Wm Michael Barnes '64 Department of Industrial and Systems Engineering

Industrial and systems engineering is an engineering discipline devoted to the design, installation, improvement and control of integrated systems of people, materials, and facilities in a wide range of organizations that produce goods or render services. Like other engineering fields, industrial engineering is concerned with solving problems through the application of specialized knowledge in mathematics and science, as well as the principles of engineering. Two major distinctions of our discipline, among the engineering disciplines, is the unique focus of industrial and systems engineering on human factors and the quantification and systematic removal of uncertainty from production systems. Industrial and systems engineering has five major focus areas: advanced manufacturing, operations research, data sciences and machine learning, health and human systems, and systems engineering.

An important characteristic of industrial and systems engineering is its systems approach to integrate the basic resources of production and service systems and other relevant resources, such as information and energy, in such a way as to create a smooth, efficient and competitive operation within an enterprise. Industrial and systems engineers are needed in virtually all types of enterprises, ranging from industries such as manufacturing, distribution, logistics, transportation, and construction; service sectors such as health care, telecommunications, retail, banking, and engineering consulting to government agencies, military, and non-profit organizations.

The mission of the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering is to serve the state, nation, and global community by educating industrial engineering students to be well founded in engineering fundamentals and to have the knowledge and skills required to design, develop, improve, implement and control sophisticated production and service systems in an environment characterized by complex technical and social challenges. Throughout this educational process, students will be instilled with the highest standards of professional and ethical behavior. It is the intent of the undergraduate industrial engineering program to equip its graduates to achieve the following accomplishments a few years after graduation:

Graduates are successful in advancing the practice of industrial engineering.
Graduates are providing leadership in their respective organizations exemplifying ethical standards and practices.
Graduates are actively engaged in lifelong learning through professional development.

The undergraduate program in Industrial Engineering at Texas A&M University is accredited by the Engineering Accreditation Commission of ABET, www.abet.org.

Industrial and systems engineering students may participate in one of two accelerated programs: the Accelerated Bachelor of Science and Master of Public Health or the Accelerated Bachelor of Science and Master of Science in Finance. Students who successfully complete either of these programs graduate with both degrees in just five years. The department also has a combination program for academically qualified students who want to take selected graduate courses in industrial and systems engineering and receive both graduate and undergraduate credit by meeting specific requirements.

Students may also participate in the Undergraduate Minor Program offered by Texas A&M University. This program is usually comprised of 15 to 18 hours, some of which may be substituted as technical electives. The most common minors are math, business and engineering project management. The department encourages students to participate in industrial internships or the Cooperative Education Program to acquire practical experience to complement their industrial engineering education. Internships are generally encouraged during the summer months only. Students who participate in the Co-op program during three academic semesters may count the three credit hours as a technical elective in their curriculum.

Graduate degrees including the Master of Science (MS), Master of Engineering (MEng), and Doctor of Philosophy (PhD) are also offered by the department in addition to the Bachelor of Science in Industrial Engineering and the two accelerated programs. For graduate degree information, please see the Texas A&M University Graduate Catalog.

Before commencing course work in the major, students must be admitted to the major or have the approval of the department.

Data Engineering (DAEN)
Industrial and Systems Engineering (ISEN)

Data Engineering (DAEN)

DAEN 210 Uncertainty Modeling

Credits 3. 3 Lecture Hours. Models and methods for exploration of data based on probability and statistics; random variables, expectation, data collection, distribution fitting, goodness of fit tests, point estimates and interval estimates and central limit theorem. Prerequisite: Grade of C or better STAT 211.

DAEN 300 Data Engineering Coding Experience I

Credit 1. 0 Lecture Hours. 3 Lab Hours. Application of computational tools to model and solve data engineering problems primarily involving machine learning and optimization techniques. Prerequisite: Grade of C or better in ECEN 360; concurrent enrollment in DAEN 321 and DAEN 331; junior or senior classification.

DAEN 301 Data Engineering Coding Experience II

Credit 1. 0 Lecture Hours. 3 Lab Hours. Application of computational tools to model and solve data engineering problems involving stochastic systems, reinforcement learning, ensemble learning and data visualization. Prerequisites: Grade of C or better in DAEN 300; concurrent enrollment in DAEN 323 and DAEN 328; junior or senior classification.

DAEN 321 Quantitative Models for Statistical and Machine Learning

Credits 3. 3 Lecture Hours. Principles of parameter estimation, confidence interval, p-values, hypothesis testing, design of experiments, model building, multiple regression, ANOVA, statistical quality control, Shewhart charts, CUSUM, EWMA, and residual-based control charts. Prerequisite: Grade of C or better DAEN 210; junior senior classification.

DAEN 323 Statistical Learning and Decisions

Credits 3. 3 Lecture Hours. Stochastic systems components; stochastic process models using Markov chains; Markov-decision processes; use of multi-armed bandit problems; exploration and exploitation; reinforcement learning; Bayesian updates; ensemble learning with bagging, boosting and stacking. Prerequisite: Grade of C or better in DAEN 321; junior or senior classification.

DAEN 328 Data Engineering for Humans

Credits 3. 3 Lecture Hours. Human considerations and constraints for data engineering, including human factors of data visualization; data processing and analysis of human physiological, psychological and performance data; the role of human biases in model development, analysis and interpretation; human factors of interactions with machine learning-based artificial intelligence tools in complex systems such as healthcare, manufacturing and transportation. Prerequisites: Junior senior classification.

DAEN 331 Optimization of Analytics

Credits 3. 3 Lecture Hours. Mathematical optimization algorithms with applications to data analytics; primary topics include convexity, gradient and subgradient methods and their variants, Newton’s and quasi-Newton methods, Frank-Wolfe, duality, derivative-free optimization. Prerequisites: Grade of C or better in MATH 251 or MATH 253; grade of C or better in MATH 304 or MATH 323.

DAEN 399 Professional Development

Credits 0. 0 Lecture Hours. Participation in an approved high-impact learning practice; reflection on professional outcomes from the National Society of Professional Engineers’ Engineering Body of Knowledge; documentation and self-assessment of learning experience at mid-curriculum point. Must be taken on a satisfactory/unsatisfactory basis. Prerequisite: Junior or senior classification.

DAEN 400 Case Studies in Data Engineering

Credits 3. 3 Lecture Hours. Exploration of a set of diverse case studies; application of analytical and computational tools to model and develop solutions to open ended problems from different application areas; development of writing capabilities through the writing of case study reports. Prerequisite: Grade of C or better DAEN 301.

DAEN 410 Optimization Algorithms for Data Engineering

Credits 3. 3 Lecture Hours. . Development of optimization skills for solving problems faced in data engineering; introduction to quantitative solution of optimization problems; exploration of branch and bound, and other IP techniques; methodologies for network flow algorithms, dynamic programming, and nonlinear optimization algorithms, and their applications in data engineering problems. Prerequisite: Grade of C or better in DAEN 331; junior or senior classification.

DAEN 420 Smart Manufacturing

Credits 3. 3 Lecture Hours. Hands-on learning experiences of sensing, measurement and elementary prognostic technologies for modern manufacturing operations; introduction to the emerging challenges and opportunities for harnessing information to substantially improve quality assurance, process design and discovery resulting from advances in sensors, machine tool and plant-floor and a range of system-wide sensor technologies. Prerequisites: Grade of C or better DAEN 321; junior or senior classification.

DAEN 427/ISEN 427 Decision and Risk Analysis

Credits 3. 3 Lecture Hours. Overview of the state of the art in descriptive and prescriptive theories of decision making under uncertainty with emphasis on the ways in which human decisions depart from normative models of rationality; analytical foundations stemming from several disciplines, economics, psychology, management science; application in engineering systems will be considered. Prerequisites: Grade of C or better in ISEN 310, DAEN 321, or STAT 212. Cross Listing: ISEN 427/DAEN 427.

DAEN 429 Data Analytics II

Credits 3. 3 Lecture Hours. Deep learning, including basic machine learning, supervised learning, logistic regression, loss functions, neural networks, optimization, error back-propagation, regularization and generalization, convolutional neural networks, recurrent neural networks, attention models, applications to natural language processing and computer vision. Prerequisites: Grade of C or better in DAEN 301 and DAEN 323; junior or senior classification.

DAEN 430 Forecasting Using Machine-Learning Approaches

Credits 3. 3 Lecture Hours. Forecasting principles and methods, including point and interval forecasts; accuracy; statistical methods in the context of forecasting, including exponential smoothing and Auto Regressive Integrated Moving Average (ARIMA), exogenous variables, seasonality and trends; tree-based models for predictions, prophet models, probabilistic forecasts and predictive and prescriptive analytics. Prerequisites: Grade of C or better in DAEN 321; junior or senior classification.

DAEN 459 Capstone Senior Design Planning

Credits 3. 2 Lecture Hours. 3 Lab Hours. First in a two-course sequence for the capstone senior design experience; formation of a senior design team, visitation with the team sponsor, preparation of the groundwork for the project, preparation of the project charter and collection or acquisition of initial set of data; provision of instructions on different aspects of capstone design, including ethics, design constraints, applicable standards, project management, report writing specifications and requirements, and oral and visual presentations. Prerequisite: Grade of C or better in DAEN 301; concurrent enrollment in DAEN 400, ISEN 427/DAEN 427, and DAEN 429.

DAEN 460 Capstone Senior Design

Credits 3. 1 Lecture Hour. 6 Lab Hours. Second course in a two-course sequence for the capstone senior design experience; continuation of work on the senior design project in teams; data collection, analysis, application of data engineering methods and tools and development of recommendations considering design constraints, evaluation of alternative design, and application of relevant standards; engagement in oral presentations and creation of the project report, with relevant feedback provided during the semester. Prerequisite: Grade of C or better in DAEN 459.

DAEN 489 Special Topics in...

Credits 1 to 4. 0 Lecture Hours. 1 to 4 Other Hours. Selected topics in an identified area of data engineering. May be repeated for credit. Prerequisite: Junior or senior classification; approval of instructor.

DAEN 491 Research

Credits 1 to 4. 1 to 4 Other Hours. Research conducted under the direction of faculty member in data engineering. May be repeated for credit. Prerequisites: Junior or senior classification and approval of instructor.

Industrial and Systems Engineering (ISEN)

ISEN 210 Deterministic Optimization Modeling and Design

Credits 2. 2 Lecture Hours. Development and application of fundamental deterministic optimization models; emphasis on formulating real-world problems as linear, integer, and network flow models; computer optimization software will be used to solve the linear and integer models. Prerequisites: Grade of C or better in MATH 152 and ENGR 102.

ISEN 230 Informatics for Industrial Engineers

Credits 3. 3 Lecture Hours. Structured programming concepts for implementing mathematical and statistical models in industrial engineering problems; emphasis on introductory production and service system problems and computer-based approaches to solve the problems; engineering applications of probability and statistics concepts. Prerequisites: CSCE 206, CSCE 111, CSCE 120, CSCE 121, or CSCE 110 or equivalent; concurrent enrollment in STAT 211.

ISEN 281 Essentials of Modern Manufacturing Methods for Engineering Design

Credits 3. 2 Lecture Hours. 3 Lab Hours. . Fundamental basis from materials and manufacturing methods selection in engineering design; basic elements and theory of a range of materials manufacturing methods, with specific emphasis on recent developments in the field including rapid prototyping 3D, computer control of machines - tools, automation robotics micro-fabrication and metrology. Prerequisite: Grade of C or better in ENGR 217/PHYS 217 or PHYS 217/ENGR 217, or concurrent enrollment.

ISEN 285 Directed Studies

Credits 1 to 4. 1 to 4 Other Hours. Problems of limited scope in industrial engineering approved on an individual basis intended to promote independent study. Prerequisite: Approval of department head.

ISEN 289 Special Topics in...

Credits 1 to 5. 1 to 5 Other Hours. Selected topics in an identified area of Industrial Engineering. May be repeated for credit. Prerequisites: Approval of instructor.

ISEN 291 Research

Credits 1 to 4. 1 to 4 Other Hours. Research conducted under the direction of faculty member in industrial and systems engineering. May be taken four times for credit. Prerequisites: Freshman or sophomore classification and approval of instructor.

ISEN 302 Economic Analysis of Engineering Projects

Credits 2. 2 Lecture Hours. Principles of economic equivalence; time value of money; analysis of single and multiple investments; comparison of alternatives; capital recovery and after-tax analysis of economic projects. Prerequisites: Grade of C or better in MATH 152 or MATH 172; also taught at Qatar campus.

ISEN 310 Uncertainty Modeling for Industrial Engineering

Credits 3. 3 Lecture Hours. Models and methods based on probability and statistics for industrial engineering applications; random variables, expectation, distribution fitting, reliability of systems, central limit theorem and interval estimates in the context of production and service systems. Prerequisites: Grade of C or better in ISEN 230 or concurrent enrollment; grade of C or better in STAT 211.

ISEN 311 Introduction to Data Analytics and Engineering

Credits 3. 3 Lecture Hours. Basic concepts in data engineering, including data acquisition, data management and models for learning with associated algorithms; iterative algorithms; tree-based and regression-based classification; graphs and graph-based methods; clustering; neural networks basics and their training; data structures for storing and processing data; introduction to databases. Prerequisites: Grade of C or better in CSCE 110, CSCE 111, CSCE 120, CSCE 121, or CSCE 206; grade of C or better in STAT 211; junior or senior classification.

ISEN 320 Operations Research I

Credits 3. 3 Lecture Hours. Development and application of fundamental deterministic optimization models and solution methods; focus on quantitative modeling and formulation of linear, integer, and network flow problems; use of computer optimization software to model and solve real-life problems. Prerequisites: Grade of C or better in MATH 304 or MATH 323.

ISEN 330 Human Systems Interaction

Credits 3. 3 Lecture Hours. Principles of human factors and ergonomics; emphasis on design to support human capabilities, limitations, and interaction tendencies in sociotechnical work systems; topics include human information processing, physiological and biomechanical functioning, and implications for design of the workplace and jobs; case studies in manufacturing, medicine, aerospace, ground transportation, and computer interaction. Prerequisite: Grade of C or better in MEEN 221; junior or senior classification.

ISEN 340 Operations Research II

Credits 3. 3 Lecture Hours. Probabilistic methods for industrial and service systems; stochastic processes used in industrial engineering, including Poisson processes and discrete and continuous-time Markov chains; applications to production operations, inventory control, revenue management, quality control, reliability, digital simulation and finance. Prerequisites: Grade of C or better in MATH 304 or MATH 323; grade of C or better in ISEN 310, STAT 212, or ECEN 303; junior or senior classification.

ISEN 350 Quality Engineering

Credits 3. 2 Lecture Hours. 3 Lab Hours. Strategic approach to implementing quality, process and business improvement methods using data analysis tools; total quality management and six sigma approaches to define, measure, analyze, improve and control processes; principles of lean engineering; control charts; process capability analysis; basic metrology, applied statistics, lean principles and process capability. Prerequisites: Grade of C or better in ISEN 310 and ISEN 230.

ISEN 355 System Simulation

Credits 3. 2 Lecture Hours. 3 Lab Hours. Systems simulation structure, logic and methodologies; development of simulation models; data handling methods; analysis of simulation data; verification and validation; system simulation languages, models and analysis; applications to industrial situations. Prerequisites: Grade of C or better in ISEN 230 and ISEN 310.

ISEN 360 Lean Thinking and Lean Engineering

Credits 3. 3 Lecture Hours. Philosophical, managerial, and operational principles of lean thinking within the context, tools, and practices of lean engineering; focuses on principles and practices of lean engineering that are applicable across industry types; includes design of lean systems, lean cell design, modeling of lean manufacturing systems and operation of manufacturing cells; queuing network theory for the analysis of lean systems. Prerequisite: Grade of C or better in STAT 211; junior or senior classification.

ISEN 370 Production Systems Engineering

Credits 3. 3 Lecture Hours. Principles, models, and techniques for planning and analysis of production and distribution systems; application of linear, integer, and nonlinear optimization models and solution methods for aggregate planning, supply chain planning, push (MRP) and pull (JIT) material flow management, inventory control under deterministic and stochastic demands, operations scheduling, and production scheduling. Prerequisites: Grade of C or better in ISEN 230 and ISEN 320.

ISEN 399 Professional Development

Credits 0. 0 Other Hours. Participation in an approved high-impact learning practice; reflection on professional outcomes from engineering body of knowledge; documentation and self-assessment of learning experience at mid-curriculum point. Prerequisites: Grade of C or better in ISEN 210 and ISEN 230; senior classification.

ISEN 405 Facilities Design and Material Handling

Credits 3. 3 Lecture Hours. Principles of facilities location, layout, and material handling systems and to practice designing facilities; modeling, design, and analysis techniques; methodologies in facilities location, layout, and material handling; integration of ergonomics analysis techniques and their implications on design, layout, safety and quality. Prerequisites: Grade of C or better in ISEN 210 and ISEN 320; junior or senior classification.

ISEN 410 Advanced Engineering Economy

Credits 3. 3 Lecture Hours. Principles of economic equivalence; borrowing, lending, and investing; establishing minimum attractive rate of return; replacement analysis; capital budgeting; uncertainty analysis; decision trees. Prerequisites: Grade of C or better in ISEN 210 or ISEN 302; junior or senior classification.

ISEN 411 Engineering Management Techniques

Credits 3. 3 Lecture Hours. Techniques relating to managing engineering activities; engineer's transition into management; engineering managerial functions; motivation of individual and group behavior; productivity assessment/improvement; managing the quality function and communications. Prerequisite: Grade of C or better in ISEN 350; senior classification.

ISEN 413 Advanced Data Analytics for Industry

Credits 3. 3 Lecture Hours. Data mining; linear discriminant analysis (LDA), principal component analysis (PCA) and other methods; classification, clustering, and mining, information extraction; dealing with uncertainty, Bayesian inference; neural models, regression and feature selection. Prerequisites: Grade of C or better in ISEN 310 and ISEN 350; junior or senior classification.

ISEN 414 Total Quality Engineering

Credits 3. 2 Lecture Hours. 3 Lab Hours. Principles of total quality engineering; total quality management philosophy, engineering approaches for designing quality into products and processes; off-line experimentation methods for the robust design; emphasis on teamwork and continuous quality improvement. Prerequisite: Grade of C or better in STAT 211; junior or senior classification.

ISEN 425 Design and Analysis of Industrial Systems with Simulation

Credits 3. 2 Lecture Hours. 3 Lab Hours. In-depth study into the design-modeling and subsequent analysis of contemporary production/service systems; factory/service systems are modeled using the ARENA/SIMAN V simulation-animation language; emphasis is placed on the critical analysis of alternative flow designs of modeled systems using flow and economic parameters to assess system improvement. Prerequisites: Grade of C or better in ISEN 210 and ISEN 355.

ISEN 427/DAEN 427 Decision and Risk Analysis

Credits 3. 3 Lecture Hours. Overview of the state of the art in descriptive and prescriptive theories of decision making under uncertainty with emphasis on the ways in which human decisions depart from normative models of rationality; analytical foundations stemming from several disciplines, economics, psychology, management science; application in engineering systems will be considered. Prerequisite: Grade of C or better in ISEN 310, DAEN 321, or STAT 212. Cross Listing: DAEN 427/ISEN 427.

ISEN 432/AERO 436 Human Factors Engineering for Aerospace Designs

Credits 3. 3 Lecture Hours. Physiological and psychophysiological issues encountered in aviation and space environments and their effect on human cognitive and physical performance; survey methods for human workload and performance evaluations; apply human-systems design principles to an aerospace design project. Prerequisites: Junior or senior classification. Cross Listing: AERO 436/ISEN 432.

ISEN 433 Biomechanics of Work

Credits 3. 3 Lecture Hours. Mechanical behavior of the human musculoskeletal system and component tissues during physical activity; fundamental biomechanical concepts, principles, tools and methods for the measurement of human physical attributes, evaluation of human mechanical capabilities and modeling of human musculoskeletal system; application of concepts to improve performance or prevent injuries during daily activities. Prerequisite: Grade of C or better in ISEN 330; junior or senior classification.

ISEN 434 Human Error and Resilient System Design

Credits 3. 3 Lecture Hours. Human error from a sociotechnical systems perspective; role of error in complex system failures; human behavioral modes and system design factors; analytical methods for defining the roles and impact of errors in large-scale system accidents; real-world case studies. Prerequisites: Grade of C or better in ISEN 330; junior or senior classification.

ISEN 440 Systems Thinking

Credits 3. 3 Lecture Hours. Systems thinking process, systems of systems and the fundamental considerations associated with the engineering of large-scale systems, or systems engineering including systems modeling, design and the system development process. Prerequisites: Grade of C or better in MATH 304; junior or senior classification.

ISEN 441 System Reliability Engineering

Credits 3. 3 Lecture Hours. Methods of system modeling and analysis to predict and enhance the reliability, maintainability, and availability of complex engineered systems including component and systems reliability models, system life prediction, and data collection and analysis of failure and repair distributions. Prerequisites: Grade of C or better in STAT 211 or equivalent; junior or senior classification.

ISEN 442 Organizational Systems

Credits 3. 3 Lecture Hours. Role of people and organizations in the design and development of complex engineered systems; providing engineers with the skills needed to effectively manage large-scale system development programs. Prerequisites: Grade of C or better in ISEN 330; junior or senior classification.

ISEN 453 Manufacturing Operations

Credits 3. 3 Lecture Hours. Analytical principles of manufacturing systems design, analysis and control; emphasis placed on stochastic analysis; role of variability and impact on cycle time; push versus pull production strategies including Kanban and constant WIP control; probability, queuing theory, Little’s Law, heavy traffic approximations, and queuing networks. Prerequisites: Grade of C or better in ISEN 340 and MATH 304; junior or senior classification.

ISEN 460 Capstone Senior Design

Credits 3. 1 Lecture Hour. 6 Lab Hours. Engineering design including identification of a problem; development, analysis and evaluation of alternative solutions; and recommendations for and, where possible, development of systems improvement tools; application of experience and training to provide a product or solution that helps company clients; balancing client needs with academic requirements. Prerequisite: Grade of C or better in ISEN 210, ISEN 330, ISEN 340, ISEN 350, ISEN 355 and ISEN 370; junior or senior classification.

ISEN 485 Directed Studies

Credits 1 to 6. 1 to 6 Other Hours. Permits work on special project in industrial engineering; project must be approved by department head. May be taken for credit up to 6 hours. Prerequisites: Senior classification in industrial engineering.

ISEN 489 Special Topics in...

Credits 1 to 5. 1 to 5 Lecture Hours. 0 to 5 Lab Hours. In-depth study of areas of current student interest and recent advances; normally used for first time offering of new courses. Prerequisite: Approval of instructor.

ISEN 491 Research

Credits 1 to 4. 1 to 4 Other Hours. Research conducted under the direction of faculty member in industrial and systems engineering. May be taken for credit up to 6 hours. Prerequisites: Junior or senior classification and approval of instructor.

Aprahamian, Hrayer, Associate Professor
Industrial & Systems Engineering
PHD, Virginia Tech, 2018

Banerjee, Amarnath, Professor
Industrial & Systems Engineering
PHD, University of Illinois at Chicago, 1999

Bennett, Kem, Senior Professor
Industrial & Systems Engineering
PHD, Texas Tech University, 1970

Bukkapatnam, Satish T, Professor
Industrial & Systems Engineering
PHD, Pennsylvania State University, 1997

Butenko, Sergiy I, Professor
Industrial & Systems Engineering
PHD, University of Florida, 2003

Currie-Gregg, Nancy J, Professor of the Practice
Industrial & Systems Engineering
PHD, University of Houston, 1997

Curry, Guy L, Senior Professor
Industrial & Systems Engineering
PHD, University of Arkansas, 1971

Do, Michael D, Professor of the Practice
Industrial & Systems Engineering
PHD, University of Arizona, 2012

Eckman, David J, Assistant Professor
Industrial & Systems Engineering
PHD, Cornell University, 2019

Eksin, Ceyhun, Associate Professor
Industrial & Systems Engineering
PHD, University of Pennsylvania, 2015

Elizondo, Jon P, Lecturer
Industrial & Systems Engineering
PHD, Texas A&M University, 2021

Elwany, Alaa, Associate Professor
Industrial & Systems Engineering
PHD, Georgia Institute of Technology, 2009

Erraguntla, Madhav, Professor of the Practice
Industrial & Systems Engineering
PHD, Texas A&M University, 1996

Feldman, Rich, Senior Professor
Industrial & Systems Engineering
PHD, Northwestern University, 1975

Ferris, Thomas K, Associate Professor
Industrial & Systems Engineering
PHD, University of Michigan at Ann Arbor, 2010

Garcia, Alfredo A, Professor
Industrial & Systems Engineering
PHD, University of Michigan, 1997

Geunes, Joseph Patrick, Professor
Industrial & Systems Engineering
PHD, Pennsylvania State University, 1999

Gutman, David Huckleberry, Assistant Professor
Industrial & Systems Engineering
PHD, Carnegie Mellon University, 2019

Idan, Lihi, Assistant Professor
Industrial & Systems Engineering
PHD, Yale University, 2022

Jeong, Jihoon, Assistant Professor
Industrial & Systems Engineering
PHD, University of Texas at Austin, 2020

Lawley, Mark A, Professor
Industrial & Systems Engineering
PHD, University of Illinois at Urbana-Champaign, 1995

Lee, Jaesung, Assistant Professor
Industrial & Systems Engineering
PHD, University of Wisconsin - Madison, 2022

Li, Yan, Assistant Professor
Industrial & Systems Engineering
PHD, Georgia Institute of Technology, 2024

Malave, Ceasar, Professor
Industrial & Systems Engineering
PHD, University of South Florida, 1987

Maros, Marie, Assistant Professor
Industrial & Systems Engineering
PHD, KTH Royal Institute of Technology, 2019

Mayer, Richard John, Professor of the Practice
Industrial & Systems Engineering
PHD, Texas A&M University, 1988

Miller, Scott S, Lecturer
Industrial & Systems Engineering
MBA, University of Houston, 1996

Momcilovic, Petar, Associate Professor
Industrial & Systems Engineering
PHD, Columbia University, 2003

Moreno-Centeno, Erick, Associate Professor
Industrial & Systems Engineering
PHD, University of California at Berkeley, 2010

Ntaimo, Lewis, Professor
Industrial & Systems Engineering
PHD, University of Arizona, 2004

Park, Jangho, Assistant Professor
Industrial & Systems Engineering
PHD, Virginia Tech, Blacksburg, 2021

Parsaei, Hamid R, Professor
Industrial & Systems Engineering
PHD, University of Texas Arlington, 1984

Pei, ZJ, Professor
Industrial & Systems Engineering
PHD, University of Illinois, 1995

Rodgers, Annette Milagros, Associate Professor of the Practice
Industrial & Systems Engineering
BS, Texas A&M University, 1982

Sagapuram, Dinakar, Associate Professor
Industrial & Systems Engineering
PHD, Purdue University, 2013

Sasangohar, Farzan, Associate Professor
Industrial & Systems Engineering
PHD, University of Toronto, 2015

Smith, Donald R, Senior Associate Professor
Industrial & Systems Engineering
PHD, University of Arkansas, 1973

Tuo, Rui, Associate Professor
Industrial & Systems Engineering
PHD, Chinese Academy of Sciences, 2013

Valdez Flores, Ciriaco, Professor of the Practice
Industrial & Systems Engineering
PHD, Texas A&M University, 1987

Vazquez, Jose A, Senior Lecturer
Industrial & Systems Engineering
MA, University of Iowa, 1986

Wang, Shiren, Professor
Industrial & Systems Engineering
PHD, Florida State University, 2006

Wang, Yen J, Senior Lecturer
Industrial & Systems Engineering
PHD, Northwestern University, 1991

Zahabi, Maryam, Associate Professor
Industrial & Systems Engineering
PHD, North Carolina State University, 2017

Zhang, Shixuan, Assistant Professor
Industrial & Systems Engineering
PHD, Georgia Tech, 2022

Zhang, Xudong, Professor
Industrial & Systems Engineering
PHD, University of Michigan Ann Arbor, 1997