Operations Research (OR) is a discipline that involves the development and application of advanced analytical methods to aid complex decisions. This course will provide students with the skills to be able to apply a variety of analytical methods to a diverse set of applications. Methods considered include linear and mixed-integer programming, nonlinear and combinatorial optimization, network models, and machine learning. Focus will be on how to translate real-world problems into appropriate models and then how to apply computational procedures and data so that the models can be used as aids in making decisions. Applications will include improving the operation of a variety of different production and service systems, including healthcare delivery and transportation systems, and also how OR can be used to make better decisions in areas like sports, marketing, and project management. Prerequisites include undergraduate courses in single variable differential and integral calculus and an introductory course in probability.

Prerequisites include undergraduate courses in single variable differential and integral calculus (similar to MA 421) and an introductory course in probability (similar to ST 421 or ST 371 and ST 372)

Typically offered in Fall, Spring, and Summer

Introduction including: applications to economics and engineering; the simplex and interior-point methods; parametric programming and post-optimality analysis; duality matrix games, linear systems solvability theory and linear systems duality theory; polyhedral sets and cones, including their convexity and separation properties and dual representations; equilibrium prices, Lagrange multipliers, subgradients and sensitivity analysis.

Prerequisite: MA 405

Typically offered in Fall only

Engineering economy analysis of alternative projects including tax and inflation aspects, sensitivity analysis, risk assessment, decision criteria. Emphasis on applications.

Prerequisite: Undergrad. courses in engineering economics and ST

Typically offered in Spring only

This course introduces students to the principles of microeconomic analysis applied to decision-making in supply chains. Emphasis will be put on strategic interactions between different decision makers in the supply chain, including suppliers, manufacturers, retailers, and consumers. Topics include classical demand and production theory, pricing and revenue management, competition between firms, and cooperation between and within firms under information asymmetry.

Prerequisite: ISE 135

Typically offered in Fall only

This course provides a comprehensive treatment of humanitarian logistics (HumLog) from an operations research perspective, focusing on the use of quantitative modeling for decision making and best practices disaster management. Background and overview on disaster management will be covered. The four phases of the disaster management cycle are introduced as well as the types of decisions that are made in each phase. Mathematical models are presented for typical humanitarian logistics decisions, such as inventory prepositioning, facility location, transportation, routing and capacity planning.

Prerequisite: ISE 361

Typically offered in Spring only

Manufacturing process engineering, primary, secondary, finishing and assembly processes. Traditional and non-traditional manufacturing processes, group technology, manufacturing analyses and application of economic analyses. Graduate standing in Engineering.

Typically offered in Fall and Summer

Rapid application development (RAD) tools to design and implement database-based applications. This includes: SQL query language, Visual Basic for Applications in database application construction, a standard RAD environment and how to access information in a database, entity/attribute modeling of the database structure, anomalies of database structures that create problems for applications, modeling of application system's functionality, and integrating these tools together to design and implement engineering applications. Examples from manufacturing and production systems. Restricted to advanced undergraduates and graduate students.

Prerequisite: ISE 110

Typically offered in Fall and Spring

Methods used to improve the performance of health care delivery systems with emphasis on patient care cost, access, and quality. Adaptation of lean and six-sigma to rapid and continuous health care systems improvement through organizational and process transformation. Fundamentals of scheduling, staffing, and productivity in health systems employing simulation and optimization. Health care policy and management.

Prerequisite: ST 372, ISE 352, ISE 361, and ISE 441

Typically offered in Fall only

Continuation of ISE 520 with a concentration on the completion of a healthcare systems process improvement project at the sponsoring health care institution. Project must employ the tools and techniques of healthcare systems process improvement. The project is done in conjunction with a diverse and multi-disciplinary team from the healthcare institution. The student must serve as a facilitator and coach, resulting in a project with measured success. Success will be determined by the improvement in patient care as quantified in cost, quality, and access.

Prerequisite: ISE 520

Typically offered in Spring only

This will focus on the use of optimization in Medicine. The main goal of this course is for you to develop an understanding of the recent methodological literature on optimization methods applied to medical decision making. We will cover a broad range of topics, both from the methodological perspective (study models using integer programming, dynamic programming, simulation, etc.) and from the public policy/public health perspective (who are the stake holders, what are the relevant questions modelers can answer, how is the patient taken into account, etc.).

P: ISE/OR 505 or equivalent and ISE 560 or equivalent or permission by instructor

Typically offered in Spring only

This course intends to provide a comprehensive treatment on the use of quantitative modeling for decision making and best practices in the service industries. The goal of this class is to teach students to able to identify, understand, and analyze services; and acquire the quantitative skills necessary to model key decisions and performance metrics associated with services. Students will be exposed both to classical and contemporary examples of challenges and opportunities that arise when working in the service sector.

Prerequisite: ISE 361

Typically offered in Spring only

The objective of this course is to build on your knowledge of computing and data analysis by focusing on programming using the Python language. IN particular, you will learn more about the Python and its ecosystem of libraries, how to use data structures in Python programs, conduct File I/O operations, and perform numerical and scientific computing within Python. This course is designed for senior undergraduate and graduate students to get the basics of the Python language and learn to use it to perform scientific computing within Python with two of its most popular packages in use for heavy data intensive analysis - Numpy and SciPy. Several engineering examples from physics, industrial engineering core courses and general engineering will be used to contextualize the programming examples.

Prerequisites: ST 370 OR ST 371 and ISE 135 OR CSC 111 OR CSC 113 OR CSC 116 or ST 307 OR ST 308 OR ECE 209. Restrictive Statement: Department Approval Required

Typically offered in Fall only

In this course, graduate students will learn basic data science methodologies. Examples of the methodologies include linear regression, generalized linear models, regularization and variable selection, and dimensionality reduction. In addition, students will also learn how to use these methods to solve real-world Industrial Engineering-related problems by analyzing industrial datasets and projects.

Prerequisite: ST 370: "Probability and Statistics for Engineers" or equivalent

Typically offered in Spring only

Introduction to problems of the systems development cycle, including human-machine function allocation, military specifications, display-control compatibility, the personnel sub-system concept and maintainability design. Detailed treatment given to people as information processing mechanisms.

Prerequisite: IE 452 or PSY 340, Corequisite: ST 507 or 515

Typically offered in Spring only

Occupational accident-injury study; morbidity, mortality; investigation and analysis. Hazard control; energy countermeasure strategies; control technology. Impact biomechanics, trauma and survivability. Risk assessment; systems safety analysis. Product design, manufacturing defects, system failures and human error as causative factors. Safety program development. Near-accident reporting. OSHA compliance; standards. Accident, trauma and forensic case studies from manufacturing, motor carrier andconstruction industries.

Typically offered in Spring only

Anatomy, physiology and biomechanics of musculoskeletal system including muscle bone, tendon, ligament, cartilage, nerve. Modeling of tissue and joints with special emphasis on spine and upper extremity. Physical, mathematical, optimization and finite element modeling techniques as applied in biomechanics research.

Prerequisite: BIO 125 or BAE(BIO) 235 or Graduate standing

Anatomical, physiological, and biomechanical bases of physical ergonomics. Strength of biomaterials, human motor capabilities, body mechanics, kinematics and anthropometry. Use of bioinstrumentation, active and passive industrial surveillance techniques and the NIOSH lifting guide. Acute injury and cumulative trauma disorders. Static and dynamic biomechanical modeling. Emphasis on low back, shoulder and hand/wrist biomechanics.

Prerequisite: Graduate standing

Typically offered in Fall only

Planning, design, and development and implementation of comprehensive computer-based information systems to support management decisions. Formal information systems principles; information requirements analysis; knowledge acquisition techniques; information modeling. Information resource management for quality operational control and decision support; system evaluation, process improvement and cost effectiveness.

Prerequisite: CSC 423 or BUS 541

Typically offered in Fall only

Health professional are capable of collecting massive amounts of data and look for best strategies to use this information. Healthcare analytics have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. This course will explore some of the frequently used data science methods in healthcare and examine a compilation of the most recent academic journal articles on the subject. Students are expected to have a strong background in optimization and stochastic modeling.

Prerequisite: ISE 362

Typically offered in Fall only

Basic terminology and techniques for the control of production and service systems including economic order quantity models; stochastic inventory models; material requirements planning; Theory of Constraints; single and mixed model assembly lines ; and lean manufacturing. Emphasis on mathematical models of the interaction between limited capacity and stochastic variability through the use of queueing models to describe system behavior.

Prerequisite: ST 371 or ST 372

Typically offered in Fall only

Basic issues in operating supply chains, using state of the art modeling tools available for their analysis. Emphasis on using engineering models to develop insights into the behavior of these systems.

Prerequisite: ISE 361 and ST 372

Typically offered in Spring only

This course aims to introduce students on the power of digital manufacturing and design technologies, particularly how product data can seamlessly transfer through the entire lifecycle of a manufactured product. Students will also be introduced to methods to design and build plugin apps that interface with the design models. All hands-on modeling and virtual manufacturing exercises will be in Autodesk Fusion 360, a cloud based design and manufacturing software.

R: ISE 316 or Graduate Standing

Typically offered in Fall only

ISE/OR 560 will introduce mathematical modeling, analysis, and solution procedures applicable to uncertain (stochastic) production and service systems. Methodologies covered include probability theory and stochastic processes including discrete and continuous Markov processes. Applications relate to design and analysis of problems, capacity planning, inventory control, waiting lines, and service systems.

Typically offered in Fall only

This course concentrates on design, construction, and use of discrete/continuous simulation object-based models employing the SIMIO software, with application to manufacturing, service, and healthcare. The focus is on methods for modeling and analyzing complex problems using simulation objects. Analysis includes data-based modeling, process design, input modeling, output analysis, and the use of 3D animation with other graphical displays. Object-oriented modeling is used to extend models and enhance re-usability.

Typically offered in Spring only

Special developments in some phase of industrial engineering using traditional course format. Identification of various specific topics and prerequisites for each section from term to term.

Seminar discussion of industrial engineering problems for graduate students. Case analyses and reports.

Typically offered in Fall and Spring

Special developments in some phase of industrial engineering using traditional course format. Identification of various specific topics and prerequisites for each section from term to term.

Independent study providing opportunity for individual students to explore topics of special interest under direction of a member of faculty.

Typically offered in Fall and Summer

Independent study providing an opportunity for individual graduate students to explore advanced topics of special interest under the direction of a member of the faculty.

Typically offered in Fall, Spring, and Summer

Investigation and written report on assigned problems germane to industrial engineering. Maximum of six credits to be earned for MIE degree.

Prerequisite: MIE candidates

Typically offered in Fall, Spring, and Summer

Teaching experience under the mentorship of faculty who assist the student in planning for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

For students in non-thesis master's programs who have completed all credit hour requirements for their degree but need to maintain half-time continuous registration to complete incomplete grades, projects, final master's exam, etc.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

For students in non-thesis master's programs who have completed all credit hour requirements for their degree but need to maintain full-time continuous registration to complete incomplete grades, projects, final master's exam, etc. Students may register for this course a maximum of one semester.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

Thesis research.

Typically offered in Fall, Spring, and Summer

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

Prerequisite: Master's student

Typically offered in Summer only

For student who have completed all credit hour requirements and full-time enrollment for the master's degree and are writing and defending their theses.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

Concepts and application of real-time control of automated manufacturing systems. Development of prototype manufacturing control applications involving introductions to following topics: computer architecture; real-time, multi-tasking operating systems; data modeling; multi-processing systems; local area networks; inter-task communication; and development of multi-tasking control systems. Design development of control system.

Typically offered in Fall only

General integer programming problems and principal methods of solving them. Emphasis on intuitive presentation of ideas underlying various algorithms rather than detailed description of computer codes. Students have some "hands on" computing experience that should enable them to adapt ideas presented in course to integer programming problems they may encounter.

Prerequisite: MA 405, OR (MA,IE) 505, Corequisite: Some familiarity with computers (e.g., CSC 112)

Typically offered in Spring only

Introduction to theory and computational aspects of dynamic programming and its application to sequential decision problems.

Prerequisite: MA 405, ST 421

Typically offered in Spring only

Analysis of economic merits of alternatives including interest and income tax considerations. Risk and sensitivity exploration techniques. Introduction to analytical techniques for multiple objectives or criteria. Use of mathematical programming andcomputers for capital budgeting.

Prerequisites: ISE 311 and ST 371

Typically offered in Fall only

The Bayesian approach to decision making, with numerous applications in engineering and business. Expected value maximization, decision trees, Bayes' theorem, value of information, sequential procedures and optimal strategies. Axiomatic utility theory and controversies, utility of money, theoretical and empirical determination of utility functions and relationship to mean-variance analysis. Brief introduction to multi-attribute problems, time streams and group decisions.

Prerequisite: ST 371 or ST 421

Typically offered in Spring only

Product development course targeted toward the medical device industry. Product design and development, concept generation and selection, parametric feature-based CAD, design for manufacturability (DFM) and assembly (DFA), tolerancing, rapid prototyping, tool design, tool fabrication, and medical device fabrication.

Prerequisite: ISE 515

Typically offered in Spring only

Manufacturing process engineering, primary,secondary, finishing and assembly processes. concurrent engineering, process planning, group technology, manufacturing analyses and application of economic analyses.

Typically offered in Spring only

General principles of operation and programming of automated systems. Automated assembly, automated manufacturing, and inspection systems. Control of automated manufacturing. Industrial logic systems and programmable logic controllers. Computer numerical control, industrial robotics, and computer integrated manufacturing.

Typically offered in Fall and Spring

Introduction to physical theory, process design, analysis, and characterization of micro/nano scale fabrication and manufacturing. The main focus of the course is on the fabrication/manufacturing of important types of microstructures used in micro/nano devices and the techniques and tools used to fabricate and characterize them.

Prerequisite: ISE 316 or graduate standing in the college of engineering

Typically offered in Spring only

An analysis of Production-Inventory systems. Discussion of commonly used planning and scheduling techniques. Introduction to use of math modeling for solution of planning and scheduling problems. Interface with quality control and information systems.

Prerequisite: OR 501 and ST 511

Typically offered in Spring only

The course introduces the concepts and applications of smart manufacturing systems that begin from the machine asset on the factory floor to the higher order information technology systems. Development of prototype smart manufacturing applications involving introduction to topics such as: real-time streaming machine sensor data through machine to machine (m2m) industrial communication protocols; unified namespaces for factory integration of information and operational technology; data modeling and data store architectures specifically for time series analysis and machine vision theory and applications critical to quality inspections at the factory floor.

Typically offered in Spring only

Introduction to graph theory and network theory. In-depth discussion of theory underlying (1) deterministic activity networks (CPM): optimal time-cost trade offs; the problem of scarce resources; (2) probabilistic activity networks (PERT): critical evaluation of underlying assumptions; (3) generalized activity networks (GERT, GAN): applications of signal flow graphs and semi-Markov process to probabilistic branching; relation to the theory of scheduling.

Prerequisite: OR 501, OR(IE,MA) 505

Typically offered in Spring only

Specification of attributes/criteria/objectives for complex decisions. Determination of alternatives, attribute weights and decision-making process. Graphical and weighted evaluation techniques. Multi-attribute utility, multi-objective/goal programming and analytic hierarchy process methodologies. Computer applications and case studies.

Typically offered in Spring only

Exploration of usability of computer technology. Theory and practice of user-centered design for HCI applications. Course focuses on current usability paradigms and principles, psychology of users, iterative and participatory design processes, system requirements specification, prototyping, user support systems, usability evaluation and engineering, interface design guidelines and standards. Application domains include, universal design, virtual reality, and scientific data visualization.

Prerequisite: IE(PSY) 540 or CSC 554

Systems safety engineering. Course familiarizes students with techniques for identifying and recognizing potential safety hazards and the concept of risk assessment. Preliminary Hazard Analysis, Failure Modes and Effects Analysis, System and Subsystem Hazard Analysis, Fault Tree Analysis, Process Safety Management (29CFR1910.119) are explored together with applications to hazard analysis and control. Industrial situations and case studies are employed to illustrate usefulness of various system safety techniques.

Typically offered in Fall only

Human skilled performance as affected by environmental stressors, including noise, vibration, heat, cold, accelerator, pressure altitude, toxic agents and illumination. Physiological effects of stressors and their relationship to health, performanceand, ultimately, to safety. Impact biomechanics and crash survival. Human survival in adverse environments. Combined stressor effects, physiological arousal, fatigue and performance decrement.

Typically offered in Spring only

Fundamentals of ergonomic performance measurement used to assess the effects of environment and system design on human performance. Treatment of topics such as workload measurement, measurement of complex performance, simulator studies, measurement of change, task taxonomies, criterion task sets and statistical methods of task analysis. Problems of laboratory and field research, measurement of change and generalizability of findings.

Prerequisite: PSY 200, ST 507 and 508

Typically offered in Fall only

Fundamentals of human information processing basic to skilled operator performance and the design of displays, controls and complex systems. Treatment of topics such as channel capacity, working memory, long-term memory, decision making, attention and process monitoring. Problems of display and control design and evaluation, evaluation of textual material, and human-computer interaction.

Prerequisite: PSY 200, ST 507 and 508

Typically offered in Spring only

Advanced aspects of human performance research. Qualitative models of human information processing. Characteristics and role of memory in decision making and response execution. Sensory channel parameters, attention allocation, time-sharing of tasks. Situation awareness and workload responses in complext tasks. Limitations of human factors experimentation. Factors in human multiple task performance. Cognitive task analysis and computational cognitave modeling/simulation of user behavior in specific applications.

Prerequisite: ST 507 or 515 or equivalent; IE (PSY) 540, CSC 554 or IE (PSY) 744

Introduction to basic concepts of reliability engineering. Application of probability and statistics to estimate reliability of industrial systems; development of reliability measures; analysis of static and dynamic reliability models; development and analysis of fault trees; analysis of Markovian and non-Markovian models; and optimization of reliability models.

Prerequisite: ST 511

Typically offered in Fall only

Introduction to basic concepts of quality engineering. Statistical process control (SPC) methods, acceptance sampling techniques, concept of parameter design and statistical as well as analytical techniques for its implementation, tolerance analysisand design, components of cost of poor quality and an introduction to quality management.

Prerequisite: OR 501, ST 511

Typically offered in Spring only

Elements of logistics networks. Supply chain design: facility location and allocation; great-circle distances; geocoding. Multi-echelon production and inventory systems; sourcing decision systems. Vehicle routing: exact, approximation, and heuristic procedures; traveling salesman problem; basic vehicle routing problem and extensions; backhauling; mixed-mode transportation system design.

Prerequisite: ISE 453

Typically offered in Spring only

Formulation and analysis of stochastic models with particular emphasis on applications in industrial engineering; univariate, multivariate and conditional probability distributions; unconditional and conditional expectations; elements of stochastic processes; moment-generating functions; concepts of stochastic convergence; limit theorems; homogeneous, nonhomogeneous and compound Poisson processes; basic renewal theory; transient and steady-state properties of Markov processes in discrete and continuous time.

Prerequisite: MA 303, ST 371

Typically offered in Fall only

Introduction of general concepts of stochastic processes. Poisson processes, Markov processes and renewal theory. Usage of these in analysis of queues, from with a completely memoryless queue to one with general parameters. Applications to many engineering problems.

Typically offered in Spring only

Basic discrete event simulation methodology: random number generators, generating random objects, design of discrete event simulation, validation, analysis of simulation output, variance reduction techniques, Markov chain Monte Carlo, simulation optimization. The course has computer assignments and projects. This course is a sequel to ISE/OR 760 Stochastic Models which serves as a prerequisite. This is NOT a software based course! Students who are looking for a class on simulation software, such as Arena and Simio, are recommended to take ISE 562 (master-level simulation class).

Students should have completed a course on stochastic models (similar to ISE 560 or ISE 760) and have a working knowledge of a programming language (e.g., Python, Matlab, R, or others).

Typically offered in Fall and Spring

Study of problems of flows in networks. These problems include the determination of shortest chain, maximal flow and minimal cost flow in networks. Relationship between network flows and linear programming developed as well as problems with nonlinear cost functions, multi-commodity flows and problem of network synthesis.

Prerequisite: OR(IE,MA) 505

Typically offered in Spring only

Gross and functional anatomy of upper extremity; properties of tendons and synovial fluid; epidemiology; disorders of shoulder, elbow, wrist, hands, fingers; biomechanical modeling; personal factors affecting cumulative trauma disorder (CTD) risk, diagnosis and treatment of upper extremity CTDs; wrist splints; workplace ergonomics to alleviate upper extremity CTDs.

Typically offered in Fall only

Gross and fine anatomy of spine, mechanism of pain, epidemiology, in vitro testing, psychophysical studies, spine stability models, bioinstrumentation: intradiscal pressure, intra-abdominal pressure and electromyography. Biomechanics of lifting and twisting, effects of vibration, effects of posture/lifting style, lifting belts, physical models, optimization models, mathematical models, muscle models, finite element models, current trends in medical management and rehabilitation, chiropractic.

Typically offered in Fall only

This course is methodologically focused and a continuation of ISE 762 in Monte Carlo methods. The topics include, but are not limited to, Quasi-Monte Carlo, importance sampling and other advanced variance reduction approaches, derivative estimation, and advanced simulation optimization in continuous and finite spaces. While the application of these techniques to actual simulations is practiced as assignments, the discussion on simulation software and programming will be minimal. A current topic research presentation/paper required.

Prerequisite: (CSC,ECE,IE,OR) 762 and ST 516

Typically offered in Spring only

Advanced topics in some phase of industrial engineering using traditional course format. Identification of various specific topics and prerequisites for each section from term to term.

Typically offered in Fall and Spring

Advanced topics in some phase of system optimization using traditional course format. Identification of various specific topics and prerequisites for each section from term to term.

Typically offered in Fall and Spring

Exploration in depth of a problem area of contemporary interest involving man-machine-environment interface. Class discussion and analysis of research and theory, with special focus on human factors aspects of systems design and operation.

Typically offered in Fall only

Human-systems engineering research topic development, literature evaluation, experimental design, use of research instrumentation, data collection, basic data interpretation, statistical analysis, manuscript preparation.

Typically offered in Spring only

Seminar discussion of industrial engineering problems for graduate students. Case analyses and reports.

Typically offered in Fall and Spring

Study of special advanced topics in area of mathematical programming. Discussion of new techniques and current research in this area. The faculty responsible for this course select areas to be covered during semester according to their preference and interest. This course not necessarily taught by an individual faculty member but can, on occasion, be joint effort of several faculty members from this university as well as visiting faculty from other institutions. To date, a course of Theory of Networks and another on Integer Programming offered under the umbrella of this course. Anticipation that these two topics will be repeated in future together with other topics.

Prerequisite: IE(MA,OR) 505

Typically offered in Spring only

Advanced topics in some phase of system optimization. Identification of various specific topics and prerequisite for each section from term to term.

Typically offered in Fall and Spring

Independent study providing opportunity for individual students to explore topics of special interest under direction of a member of faculty.

Typically offered in Fall, Spring, and Summer

Independent study providing an opportunity for individual graduate students to explore advanced topics of special interest under the direction of a member of the faculty.

Typically offered in Fall, Spring, and Summer

The structure and operation of production planning, scheduling and control systems; emphasis on system structure, capacity planning, master production scheduling, shop loading and supply chain; investigation of current trends.

Typically offered in Fall only

Investigation and written report on assigned problems germane to industrial engineering. Maximum of six credits to be earned for MIE degree.

Prerequisite: MIE candidates

Typically offered in Fall, Spring, and Summer

Teaching experience under the mentorship of faculty who assist the student in planning for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

For students who are preparing for and taking written and/or oral preliminary exams.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

Dissertation Research

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

Prerequisite: Doctoral student

Typically offered in Summer only

For students who have completed all credit hour requirements, full-time enrollment, preliminary examination, and residency requirements for the doctoral degree, and are writing and defending their dissertations.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer