -
May 2016
Daniel Gellerup M.S. Thesis: Discriminating Parkinson’s Disease Using Functional Connectivity And Brain Network Analysis. Manufacturing Engineer at Texstars, Inc.
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Courses
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IE 6318-001
DATA MINING & ANALYTICSThis 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 includedata 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), andalgorithm independent machinelearningmodels. 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 willgain the practical know-how needed to quickly and powerfully apply these techniques to solve data mining and knowledge discovery problems.
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IE 3301-004
ENGINEERING PROBABILITYTopics in engineering that involve random processes. Applications and backgrounds for topics in reliability, inventory systems, and queuing problems, including absolute and conditional probabilities, discrete and continuous random variables, parameter estimation, hypothesis testing, and an introduction to linear regression.
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IE 5317-001
Introduction to StatisticsThis 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.
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IE 5300-001
Data Analytics and ModelingThis 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.
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IE 5317-004
INTRODUCTION TO STATISTICSThe 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.
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IE 5300-001
Topics in Industrial EngineeringTopics 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.
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IE 5300-001
Topics in Industrial EngineeringThis 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.
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IE 3312-001
Engineering EconomyThis 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.
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Service to the University
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Appointed
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Sept 2013 to Present IMSE Research Committee
Industrial and Manufacturing Systems Engineering Research Committee, The University of Texas at Arlington. 2013–present
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Sept 2013 to Present COSMOS Research Member
Faculty of the Center On Stochastic Modeling, Optimization, & Statistics (COSMOS). 2013-present
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Service to the Profession
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Appointed
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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.
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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.
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Elected
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June 2016 to Present Organization Committee
Organization Committee, the International Conference on Brain Informatics & Health (BIH), Omaha, NE, 2016.
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June 2016 to Present Organizer BDBM 2016
International Workshop on Big Data Neuroimaging Analytics for Brain and Mental Health, Omaha, NE, 2016.
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