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Assist Professor at Industrial & Manufacturing Systems Engineering

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. 

Rutgers, the State University of New Jersey
PhD
Industrial & Systems Engineering
Delft University of Technology
MS
Systems and Control Engineering
Harbin Institute of Technology
BS
Systems and Control Engineering
August 2013
Ongoing
Assist Professor
The University of Texas at Arlington
September 2011
August 2013
Research Scientist
University of Washington
October 2012
Ongoing
Membership
Association for Information Systems, Business Administration, The University of Texas at Arlington
October 2009
Ongoing
Membership
Institute for Operations Research and the Management Sciences (INFORMS)
September 2009
Ongoing
Membership
Institute of Industrial Engineers (IIE)
September 2008
Ongoing
Membership
Institute of Electrical and Electronics Engineers (IEEE)
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.

Ongoing Research Directions

Interdisciplinary research is necessary to address complex real-world problems. In my future research, I will continue to seek active cooperation with colleagues 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 future research plans are summarized as follows.
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.

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.

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.

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. 

2013
S. Wang, W. Chaovalitwongse and S. Wong. A Probabilistic Pattern Learning Framework forPersonalized Epileptic Seizure Prediction. IEEE Transactions on Neural Networks andLearning Systems.
Journal Article
Revised and Resubmitted
2013
S. Wang and W. Chaovalitwongse. Piecewise Linear Segmentation of Time Series Using a Data-Driven Threshold With Guaranteed Accuracy. Pattern Recognition Letters.
Journal Article
Revised and Resubmitted
2013
S. Wang, C-J Lin, C. Wu, and W. Chaovalitwongse. Online Adaptive Learning and Prediction ofDriver’s Mental States in a Simulated Driving Environment. IEEE Transactions on Intelligent Transportation Systems.
Journal Article
Revised and Resubmitted
2013
S. Wang, S. Bowen, W. Chaovalitwongse. Multivariate Respiratory Trace Models for PredictingEfficacy of Respiratory-Gated PET/CT Imaging Quality. Physics in Medicine & Biology.
Journal Article
Revised and Resubmitted
2013
S. Wang, J. Gwizdka, W. Chaovalitwongse. Monitoring and Classification of Cognitive MemoryLoad Using Multivariate EEG Measures. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans.
Journal Article
Submitted
2013
S. Wang and W. Chaovalitwongse. An Efficient and Robust Approach for Automated OnlineSegmentation of Time Series Streams. IEEE Transactions on Knowledge and Data Engineering.
Journal Article
Submitted
2012
S. Wang, W. Chaovalitwongse, and R. Babuska. Machine Learning Algorithms in Bipedal RobotControl. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews,Volume: 42, Issue: 5, Pages: 728-743, 2012.
Journal Article
Published
2012
S. Wang, W. Chaovalitwongse, and S. Wong. A Gradient-Based Adaptive Learning Framework forAn Online Seizure Prediction. International Journal of Data Mining and Bioinformatics.
Journal Article
Published
2012
S. Wang, W. Chaovalitwongse, and S. Wong. Online Seizure Prediction Using an Adaptive Learning Approach. IEEE Transactions on Knowledge and Data Engineering, 2012.
Journal Article
In-press
2011
S. Wang and W. Chaovalitwongse. Evaluating and Comparing Forecasting Models. Encyclopaediaof Operations Research and Management Science, Wiley & Sons, 2011.
Book Chapter
Published
2011
S. Wang, O. Seref and W. Chaovalitwongse. Operations Research in Data Mining. Encyclopaedia ofOperations Research and Management Science, Wiley & Sons, 2011.
Book Chapter
Published
2011
S. Wang, C.J. Lin, C. Wu, and W. Chaovalitwongse. Early Detection of Numerical Typing ErrorsUsing Data Mining Techniques. IEEE Transactions on Systems, Man, and Cybernetics, Part A:Systems and Humans, Volume: 41, Issue: 6, Pages: 1199-1212, 2011.
Journal Article
Published
2011
W. Chaovalitwongse, R.S. Pottenger, S. Wang, Y.J. Fan, and L.D. Iasemidis. Pattern-Based andNetwork-Based Classification Techniques for Multichannel Medical Data Signals to Improve BrainDiagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans,Volume: 41, Issue: 5, Pages: 977-988, 2011.
Journal Article
Published
2010
S. Wang, W. Chaovalitwongse, and S. Wong. A Novel Reinforcement Learning Framework forOnline Adaptive Seizure Prediction. Proceedings of IEEE International Conference onBioinformatics & Biomedicine, pp. 499-504, 2010, Hong Kong, China. (Accept rate 17%, and wonStudent Travel Award)
Conference Proceeding
Published
2006
S. Wang, J. Braaksma, R. Babuska, and D. Hobbelen. Reinforcement learning control for bipedrobot walking on uneven surfaces. Proceedings of 2006 International Joint Conference on NeuralNetworks, pp. 4173-4178, 2006, Vancouver, Canada.
Conference Proceeding
Published
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.

Invited
October 2012

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

Invited
October 2012

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

Invited
June 2012

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

Invited
November 2011

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

Invited
December 2010

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

Invited
July 2006

Reinforcement Learning Control for Biped Robot Walking on Uneven Surfaces, International Joint Conference on Neural Networks, Vancouver, Canada, July 2006.

shén jīng wǎng luò
神经网络
1
de|dì|dí
2
guó jì
国际
3
lián xí huì yì
联席会议
4
 
1.神经网络 {shén jīng wǎng luò} nn
2. {de;dì;dí} of; target
3.国际 {guó jì} international
4.联席会议 {lián xí huì yì} joint conference
Invited
Spring 2015
IE 5300 - 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.  
Last Updated on January 18, 2015, 10:51 pm
Spring 2014
IE 5300 - 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.  
Last Updated on January 9, 2014, 1:48 pm
Fall 2013
IE 3312 - Engineering Economy
Office Hours (also by appointment)
DayStartEnd
Monday2:00AM3:30PM
Wednesday2:00AM3:30PM
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.
Last Updated on August 23, 2013, 5:38 pm