Faculty Leader

Dr. Victoria Chen

Dr. Victoria Chen is Professor and Director of Doctoral Studies in IMSE at UT Arlington.  She is co-founder and Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS).  She previously served as Interim Chair and Director of Research Development, and she received the George and Elizabeth Pickett Professorship in 2015 and 2016.  From 1993-2001, she was on the Industrial and Systems Engineering faculty at the Georgia Institute of Technology. She holds a B.S. in Mathematical Sciences from The Johns Hopkins University, and M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Dr. Chen is actively involved with the Institute for Operations Research and the Management Sciences (INFORMS), serving on the Executive Board and as cluster chair, session organizer/chair, and officers for the INFORMS Section on Data Mining and for the Forum for Women in OR/MS.

Dr. Chen teaches statistics courses, and her research utilizes statistical perspectives to create new decision-making methodologies that accommodate the uncertainty and complexity of real world problems.  She has expertise in design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization.  She has studied a variety of uncertain systems, including inventory forecasting, airline optimization, air quality, water quality, energy systems, green building, waste management, nurse planning, and pain management.  Through her statistics-based approach, she has developed computationally-tractable methods for stochastic dynamic programming, stochastic programming, yield management, environmental decision-making, and simulation.  Her research has been funded by the National Science Foundation, the Environmental Protection Agency, the Department of Energy, the National Institute of Justice, and various foundations and industry partners.

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Dr. Bill Corley

Dr. Bill Corley is Professor of IMSE at UT Arlington, where he has been a faculty member since 1971.  Prior to UT Arlington, Dr. Corley worked for IBM at Cape Kennedy in the space program, where he developed the computerized pre-launch checkout of the Saturn V rocket and for McDonnell Douglas in the defense industry, where he wrote computer simulations of anti-aircraft missiles.  Dr. Corley holds a B.S. in Electrical Engineering and an M.S. in Information Science from the Georgia Institute of Technology, a Ph.D. in Systems Engineering from the University of Florida, and a Ph.D. in Mathematics from UT Arlington.  He is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and a registered professional engineer in the State of Texas.

Dr. Corley’s areas of expertise include systems engineering, mathematical modeling, network analysis, abstract optimization theory, functional analysis, statistics, game theory, fuzzy sets, discrete mathematics, and stochastic processes.  His research interests range from the abstract to the applied.  For example, he has developed abstract optimization theories for set-valued functions and for functions whose variables are sets, both which are now studied widely.  In functional analysis, he has established a new type of hybrid fixed-point theorem.  In statistics, he has defined multivariate order statistics and, with Dr. Kim, developed a general family of recursive probability distributions subsuming various standard ones.  He has discovered a new equilibrium for game theory, applied multiple criteria to network analysis, and used fuzzy sets to assess customer satisfaction.  With Dr. Rosenberger, he has also developed an analytical method for constructing “small world” networks and the Constraint Optimal Selection Technique (COST) approach for linear programming, which is significantly more efficient for large-scale problems than present methods.  He won the 2005 UT Arlington College of Engineering Lockheed Martin Award for teaching and was awarded a faculty research leave during 2006-2007.

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Dr. Chen Kan

Dr. Chen Kan is Assistant Professor of IMSE Department at UT Arlington. He earned his Ph.D. degree in Industrial and Manufacturing Engineering from the Pennsylvania State University in 2018, M.S. degree in the Industrial and Management Systems Engineering from the University of South Florida in 2012, and B.S. degree in Electrical Engineering from the China University of Mining and Technology, Beijing in 2010. Dr. Kan’s research interests include: 1) sensor-based modeling and monitoring of advanced manufacturing systems for quality control and operational improvement, and 2) integrating engineering knowledge with data science methods for the identification of disease patterns and monitoring of disease progression to support personalized medical decisions. Dr. Kan’s current research focuses on time-series analysis and high-dimensional data stream modeling at the interface between machine learning, nonlinear dynamics, and large-scale system control/optimization. Dr. Kan’s research has been applied in various areas including additive manufacturing, ultraprecision machining, unmanned aerial/ground vehicles, robotics, cardiac care, and mental health. His research also includes the design and development of mobile/web apps and Internet-of-Things devices. He has served as border director of IISE Data Analytics & Information Systems Division and track/session chairs in INFORMS and IISE annual meetings. He is a member of INFORMS, IISE, and IEEE.

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Dr. Jay Rosenberger

Dr. Jay Rosenberger is Professor and Director of Research Development in IMSE at UT Arlington.  He previously served as Director of COSMOS, and he is currently serving as Associate Director of the Center for Transportation Equity, Decisions & Dollars. He has a B.S. in Mathematics from Harvey Mudd College, an M.S. in Industrial Engineering from the University of California at Berkeley, and a Ph.D. in Industrial Engineering from the Georgia Institute of Technology. His research interests include mathematical optimization and simulation in transportation, energy, defense, and health care. He is the original developer of SimAir, a simulation of airline operations, which is currently used by many airlines and airline consulting firms around the world. Dr. Rosenberger’s graduate research on airlines won the First Place 2003 Pritsker Doctoral Dissertation award. Prior to joining UT Arlington, Dr. Rosenberger worked in the Operations Research and Decision Support Department at American Airlines, where he researched inventory control and revenue management for cargo operations. At UT Arlington, Dr. Rosenberger teaches courses in engineering probability, operations research, linear programming, and combinatorial optimization. He is a member of the Institute of Industrial Engineers and has served as chair and cluster chair of the Institute for Operations Research and the Management Sciences (INFORMS) Section on Health Applications. He is currently an associate editor of Omega: The International Journal of Management Science.

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Dr. Shouyi Wang

Dr. Shouyi Wang is an Associate Professor of IMSE Department at UT Arlington. Before joining the faculty at UT Arlington, Dr. Wang worked as a research associate in the Department of Industrial and Systems Engineering and the Integrated Brain Imaging Center (IBIC) at the University of Washington from 2011-2013. He earned his B.S. degree in Systems and Control Engineering from Harbin Institute of Technology, Harbin, China, M.S. degree in Systems and Control Engineering from Delft University of Technology, Delft, Netherlands, and Ph.D. degree in Industrial and Systems Engineering from Rutgers University, New Brunswick, New Jersey. Dr. Wang has interests in data mining, machine learning, pattern recognition, multivariate process monitoring and prediction, applied operation research, human-centered computing, and interactive intelligent human-machine systems. He has developed mathematical theories and algorithms to frame, model and optimize complex systems, and solve large-scale data mining and knowledge discovery problems in engineering and science. He has conducted research projects on intelligent learning control systems for humanoid walking robots, personalized healthcare online monitoring and decision-making systems using multivariate physiological signals, functional and diagnostic brain imaging analysis and brain network modeling, decision models for optimizing personalized motion management during PET/CT imaging for radiation therapy planning, real-time prediction/detection of mental states and cognitive activities using brain-computer interfaces. He is a member of Institute of Industrial Engineers (IIE), Institute for Operations Research and the Management Sciences (INFORMS), and Institute of Electrical and Electronics Engineers (IEEE).

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