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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
University of Texas at Arlington
September 2011
August 2013
Research Scientist
Integrated Brain Imaging Center, University of Washington
October 2012
Ongoing
Membership
Association for Information Systems, Business Administration, 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)
June 2016

CBS News Reported our Brain Computer Interface and Emotion Management Research Project

March 2014

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?

November 2013

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.

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.

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.
Journal Article
Revised and Resubmitted
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
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.
Journal Article
Submitted
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
Published
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
Published
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
Published
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
Published
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
Published
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
Published
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)
Journal Article
Published
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
Published
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
Published
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
Published
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
Published
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.  
Journal Article
Published
2011
S. Wang and W. Chaovalitwongse. Evaluating and Comparing Forecasting Models. Encyclopaedia of Operations Research and Management Science, Wiley & Sons, 2011.
Book Chapter
Published
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
Journal Article
Published
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.
Journal Article
Published
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.
Journal Article
Published
2011
S. Wang, O. Seref and W. Chaovalitwongse. Operations Research in Data Mining. Encyclopaedia of Operations Research and Management Science, Wiley & Sons, 2011.
Book Chapter
Published
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
Published
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. 
Conference Proceeding
Published
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)

Reviewed
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)

Invited
November 2015
Big Data Analytics and Supercomputing for Healthcare

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

Invited
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)

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

Talk at Annual Meeting of Cognitive Neuroscience Society (CNS)

Invited
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)

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

Invited Talk and Session Chair at INFORMS 2014

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

Talk at Brain KDD 2014. 

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

Invited Talk at UTA CSE Colloquiums, Arlington, TX.

Invited
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
June 2014
Ongoing
Respiratory Motion Pattern Analysis for a Personalized PET/CT Scan Strategy
Principal Investigator
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. 

September 2013
Ongoing
Multivariate Time Series Data Mining
Principal Investigator
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.

September 2013
Ongoing
Online Monitoring and Abnormality Prediction of Mental States and Cognitive Activities
Principal Investigator
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.

June 2012
Ongoing
Personalized Early Diagnosis and Prediction System for Epileptic Seizures
Principal Investigator
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

June 2016 -
Ongoing
Directed Brain Network Modeling to Improve Pre-Surgical Evaluation of Brain Disorders
$10,000
September 2015 -
Ongoing
Collaborative: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning
$250,000
May 2015 -
Ongoing
GPU-Accelerated Big Data Computing for Brain Informatics Research
$10,000
October 2014 -
Ongoing
Networking Infrastructure: Campus Networking for Transformative Exascale Research.
$500,000
August 2015 -
July 2016
A System for Neuro-Feedback Anger Management to Prevent Domestic Violence
$20,000
June 2014 -
December 2014
Predictive Modeling of Respiratory Motion for Patients with Lung Cancer
$2,500
Ongoing
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)
Doctoral
Ongoing
S2014 - Present,  Ph.D. Thesis Topic: Data Analysis and Variable Selection for High Dimensional Data using Sparse Learning, The University of Texas at Arlington. 
Doctoral
May 2016
M.S. Thesis: Discriminating Parkinson’s Disease Using Functional Connectivity And Brain Network Analysis, The University of Texas at Arlington.
Master's
May 2015
PhD Thesis: Nonstationary and Complex Time Series Modeling and Prediction, The University of Texas at Arlington, 2015. 
Doctoral
Fall 2016
IE 5317 - Introduction to Statistics
Office Hours
DayStartEnd
Tuesday3:30PM5:00PM
Thursday3:30PM5:00PM
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. 
Last Updated on September 14, 2016, 1:32 am
No Documents Attached
Spring 2016
IE 5300 - Data Analytics and Modeling
Office Hours
DayStartEnd
Tuesday3:30PM5:00PM
Thursday3:30PM5:00PM
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.  
Last Updated on June 4, 2016, 4:00 pm
No Documents Attached
Fall 2015
IE 5317 - INTRODUCTION TO STATISTICS
Office Hours
DayStartEnd
Monday2:00PM4:00PM
Wednesday3:00PM4:00PM
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. 
Last Updated on September 15, 2015, 4:48 pm
No Documents Attached
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
No Documents Attached
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
No Documents Attached
June 2016
Ongoing
Organization Committee

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

Elected
June 2016
Ongoing
Organizer BDBM 2016

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

Elected
June 2015
Ongoing
Editor

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

Appointed
June 2014
Ongoing
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.  

Appointed
September 2013 -
Ongoing
IMSE Research Committee

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

Appointed
September 2013 -
Ongoing
COSMOS Research Member

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

Appointed