Systems Engineering

Systems engineering is an interdisciplinary approach and means to enable the realization of successful systems by defining customer needs and required functionality early in the development cycle. Systems engineers are responsible for the design and management of complex systems guided by systems requirements. There is a growing need for engineers who are concerned with the whole system and can take an interdisciplinary and top down approach. Systems engineers need to be problem definers, not just problem solvers, and be involved with a system through its life cycle, from development through production, deployment, training support, operation, and disposal.

Graduate programs leading to the M.S. and Ph.D. degrees are offered in systems engineering. The graduate program builds on sound engineering undergraduate education, experience, and maintains engineering specialization diversity in its graduates.

Systems engineering research is supported by interdisciplinary research and collaboration. Research areas include: model based engineering, systems architecting, modeling and simulation, complex adaptive systems, computational intelligence, human system integration and infrastructure systems. The systems engineering graduate program, with over 500 graduates since 2000, contributes to the research challenges of systems engineering imposed by today's complex, adaptive, distributed, cooperative and dynamically changing engineering systems. As one of the leading systems engineering program in the nation, Missouri S&T is the only university in the world to have four INCOSE (International Council on Systems Engineering) Stevens Doctoral Award recipients.

Requirements for Completion

Students following their approved program of study will be assured of graduation upon maintenance of good academic standing. A minimum of 30 hours of course work (non-thesis) or 36 hours of course work (thesis) from the areas listed below must be completed with a cumulative grade point average of 3.00 (on a 4.00 scale) and a "C" grade or better in each course. Accumulation of more than 10 hours of “C” or “F” results in dismissal from the program. A maximum of nine hours of course work for M.S. degrees may be transferred from universities outside the University of Missouri System. Such credits for transfer must have been registered as graduate courses when they were taken. All courses applied to the degree require prior written advisor approval recorded on the study plan in the student's file. It is the responsibility of each student to apply for graduation with the Missouri S&T registrar's office during his or her last semester. Assistance on this final step can be provided by the engineering management and systems engineering department.

Departmental Laboratories

The department has several "hands on" laboratories that have both a research and teaching focus. Each of our labs is directed by faculty members that work closely with students to enhance their learning experience. The description below gives a brief introduction that will help you understand the purpose of each lab.

Modeling and Integration Lab (M&IL)

The Modeling and Integration Lab in the engineering management and systems engineering department provides research space for faculty and student teams in human performance modeling, safety analysis, operations modeling and simulation, alternative energy vehicles.

The 5,000 square foot, high bay facility enables leading edge research in these important areas.

Smart Engineering Systems Lab (SESL)

The department established the Smart Engineering Systems Lab (SESL) to develop approaches in building complex systems that can adapt in the environments in which they operate. The term "smart" in the context indicates physical systems that can interact with their environment and adapt to changes both in space and time by their ability to manipulate the environment through self-awareness and perceived models of the world based on both quantitative and qualitative information. The emerging fields of cyber physical systems, deep neural networks, fuzzy logic, evolutionary programming, and complex adaptive systems provide essential tools for designing such systems.

The focus of the SESL is in developing smart engineering architectures that integrate and/or enhance the current and future technologies necessary for developing smart engineering systems while illustrating the real life applications of these architectures. The smart engineering systems design and operations cut across a diversity of disciplines, namely manufacturing, electrical, computer, and mechanical, biomedical, civil and other related fields such as applied mathematics, cognitive sciences, biology and medicine. Current research is on developing new models and tools for building complex systems architectures that are intelligent, modular, and adaptive.

Design Engineering Center (DEC)

The center is one of the outreach arms of the engineering management and systems engineering department. The focus is on research and service activities in support of the educational goals of the department through externally funded projects. Current areas of research include total quality management, concurrent engineering, Taguchi Methods®, quality engineering, the product development process, and design optimization.

Laboratory for Investment and Financial Engineering

The goal of the Laboratory for Investment and Financial Engineering is to develop techniques and computational tools for increasing investment and capital return while managing and reducing financial risk. This involves research into stocks and financial derivatives (options, futures, forwards, and swaps), financial risk and uncertainty, financial forecasting, market efficiency and behavioral finance, fundamental and technical analysis, equity valuation, real options, and engineering economics. In cooperation with the Smart Engineering Systems Lab, research in the lab may also involve the use of smart and intelligent systems, such as neural networks, fuzzy logic, genetic and evolutionary algorithms, expert systems, intelligent agents, artificial life, chaos and fractals, and dynamic and complex systems. Data mining, principal component analysis and various other forms of applied statistics are also used. Members of the lab have access to financial data and various financial modeling software packages.

Additional Information

For additional information you can call our main department phone at 573-341-4572 or you can visit our web page at http://emse.mst.edu/.

Master of Science
Admission Standards

  • B.S. in engineering or a physical science
  • Undergraduate courses:  Calculus Series (I, II, III), Differential Equations, Statistics, Physics (I, II) or Chemistry, Engineering Economy
  • GPA: Regular status: 3.0 cumulative
  • Graduate Record Exam (GRE): All students must submit current GRE scores. Students successfully completing one of the department's graduate certificates with a grade of B or better in all the certificate courses can be admitted without the GRE.
  • Regular status: V+Q≥ 1100, A≥ 4.0 (former scoring)
    or V≥ 155, Q≥ 148, A≥ 4.0
  • Condition: Student must earn B or better in each of first four graduate (5000 or 6000 level) classes after conditional admission.
  • TOEFL: All international applicants must submit a current TOEFL score, regardless of prior academic experience or place of study.
  • Regular status: 580/237/92
  • Statement of Purpose: All applicants must submit a statement of purpose.
  • Financial Support: Students in conditional status are not eligible for financial support from the department.
  • Three reference letters

The M.S. degree program is offered on the Rolla campus and several locations including the Missouri S&T Global - St. Louis, and by distance education throughout the United States and selected international locations. Distance course lectures are archived upon completion of the lecture and all lectures are available to students through streaming video during the semester for review. These courses can be reached from anywhere at any time. It is feasible to obtain a Missouri S&T non-thesis M.S. degree regardless of your location.

The M.S. non-thesis program requires completion of at least 10 three-credit hour courses approved by the academic advisor. The M.S. with thesis option requires 36 credit hours including the thesis. All students are required to take the following:

CORE Courses

System Engineering and Analysis
Information Based Design
Systems Life Cycle Costing
Systems Architecting
Complex Engineering Systems Project Management
Systems Engineering Capstone

Specialization Courses

Specialization courses provides students with the ability to address his/her technology needs in the context of the overall Systems Engineering program. These graduate courses can be selected from engineering or the physical science department as long as they are approved by the program director.

One of the graduate certificates may be substituted for a specialization track with the permission of the program director.

Choose 4 courses in an area or combination of areas. (Please refer to the engineering management and systems engineering department for course information in each area.)

Civil and Environmental

Contemporary Structural Engineeing
Geoenvironmental Engineering
Geotechnical Earthquake Engineering
Infrastructure Renewal

Computer Science

Big Data Management & Analytics
Big Data Management & Security
Computational Intelligence
Information Assurance & Security Officer Essentials
Multimedia & Information Systems
Software Design & Development
Systems and Software Architecture
Wireless Networks and Mobile Systems

Electrical Engineering

Computation Intelligence
Electric Machines and Drives
Electric Power Systems Engineering
Information Assurance & Security Officer Essentials
Network Centric Systems

Engineering Management

Engineering Management
Financial Engineering
Human Systems Integration
Leadership in Engineering Organizations
Lean Six Sigma
Project Management

Manufacturing Engineering

CAD/CAM & Rapid Product Realization
Manufacturing Systems

Mechanical and Aerospace Engineering

Composite Materials and Structures
Control Systems
Energy Conversion & Transport
Engineering Mechanics
Manufacturing Automation

Doctor of Philosophy
Admission Standards

  • B.S. in engineering, or a physical science
  • Undergraduate courses:  Calculus Series (I, II, III), Differential Equations, Statistics, Physics (I, II) or Chemistry, Engineering Economy
  • GPA: M.S. GPA = 3.5
  • Graduate Record Exam (GRE): All students must submit current GRE scores. V+Q≥ 1100, A≥ 4.0 (former scoring) or V≥ 155, Q≥ 148, A≥ 4.0
  • TOEFL: All international applicants must submit a current TOEFL score, regardless of prior academic experience or place of study.
  • Regular status: 580/237/92
  • Statement of Purpose: All applicants must submit a statement of purpose.
  • Three reference letters

A candidate for the Ph.D. in systems engineering must complete the equivalent of at least three years of full time work beyond the bachelor's degree. The content of all Ph.D. programs are individually structured by the student in consultation with and approved by the student's advisory committee. All requirements for the degree must normally be completed within an eight year period. At appropriate points in their program, Ph.D. students must pass both a Qualifying Exam and Comprehensive Exam. Off-campus students are expected to complete all requirements listed in the Missouri S&T Graduate Catalog under the section entitled Doctor of Philosophy Degree and follow all procedures listed under the Procedures for Ph.D. Candidates.  

The total credit requirements for graduation are a minimum of 60 credit hours after the successful completion of M.S. degree in systems engineering. Actual courses taken will be determined by the candidate's committee and his/her plan of study. The student is expected to complete all requirements.

Residency Requirements

All students are expected to follow the Missouri S&T graduate student residency requirements. Off campus students can meet the 2 year residency requirement with the following requirements: the qualifying exam must be taken on campus within the first 5 semesters of enrollment; the student will have at minimum two video conferences per month with his/her research advisor; the Ph.D. committee will include one person from the student’s professional work location, the appointment committee member must have a Ph.D. and be familiar with the chosen research; the student is expected to meet with the Ph.D. committee on a regular basis with at least two meetings per semester; the student is expected to be on campus a minimum of 16 days per year, visits may be spread over 4 campus visits; the Ph.D. comprehensive exam must be taken on campus; the student has the option of conducting research that is beneficial to the student’s professional work; and the defense of dissertation must take place on campus.

Major Requirements

May be taken during M.S. degree

Core Curriculum24
System Engineering and Analysis
Information Based Design
Systems Life Cycle Costing
Systems Architecting
Complex Engineering Systems Project Management
Systems Engineering Capstone
Smart Engineering System Design
Modeling Complex Systems
Modeling Complex Systems
Research30
SYS ENG 6099Research1-15
Electives36
Systems Eng Process Tools, Optimization & Statics - 12 credit hours
Research Specialization Areas - 24 credit hours

Requirements for Thesis

Students will conduct original research demonstrated by journal or referred proceedings, publication under the supervision of a doctoral advisor, and communicate their findings, write a dissertation on research conducted, and provide satisfactory defense of their dissertation in a final oral examination. Students will be required to sign up for one hour of SYS ENG 6099 under their research advisor and attend systems engineering seminars every fall and spring semester during their study. These courses may be included as fulfilling research credit requirements. Students are required to publish their work in approved journals and referred proceedings. A minimum of three articles is expected.

Qualifying Exam

The objective of the systems engineering Ph.D. qualifying exam is to test the knowledge and understanding of the graduate student on systems engineering fundamentals and assess the student's level of knowledge in engineering statistics and optimization. The qualifying exam is a two day exam consisting of a written and oral part. For more information, contact the department graduate staff.

It is expected that the graduate student has a clear understanding of the research issues in the student's area of interest, its implications in industrial applications primarily in the industrial domain the student is working, possible impact of successful research contributions to systems engineering research and literature and should be able to identify up to five journals in this area. Prior to the oral exam, copies of the written exams prepared by the systems engineering faculty will be provided to all faculty for each student. The oral exam is restricted to the areas of research specialization selected by each student and will continue until there is a consensus not to ask further questions by the faculty.

Comprehensive Exam

The student's advisory committee will administer the comprehensive examination after the student has completed seventy-five percent of the coursework for the Ph.D. program and one published refereed conference proceeding or journal paper. The examination is written and oral. Upon successful completion of the written examination, the student will be orally examined by the advisory committee.

Dissertation

The dissertation, embodying the results of an original investigation, must be written upon a subject mutually agreed upon between the student and the advisor.

Research Areas

For the list of Areas of Research Specialization can be found at http://emse.mst.edu/media/academic/emse/documents/graduatedocs/Sys%20Research%20Area%20Page-1.pdf.

Graduate Certificate Programs

This program is designed to appeal to working professionals. Certificate courses taken for graduate credit can be counted in the M.S. degree once accepted into the M.S. degree. If the four-course sequence is completed with a grade of "B" or better in each of the courses taken, they can apply to be admitted to the M.S. systems engineering. The certificate program may be followed by six to eight additional 3 credit hour courses to complete the M.S. degree. The certificate program is open to all persons holding a B.S., M.S., or Ph.D. degree in engineering or a physical science and who have a minimum of 12-months of professional employment experience or are currently accepted into a graduate degree program at Missouri S&T.

Admission Standards

  • B.S. in engineering or a physical science
  • Undergraduate courses:  Calculus Series (I, II, III), Differential Equations, Statistics, Physics or Chemistry, Engineering Economy
  • GPA: Regular status: 2.75 cumulative
  • TOEFL: All international applicants must submit a current TOEFL score, regardless of prior academic experience or place of study
  • Regular status: 580/237/92

Once admitted to the program, the student must take the four designated courses as given below. In order to receive a graduate certificate, the student must have an average cumulative grade point of 3.0 or better in the certificate courses.

Computational Intelligence Certificate

Recent advances in information technology and the increased level of interconnectivity that society has achieved through Internet and broadband communication technology created systems that are very much different. The world is facing an increasing level of systems integration leading towards systems of systems (SoS) that adapt to changing environmental conditions. The number of connections between components, the diversity of the components and the way the components are organized can lead to different emergent system behavior. Computational Intelligence tools are an integral part of these systems in enabling adaptive capability in their design and operation.

This graduate certificate program provides practicing engineers the opportunity to develop the necessary skills in the use and development of computational intelligence algorithms based on evolutionary computation, neural networks, fuzzy logic, and complex systems theory. Engineers can also learn how to integrate common sense reasoning with computational intelligence elective courses such as data mining and knowledge discovery.

Core Courses
Computational Intelligence
Select one of the following:
Introduction to Neural Networks and Applications
Introduction To Artificial Intelligence
Evolutionary Computing
Select two of the following not taken as a core course:
Introduction to Neural Networks and Applications
Neural Networks Control and Applications
Introduction To Artificial Intelligence
Evolutionary Computing
Advanced Topics In Artificial Intelligence
Advanced Evolutionary Computing
Advanced Neural Networks
Clustering Algorithms
Adaptive Dynamic Programming
Advanced Topics in Data Mining
Markov Decision Processes

Model Based Systems Engineering Certificate

Recent advances in technology demands and the increased level of interconnectivity achieved through Internet and broadband communication technology is leading to systems that are increasingly complex. To manage this complexity, computational modeling and data resources have become nearly ubiquitous in systems engineering, driving the profession from a document-centric paradigm to a model-centric one. Model based systems engineering provides the means to construct models that capture system structure, behavior, and requirements and maintain consistency of these models automatically between collaborating engineers. These models can then be used in tandem with engineering and mathematics tools to quickly gain insight into the overall system performance over the entire lifecycle before a system component is ever made.

This graduate certificate program provides practicing engineers the opportunity to develop the necessary skills in the use of current modeling techniques to develop and simulate complex, multi-disciplinary engineering systems. In addition, engineers will learn methods to automate data acquisition for system development, establish rules for usability of model resources, and acquire necessary skills for simulating the designed systems. 

Smart Engineering System Design
Distributed Systems Modeling
Model Based Systems Engineering
Engineering Design Optimization

Network Centric Systems Graduate Certificate

The area of network centric systems has evolved from recent advances in information technology and the increased level of interconnectivity that society has achieved through the Internet and Broadband communication technology. The area of network centric systems has grown due to advances in information technology and increases in connectivity due to the convergence of computing and communications.

Network centric systems are frequently "systems of systems" with complex interfaces and interactions. The graduate certificate in network centric systems is a joint effort between computer engineering and systems engineering to provide practicing engineers with the necessary skills to develop and design the operation of network centric systems. These four courses count towards a M.S. degree in systems engineering or computer engineering and they address the intersection between network engineering, systems engineering, and architecting. The requirements are the successful completion of a core course and three elective courses from the list below. A grade of "B" or better is required in each course before the student is eligible for the master of science program.

Core Course:
Modeling Complex Systems
Introduction to Computer Communication Networks
Elective Courses (Select two courses):
Introduction to Network Security
Wireless Networks
Fault-Tolerant Digital Systems
Resilient Networks
Wireless Ad hoc and Sensor Networks
High Speed Networks
Network Performance Analysis
Computer Security
Mobile And Sensor Data Management

Systems Engineering Graduate Certificate

The graduate certificate in systems engineering is designed to provide graduate engineers with the advanced knowledge and skills necessary for the conception and implementation of complex systems. The emphasis is on the processes by which complex systems are conceived, planned, designed, built, tested, and certified. The systems engineering experience can be applied to defense, space, aircraft, communications, navigation, sensor, computer software, computer hardware, transportation, and other aerospace and commercial activities. 

System Engineering and Analysis
Information Based Design
Systems Life Cycle Costing
Systems Architecting

Upon successful completion of the four courses as described above, students will be awarded certification. The student must complete the four courses with a grade of "B" or better in each course. Students may apply to the M.S. program with the completion of the certificate.

SYS ENG 5000 Special Problems (IND 1.0-6.0)

Problems or readings on specific subjects or projects in the department. Consent of instructor required.

SYS ENG 5001 Special Topics (LEC 0.0 and LAB 0.0)

This course is designed to give the department an opportunity to test a new course. Variable title.

SYS ENG 5040 Oral Examination (IND 0.0)

After completion of all other program requirements, oral examination for on-campus MS/PhD students may be processed during intersession. Off-campus MS students must be enrolled in oral examination and must have paid an oral examination fee at the time of the defense/comphrehensive exam (oral/written). All other students must enroll for credit commensurate with uses made of facilities and/or faculties. In no case shall this be for less than three (3) semester hours for resident students.

SYS ENG 5099 Research (IND 1.0-15)

Investigations of an advanced nature leading to the preparation of a thesis or dissertation. Consent of instructor required. Prerequisite: Graduate standing.

SYS ENG 5101 System Engineering and Analysis (LEC 3.0)

The concepts of Systems Engineering are introduced through a project. Students work in virtual teams. The topics covered are architecture development, basic system architectural design techniques, functional decomposition, design and technical review objectives, and initial specifications. Prerequisite: Graduate Standing.

SYS ENG 5211 Computational Intelligence (LEC 3.0)

Introduction to Computational Intelligence (CI), Biological and Artificial Neuron, Neural Networks, Evolutionary Computing, Swarm Intelligence, Artificial Immune Systems, Fuzzy Systems, and Hybrid Systems. CI application case studies covered include digital systems, control, power systems, forecasting, and time-series predictions. Prerequisite: Graduate Standing. (Co-listed with Elec Eng 5310 and Comp Eng 5310).

SYS ENG 5212 Introduction to Neural Networks and Applications (LEC 3.0)

The course provides an introduction to basic neural network architectures and their applications. Students learn to construct neural networks and train them to solve engineering problems, specifically pattern recognition and function approximation. Mathematical analysis of network architectures, training algorithms and practical applications of neural nets. Prerequisites: Graduate Standing. (Co-listed with Elec Eng 5370).

SYS ENG 5323 Wireless Networks (LEC 2.0 and LAB 1.0)

Introduction to wireless communications and networking. Topics include transmission fundamentals, wireless channel, coding techniques and error control, satellite and cellular networks, cordless systems, mobile IP and management, multiple access techniques and wireless protocols, wireless LAN, IEEE 802.11, and adhoc and sensor networks. Prerequisites: Hardware competency, Elec Eng 3420 or Comp Eng 3150 and graduate standing. (Co-listed with Comp Eng 5430 and Elec Eng 5430).

SYS ENG 6000 Special Problems (IND 1.0-6.0)

Problems or readings on specific subjects or projects in the department. Consent of instructor required.

SYS ENG 6001 Special Topics (LEC 1.0-6.0)

This course is designed to give the department an opportunity to test a new course. Variable title.

SYS ENG 6010 Seminar (RSD 0.0-6.0)

Discussion of current topics.

SYS ENG 6040 Oral Examination (IND 0.0)

After completion of all other program requirements, oral examination for on-campus MS/PhD students may be processed during intersession. Off-campus MS students must be enrolled in oral examination and must have paid an oral examination fee at the time of the defense/comphrehensive exam (oral/written). All other students must enroll for credit commensurate with uses made of facilities and/or faculties. In no case shall this be for less than three (3) semester hours for resident students.

SYS ENG 6050 Continuous Registration (IND 1.0)

Doctoral candidates who have completed all requirements for the degree except the dissertation, and are away from campus must continue to enroll for at least one credit hour each registration period until the degree is completed. Failure to do so may invalidate the candidacy. Billing will be automatic as will registration upon payment.

SYS ENG 6099 Research (IND 1.0-15)

Investigations of an advanced nature leading to the preparation of a thesis or dissertation. Consent of instructor required. Prerequisite: Graduate standing.

SYS ENG 6102 Information Based Design (LEC 3.0)

This course is an introduction to the use of common data analytical methods and analysis for the purpose of decision making during the design phase of engineering system development. Through the introduction to such analytical methodologies, the systems engineering tool belt is made more effective as it is the foundation to decision analysis. Prerequisites: Graduate Standing.

SYS ENG 6103 Systems Life Cycle Costing (LEC 3.0)

Methods of economic evaluation for engineering projects involving complex systems. Economic impacts on choosing system alternatives, life cycle costing, economic decisions involving risk and uncertainty, and engineering cost estimation for projects in government, defense, and commercial industries. Prerequisites: Graduate Standing.

SYS ENG 6104 Systems Architecting (LEC 3.0)

Tools and concepts of architecting complex engineering systems. Ambiguity in Systems Architecting and Fuzzy Systems; Search as an Architecting Process; Architecting Heuristics; Systems Scoping and Attribute Selection; Assessing Architectures; Systems Aggregation, Partitioning; Systems Behavior Generation; System Science and Thinking, Cyber Physical Systems. Prerequisites: Graduate Standing.

SYS ENG 6105 Complex Engineering Systems Project Management (LEC 3.0)

The course topics include issues specific to distributed project management, team development, resource management, constraint planning, development of Integrated Master Schedule and Integrated Master Plan, monitoring technical performance, schedule, cost, and risk. Prerequisites: Graduate Standing.

SYS ENG 6110 Function-Based Risk Analysis (LEC 3.0)

Risk analysis of products and systems will be explored using product functionality as the starting point. Traditional probabilistic risk assessment techniques will be covered along with recent approaches that use historical data to produce automatic risk assessments. Prerequisite: Graduate standing.

SYS ENG 6167 Software Intensive Systems Architecting (LEC 3.0)

Basic tools and concepts of architecting complex software intensive systems are introduced. The following topics are covered under four main sections; namely Architecting Process, Architecting Heuristics, Architecting Patterns and Frameworks, and Architecture Assessment. Prerequisite: Graduate Standing.

SYS ENG 6196 Systems Engineering Capstone (LEC 3.0)

The topics covered are Systems Engineering Management Plan (SEMP), Systems Engineering processes, process re-engineering, standards, and systems engineering case studies. Students will apply the skills and theory that they mastered in previous five core courses to the analysis of assigned cases. Prerequisites: Sys Eng 6105.

SYS ENG 6213 Advanced Neural Networks (LEC 3.0)

Advanced artificial neural network architectures, namely; Radial-Basis Function Networks, Support Vector Machines, Committee Machines, Principal Components Analysis, Information-Theoretic Models, Stochastic Machines, Neurodynamic Programming, Temporal Processing are the topics covered. Prerequisite: Sys Eng 5212 or equivalent neural network course.

SYS ENG 6214 Clustering Algorithms (LEC 3.0)

An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science and statistics. Clustering in sequential data, massive data and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with student's degree program. (Co-listed with Comp Eng 6330, Elec Eng 6340, Comp Sci 6405 and Stat 6239).

SYS ENG 6215 Adaptive Dynamic Programming (LEC 3.0)

Review of Neurocontrol and Optimization, Introduction to Approximate Dynamic Programming (ADP), Reinforcement Learning (RL), Combined Concepts of ADP and RL - Heuristic Dynamic Programming (HDP), Dual Heuristic Programming (DHP), Global Dual Heuristic Programming (GDHP), and Case Studies. Prerequisites: Elec Eng 5370 or Comp Eng 5310. (Co-listed with Comp Eng 6320, Elec Eng 6360, Mech Eng 6458 and Aero Eng 6458).

SYS ENG 6216 Advanced Topics in Data Mining (LEC 3.0)

Advanced topics of current interest in the field of data mining. This course involves reading seminal and state-of-the-art papers as well as conducting topical research projects including design, implementation, experimentation, analysis, and written and oral reporting components. Prerequisite: A "C" or better grade in Comp Sci 5001 Introduction to Data Mining . (Co-listed with Comp Sci 6402 and Comp Eng 6302).

SYS ENG 6239 Smart Engineering System Design (LEC 3.0)

Covers the tools, techniques and methods used in developing Flexible Intelligent Learning Architectures for system of systems (SoS) and cyber physical systems (CPS) through evolutionary approach. Meta-architecture generation algorithms, SoS and CPS architecture evaluation methods, executable architectures, many meta-architecture objectives trade. Prerequisites: Graduate Standing.

SYS ENG 6321 Modeling Complex Systems (LEC 3.0)

Engineering Systems of today are non-linear, distributed, global, and adaptive to their environment in both space and time, thereby creating emergent behaviors. This course covers the current modeling tools and techniques used in modeling and architecting these complex systems. Prerequisites: Graduate Standing. (Co-listed with COMP ENG 6410).

SYS ENG 6322 Resilient Networks (LEC 3.0)

This course presents reliability and fault tolerance for network-centric systems, including models, metrics, and analysis techniques. This course also concentrates on security, including technical tools and methods for audit and assessment as well as management and policy issues. Prerequisites: Sys Eng 6410, Comp Eng 6410, or Comp Eng 5420. (Co-listed with COMP ENG 6510).

SYS ENG 6324 Wireless Ad hoc and Sensor Networks (LEC 3.0)

Introduction to ad hoc and sensor networks, IEEE standards, heterogeneity, quality of service, wireless channel issues, energy awareness, power and topology control, routing, scheduling, rate adaptation, self-organization, admission and flow control, energy harvesting, security and trust levels, hardware and applications. Prerequisite: Comp Eng 5430 or Comp Eng 5420 or equivalent. (Co-listed with Comp Eng 6420 and Elec Eng 6430).

SYS ENG 6541 Distributed Systems Modeling (LEC 3.0)

This course will discuss issues related to distributed systems architecting, modeling, analysis and representation, with specific focus on discrete-part manufacturing domain. Distributed modeling techniques and other model decomposition methods using simulation modeling and scalability issues will also be addressed.

SYS ENG 6542 Model Based Systems Engineering (LEC 3.0)

Provides the student with understanding of the use of models to represent systems and validate system architectures. The student will gain proficiency in using a systems modeling language and shifting systems engineering from a document centric to a model centric paradigm. Prerequisites: Graduate Standing. (Co-listed with COMP SCI 6102).

SYS ENG 6612 Investment (LEC 3.0)

An introduction to the theory and practice of investment, including financial markets and instruments, security trading, mutual funds, investment banking, interest rates, risk premiums, the capital asset pricing model, arbitrage pricing theory, market efficiency, bonds and the fixed income market, equity valuation, fundamental and technical analysis. Prerequisite: Eng Mgt 1210 or 5210. (Co-listed with Eng Mgt 6212).

SYS ENG 6613 Financial Engineering (LEC 3.0)

An introduction to financial engineering, with an emphasis on financial derivatives, including the future markets, the pricing of forwards and futures, forward rate agreements, interest and exchange rate futures, swaps, the options markets, option strategies, the binomial and Black-Scholes models for option valuation, the option Greeks, and volatility smiles. Prerequisites: Eng Mgt 1210 or 5210. (Co-listed with Eng Mgt 6213).

SYS ENG 6614 Financial Engineering II (LEC 3.0)

This course introduces advanced topics in financial engineering, which includes introduction to Wienver processes, martingales and Ito's lemma; basic numerical methods for options pricing, exotic options; interest rate models; stochastic volatility models and jump-diffusion models; and value-at-risk. Prerequisite: Eng Mgt 6213/Sys Eng 6613. (Co-listed with Eng Mgt 6214).

SYS ENG 6615 Financial Risk Management (LEC 3.0)

Techniques and methods for managing financial risk, including portfolio theory, Monte Carlo methods, ARIMA, time series forecasting, Value-at-Risk, stress testing, extreme value theory, GARCH and volatility estimation, random variables and probability distributions, real options, decision trees, utility theory, statistical decision techniques, and game theory. Prerequisite: Eng Mgt 1210 or 5210. (Co-listed with Eng Mgt 6215).

Graduate Faculty members are listed under the specific discipline most closely allied with their graduate faculty status which may not necessarily reflect the department in which current appointment is held.

Venkat Allada, Professor
PHD University of Cincinnati
Sustainable produce development, product platform design, mass customization, product innovation, lean manufacturing, intelligent manufacturing systems, process planning supply chain management, systems engineering process and design.

K Chandrashekhara, Curators Professor
PHD Virginia Polytechnic Institute
Structures and Composite Materials

Steven M. Corns, Associate Professor
PHD Iowa State University
Systems Engineering Research Focus Area: computational intelligence, modeling and simulation, risk modeling and assessment.

Elizabeth Anne Fargher Cudney, Associate Professor
PHD Missouri S&T
Quality, Six Sigma, Robust Engineering, and Lean Enterprise.

Cihan H Dagli, Professor
PHD University of Birmingham, UK
Systems Architecting and Engineering, Complex Adaptive Systems, Neural Networks, Fuzzy Logic, Evolutionary Programming, Nesting Problems. INCOSE Fellow, IIE Fellow

David Enke, Professor
PHD University of Missouri-Rolla
Investments, Derivatives, Options and Futures, Financial Forecasting, Trading Strategies, Hedge Funds, Endowment Investing, Financial Risk Management, Engineering Economy, Computational Finance, Computational Intelligence, Neural Networks.

Abhijit Gosavi, Associate Professor
PHD University of South Florida
Lean manufacturing, supply chain management, revenue management, simulation-optimization.

Katie Grantham, Associate Professor
PHD University of Missouri-Rolla

Ivan Guardiola, Associate Professor
PHD Texas Tech University
Simulation and modeling, risk modeling and assessment, systems engineering processes and design, network centric systems, wireless communications networks, stochastic and probability modeling, operations research, birth-death process modeling, speaker recognition and identification systems (pattern recognition), and rare event probability modeling.

Sheryl Hodges, Assistant Teaching Professor
DEng Louisiana Tech Univeristy

Dincer Konur, Assistant Professor
PHD University of Florida
Operations research, games theory, mathematical modeling, linear and non-linear optimization, supply chain management, logistics, transportation.

Suzanna K. Long, Professor
PHD University of Missouri-Rolla
Strategic management, change management, business logistics and marketing.

Robert Marley, Professor
PhD Wichita State University
Human System Integration, Ergonomics

Susan L Murray, Professor1
PHD Texas A&M University
Industrial engineering, human systems integration, human factors, and safety.

Louis Pape II, Assistant Adjunct Professor
PHD Missouri University of Science and Technology
The Boeing Company, Associate Technical Fellow

Ruwen Qin, Associate Professor
PHD Pennsylvania State University
Real options, financial engineering, and manufacturing and service operations.

Stephen A Raper, Associate Professor
PHD University of Missouri-Rolla
Packaging engineering, operations, productivity, total quality management, packaging systems design, environmental aspects of packaging and statistical process control.

Jagannathan Sarangapani, Professor
PHD University of Texas-Arlington
Adaptive Control, Wireless Networks.

Joan Barker Schuman, Assistant Teaching Professor
PHD University of Southern Mississippi
Project Management and Engineering Economics.

David G Spurlock, Lecturer
PHD University of Illinois Urbana
General Management

Zeyi Sun, Assistant Professor
PHD University of Illinois at Chicago
Energy efficiency management of manufacturing systems, electricity demand response of manufacturing systems, system modeling of cellulosic biofuel manufacturing, energy modeling and control in additive manufacturing, intelligent maintenance of manufacturing systems.

Donald C Wunsch II, Professor1
PHD University of Washington
Adaptive critic designs, neural networks, fuzzy systems, surety, nonlinear adaptive control, intelligent agents, applications.

Gonca Yildirim, Adjunt Professor
PHD University of Florida
Cankaya University, Ankara, Turkey

Superscripts 1, 2, 3, 4, 5, and 6 in the faculty listing refer to the following common footnotes:
1 Registered Professional Engineer
2 Registered Geologist
3 Certified Health Physicist
4 Registered Architect
5 Board Certified, American Academy of Environmental Engineers
6 LEED AP Certified