Graduate Courses
Below on this page are SIE graduate courses with descriptions, prerequisites and syllabi.
See the SIE Graduate Handbook (PDF) for program details.
Additional information, including fees and grading bases, is available through the UA Catalog under Course Descriptions.
Offered
SIE 500A: Introduction to SIE Methods: Probability and Statistics
Units: 1
Prerequisite(s): Calculus and SIE 305
Usually offered: Fall, Spring
SIE 500A Syllabus (PDF)
Axioms of probability, discrete and continuous distributions, sampling distributions. Applications of statistical estimation, hypothesis testing, confidence intervals.
SIE 500B: Introduction to SIE Methods: Stochastic Processes
Units: 1
Usually offered: Fall, Spring
SIE 500B Syllabus (PDF)
Introduction to probabilistic models commonly used in systems and industrial engineering and related disciplines. Markov chains, Poisson processes, queuing models.
SIE 500C: Introduction to SIE Methods: Linear Programming
Units: 1
Prerequisite(s): Matrix methods for systems of linear equations
Usually offered: Fall, Spring
SIE 500C Fall Syllabus (PDF)
Linear programming models, solution techniques, and duality.
SIE 501: Advanced Requirements Engineering Methods
Units: 3
Usually Offered: Spring, 7-Week-1 Session
This course presents advanced concepts in requirements engineering. The course will combine a practical focus on improving the quality of problem formulation and a research focus on advancing the state of the art in problem formulation. Topics include different types of problem spaces (outcomes vs functions), formal distinction between problem and solution, formal modeling of needs and requirements, formal syntax and ontologies for textual formulation of needs and requirements, elicitation and derivation as a byproduct of mission engineering, decomposition as a byproduct of systems architecture, mixed-formulation approaches, traceability, techniques to identify necessary vs constraining needs and requirements, and techniques to identify gaps in needs and requirements.
SIE 506: Quality Engineering
Units: 3
Usually offered: Fall
SIE 506 Fall Syllabus (PDF)
Quality, improvement and control methods with applications in design, development, manufacturing, delivery and service. Topics include modern quality management philosophies, engineering/statistical methods (including process control, control charts, process capability studies, loss functions, experimentation for improvement) and TQM topics (customer driven quality, teaming, Malcolm Baldridge and ISO 9000). Graduate-level requirements include additional readings and assignments/projects.
SIE 508: Reliability Engineering
Units: 3
Prerequisite(s): SIE 430/530 or its equivalent would be useful but is not required
Usually offered: Fall
SIE 508 Fall Syllabus (PDF)
This is a three-credit course offered for well-qualified seniors, graduate students, and engineering professionals and practitioners. This is an introductory text and will be supplemented with material on many everyday reliability engineering problems e.g. root cause analysis. The course will make use of Minitab™ software. The scope of this course includes: (1) failure distributions, (2) failure rate models and reliability concepts, (3) reliability systems and state-space models, (4) accelerated testing, (5) repair process and availability, (6) Bayesian reliability estimates, (7) case studies. May be convened with SIE 408.
SIE 514: Law for Engineers/Scientists
Units: 3
Usually offered: Spring
SIE 514 Syllabus (PDF)
Topics covered in this course include patents, trade secrets, trademarks, copyrights, product liability contracts, business entities, employment relations and other legal matters important to engineers and scientists. Graduate-level requirements include an in-depth research paper on a current topic.
SIE 515: Technical Sales and Marketing
Units: 3
Usually offered: Fall, Spring
SIE 515 Fall Syllabus (PDF) | SIE 515 Spring Syllabus (PDF)
Principles of the engineering sales process in technology-oriented enterprises; selling strategy, needs analysis, proposals, technical communications, electronic media, time management and ethics; practical application of concepts through study of real-world examples. Graduate-level requirements include a term paper on a course topic selected from a short list of topics, other graded components of the course and creation of a PowerPoint presentation to the class.
SIE 520: Stochastic Modeling I
Units: 3
Prerequisite(s): SIE 321
Usually offered: Spring
SIE 520 Syllabus (PDF)
Modeling of stochastic processes from an applied viewpoint. Markov chains in discrete and continuous time, renewal theory, applications to engineering processes.
SIE 522: Engineering Decision Making Under Uncertainty
Units: 3
Usually offered: Fall
SIE 522 Fall Syllabus (PDF) | SIE 522 Fall - Online Syllabus (PDF)
Foundations of decision analysis contextualized for engineering work. Students will learn to frame and model engineering problems as decisions that traverse physics by incorporating the firm’s objectives and the personal preferences of the engineer. In addition, the course will present formal and informal limitations of decision methods traditionally used in engineering, such as rank matrices, and will provide students with alternative theories and methods that foster better decisions. Finally, the course will present the notion of risk assessment and management as inherent to engineering decision-making, instead of as an independent engineering process.
SIE 523: V&V/T&E Planning and Execution
Units: 3
Usually Offered: Spring, 7-Week-2 Session
This course delves into systematic planning and execution of Verification and Validation or Test and Evaluation (V&V/T&E) strategies. The course explores foundational aspects that are critical in understanding the value and need of V&V/T&E activities and to effectively reason about V&V/T&E evidence, quantitative methods to inform decisions about what to V&V/T&E and when, procedural practices necessary to implement and execute V&V/T&E, and quantitative and qualitative guidance to integrate V&V/T&E considerations into system architecture.
SIE 530: Engineering Statistics
Units: 3
Usually offered: Fall
SIE 530 Fall Syllabus (PDF)
Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments.
SIE 531: Simulation Modeling and Analysis
Units: 3
Usually offered: Fall, Spring, Summer
SIE 531 Fall Syllabus (PDF) | SIE 531 Spring Syllabus (PDF) | SIE 531 Summer Syllabus (PDF)
Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Graduate-level requirements include a library research report.
SIE 532: Sports Analytics
Units: 3
Prerequisite(s): SIE 305
Usually offered: Summer
SIE 532 Summer Pre-Session Syllabus (PDF)
This course provides fundamental analytical skills necessary to analyze data and make decisions using sports examples. These skills include critical thinking, statistical analysis, computer programming, and data visualization which are generally applicable to other areas of engineering and business. May be convened with SIE 432.
SIE 533: Fundamentals of Data Science for Engineers
Units: 3
Prerequisite(s): SIE 530, equivalent courses such as SIE 500A taken in parallel or consent of instructor.
Usually offered: Spring
SIE 533 Syllabus (PDF)
This course will provide senior undergraduate and graduate students from a diverse engineering disciplines with fundamental concepts, principles and tools to extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics and machine learning, along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamental topics of data science study, including: (1) principles of data processing and representation, (2) theoretical basis and advances in data science, (3) modeling and algorithms, and (4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency, and help students to understand and identify the circumstances under which the algorithms are most appropriate.
SIE 540: Survey of Optimization Methods
Units: 3
Usually offered: Spring
SIE 540 Syllabus (PDF)
Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. Grading: Regular grades are awarded for this course: A B C D E. May be convened with SIE 440.
SIE 545: Fundamentals of Optimization
Units: 3
Prerequisite(s): SIE 340
Usually offered: Fall
SIE 545 Fall Syllabus (PDF)
Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.
SIE 546: Algorithms, Graphs, and Networks
Units: 3
Prerequisite(s): SIE 340. Credit allowed for only one of these courses: SIE 546, MIS 546.
Usually offered: Every other Fall
SIE 546 Syllabus (PDF)
Model formulation and solution of problems on graphs and networks. Topics include heuristics and optimization algorithms on shortest paths, min-cost flow, matching and traveling salesman problems.
SIE 549: Optimization for Machine Learning
Units: 3
Prerequisite(s): SIE 270, SIE 305, SIE 340
Usually offered: Spring
SIE 549 Syllabus (PDF)
This course will provide senior undergraduate and graduate students an introduction to mathematical nonlinear optimization with applications in machine learning and data science. This course will involve analysis of optimization algorithms, in particular, scalability of algorithms to large datasets will be discussed in theory and in implementation. The fundamental algorithms for nonlinear optimization are studied and applied to supervised learning models, including but not limited to nonlinear regression, logistic regression, support vector machines, and deep neural networks. Students will write their own implementation of the algorithms in the MATLAB/Python programming language and explore their performance on realistic data sets. May be convened with SIE 449.
SIE 550: Theory of Linear Systems
Units: 3
Prerequisite(s): SIE 305
Usually offered: Spring
SIE 550 Syllabus (PDF)
An intensive study of continuous and discrete linear systems from the state-space viewpoint, including criteria for observability, controllability, and minimal realizations; and optionally, aspects of optimal control, state feedback, and observer theory.
SIE 552: Space Systems Engineering
Units: 3
Usually offered: Spring
SIE 552 Syllabus (PDF)
Designing a system for a space mission (e.g. spacecraft, smallsat, cubesat) is a complex endeavor that requires the understanding of a variety of specialized subjects: orbital mechanics, attitude determination and control, space communications, thermal control, propulsion, power systems, structure and mechanisms. The course structure is conceived to provide the students with the skills and methodologies required to complete a preliminary design of a space system at both system and subsystem levels. Fundamentals of spacecraft subsystem design are introduced and embedded in a model-based system engineering process that will drive the preliminary design of a full-scale space system. The lectures will provide the technical content that drives the system and subsystem design that will be accomplished throughout the semester.
SIE 554A: Systems Engineering Process
Units: 3
Usually offered: Fall, Spring
SIE 554A Fall Syllabus (PDF) | SIE 554A Spring Syllabus (PDF)
Processes and tools for engineering large-scale, complex systems: resources, architecture, requirements, risk management, concept design, preliminary design, detail design, decision making, tradeoff studies, life-cycle models, requirements decomposition, verification planning, life cycle planning, product maintenance, teamwork, and documentation. May be convened with SIE 554A. May be convened with SIE 454A.
SIE 557: Project Management
Units: 3
Prerequisite(s): SIE 305 or consent of instructor
Usually offered: Fall, Spring
SIE 557 Fall Syllabus (PDF) | SIE 557 Spring Syllabus (PDF)
Processes and tools used to plan and control large scale projects. Topics include organizational design alternatives, formation and management of teams, construction and control of project schedules, risk assessment, and issues specific to global ventures and software development. May be convened with SIE 457.
SIE 558: Model-Based Systems Engineering
Units: 3
Prerequisite(s): Co-Requisite: SIE-454A/554A or consent of the instructor.
Usually offered: Spring
SIE 558 Syllabus (PDF)
An introduction to model-based systems engineering (MBSE), which is the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases. The course emphasizes practical use of the Systems Modeling Language (SysML) and MBSE methods.
SIE 562: Production Systems Analysis
Units: 3
Prerequisite(s): SIE 540, SIE 544
Usually offered: Spring
SIE 562 Syllabus (PDF)
Quantitative models in the planning, analysis and control of production systems. Topics include aggregate planning, multi-level production systems, inventory control, capacitated and uncapacitated lot-sizing, just-in-time systems and scheduling.
SIE 563: Integrated Logistics and Distribution Systems
Units: 3
Prerequisite(s): SIE 305, 321, 440/540
Usually offered: Spring
SIE 563 Syllabus (PDF)
Plan and design of efficient logistics and distribution systems. Topics include: supply chain management, integration of production/inventory/location/transportation decisions, shipment scheduling with incomplete and uncertain information, vehicle routing and scheduling, goods distribution networks with multiple transshipment, terminals and warehouses. Grading: Regular grades are awarded for this course: A B C D E.
SIE 565: Supply Chain Management
Units: 3
Prerequisite(s): SIE 305, SIE 340
Usually offered: Spring
SIE 565 Syllabus (PDF)
Fundamentals of Supply Chain Management including inventory/logistics planning and management, warehouse operations, procurement, sourcing, contracts and collaboration. Graduate-level requirements include an additional semester research paper. May be convened with SIE 465.
SIE 566: Life Cycle Analysis for Sustainable Design and Engineering
Units: 3
Prerequisite(s): Working knowledge of a programming language is required.
Usually offered: Spring
SIE 566 Syllabus (PDF)
This course will provide senior undergraduate and graduate students the conceptual, methodological, and scientific bases to quantify and improve the impact of engineering decisions on the environment, with a focus on applying life cycle analysis (LCA). The course will foster students to assess the environmental sustainability early on in their research to help design and develop more sustainable materials, products, and processes including manufacturing, logistics, and supply chain. Main topics covered include concept of life cycle thinking, computational structure of LCA, process based LCA, economic input-output LCA, LCA software tools and databases, case studies, recent development, and advanced topics in LCA. The students will be able to approach problems with life cycle perspectives, conduct LCA according to the ISO 14040 standards, and understand the strengths and weaknesses of LCA studies.
SIE 567: Financial Modeling for Innovation
Units: 3
Prerequisite(s): Graduate Student Status in Engineering or Science. No prior knowledge of engineering economics required. Reasonably proficient with Microsoft Excel.
Usually offered: Fall
SIE 567 Fall Syllabus (PDF)
Economic analysis of technology/business development for commercialization. Topics include Pro Forma financial statements, time value of money, valuation approaches and entrepreneurship.
SIE 571: Systems Cyber Security Engineering
Units: 3
Prerequisite(s): ECE 175 or instructor approval
Usually offered: Fall
SIE 571 Fall Syllabus (PDF) | SIE 571 Spring Syllabus (PDF)
The purpose of this course is to introduce selected topics, issues, problems and techniques in the area of System Cyber Security Engineering (SCSE), early in the development of a large system. Students will explore various techniques for eliminating security vulnerabilities, defining security specifications/plans and incorporating countermeasures to achieve overall system assurance. SCSE is an element of system engineering that applies scientific and engineering principles to identify, evaluate, and contain or eliminate system vulnerabilities to known or postulated security threats in the operational environment. SCSE manages and balances system security risk across all protection domains spanning the entire system engineering life cycle.
The fundamental elements of cyber security will be explored, including human cyber engineering techniques, penetration testing, mobile and wireless vulnerabilities, network mapping and security tools, embedded system security, reverse engineering, software assurance and secure coding, cryptography, vulnerability analysis, and cyber forensics. After a fundamental understanding of the various cyber threats and technologies are understood, the course will expand upon the basic principles, and demonstrate how to develop a threat/vulnerability assessment on a representative system using threat modeling techniques (i.e. modeling threats for a financial banking system, autonomous automobile or a power distribution system). May be convened with SIE 471.
SIE 572: Information Security and Research (INSuRE)
Units: 3
Prerequisite(s): Background in computer science, computer engineering, information technology or related technical field. One of the following is strongly recommended: SIE 471/571, ECE 478/578, ECE 509, or MIS 416/516
Usually offered: Spring
SIE 572 Syllabus (PDF)
This course engages students in diverse and varied national cybersecurity/information systems security problems, under an existing and very successful umbrella program called “INSuRE”, that enables a collaboration across several universities, Cyber professionals and cross-disciplined Cyber related technologies. Led by Stevens Institute of Technology, and made possible by a grant from the NSA and NSF, INSuRE has fielded a multi-institutional cybersecurity research course in which small groups of undergraduate and graduate students work to solve unclassified problems proposed by NSA, other US government agencies, and/or private organizations and laboratories. Students will learn how to apply research techniques, think clearly about these issues, formulate and analyze potential solutions, and communicate their results with sponsors and other participating universities.
Working in small groups under the mentorship of technical experts from government and industry, each student will formulate, carry out, and present original research on current cybersecurity/information assurance problems of interest to the nation. This course will be run in a synchronized distance fashion, coordinating activities with other INSuRE technical clients and sponsors, along with partnering universities which are all National Centers of Academic Excellence in Cyber Defense Research (CAE-R).
SIE 577: Introduction to Biomedical Informatics
Units: 3
Usually offered: Fall
SIE 577 Fall Syllabus (PDF)
Driven by efforts to improve human health and healthcare systems, this course will cover relevant topics at the intersection of people, information, and technology. Specifically, we will survey the field of biomedical informatics that studies the effective uses of biomedical data, information, and knowledge from molecules and cellular processes to individuals and populations, for scientific inquiry, problem solving, and decision-making. We will explore foundations and methods from both biomedical and computing perspectives, including hands-on experiences with systems, tools, and technologies in the healthcare system. Graduate students will be required to submit an additional assignment or project. May be convened with SIE/BME 477.
SIE 578: Artificial Intelligence for Health and Medicine
Units: 3
Prerequisite(s): Course suitable for majors APPL, BME, ECE, MEE, CSC, SIE, STAT, IS or MIS, or with instructor consent. Basic foundations in linear algebra, discrete mathematics, probability and statistics, and data structures are recommended for this course.
Usually offered: Periodically
SIE 578 Syllabus (PDF)
The practice of modern medicine in a highly regulated, complex, sociotechnical enterprise is a testament to the future healthcare system where the balance between human intelligence and artificial expertise will be at stake. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in medicine. We will explore foundational methods in artificial intelligence (AI) with greater emphasis on machine learning and knowledge representation and reasoning, and apply them to specific areas in medicine and healthcare including, but not limited to, clinical risk stratification, phenotype and biomarker discovery, time series analysis of physiological data, disease progression modeling, and patient outcome prediction. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, clinical/healthcare applications, and relevant tools.
SIE 581: Design Additive Manufacturing
Units: 3
Prerequisite(s): SIE majors: Advanced Standing; SIE 383, Other ENGR majors: Advanced Standing; (AME 211 or BE 221) and (AME 324A or MSE 370)
Usually offered: Fall
SIE 581 Fall Syllabus (PDF)
This course is an introduction to the engineering design process with a focus on understanding constraints and opportunities associated with additive manufacturing (AM). Students will gain an understanding of how to exploit AM to manufacture parts with complex geometry, while also considering economic viability and manufacturability. Opportunities and constraints associated with various AM technologies, from fused-filament fabrication (often called 3D printing) to metal AM processes, will be surveyed. The course will culminate in a hands-on design project where students will use design-for-additive-manufacturing (DfAM) frameworks and tools to design a novel product. This course aims to promote creativity and critical thinking, which are necessary to effectively use AM technology in the context of product design.
SIE 596: Special Topics in Systems and Industrial Engineering
Units: 3
Usually offered: Fall, Spring, Summer
SIE 596 - Smart Manufacturing Systems - Fall (PDF) | SIE 596 - Engineering Entrepreneurship - Spring (PDF) | SIE 596 - Robotic Systems - Spring (PDF) | SIE 596 Summer Syllabus (PDF)
This course is designed to provide a flexible topics course across several domains in the field of Systems Engineering, Industrial Engineering, and Engineering Management. Students will develop and exchange scholarly information in a small group setting. Selected advanced topics in Systems and Industrial Engineering and Operations Research, such as:
- optimization
- stochastic systems
- systems engineering and design
- human cognition systems
- informatics
SIE 599: Independent Study
Units: 3
Usually offered: Fall, Spring, Summer
Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699, or 799. May be repeated an unlimited number of times, consult your department for details and possible restrictions. (1-5 units)
SIE 606: Advanced Quality Engineering
Units: 3
Prerequisite(s): SIE 530, SIE 506
Usually offered: Periodically
SIE 606 Syllabus (PDF)
Advanced techniques for statistical quality assurance, including multivariate control charting, principal components analysis, economic design of acceptance sampling plans and control charts, inspection errors, and select papers from the recent literature.
SIE 640: Large-Scale Optimization
Units: 3
Prerequisite(s): SIE 544 or 545
Usually offered: Periodically
SIE 640 Fall Syllabus (PDF)
Decomposition-coordination algorithms for large-scale mathematical programming. Methods include generalized Benders decomposition, resource and price directive methods, subgradient optimization, and descent methods of nondifferentiable optimization. Application of these methods to stochastic programming will be emphasized.
SIE 644: Integer and Combinatorial Optimization
Units: 3
Prerequisite(s): SIE 544
Usually offered: Periodically
SIE 644 Spring Syllabus (PDF)
Modeling and solving problems where the decisions form a discrete set. Topics include model development, branch and bound methods, cutting plane methods, relaxations, computational complexity, and solving well-structured problems.
SIE 645: Nonlinear Optimization
Units: 3
Prerequisite(s): SIE544 or SIE 545
Usually offered: Periodically
SIE 645 Fall Syllabus (PDF)
This course is devoted to basic theory and algorithms for nonlinear optimization (unconstrained and constrained), including: understanding how algorithms work; choosing appropriate method to solve the problem in different situations; interpreting the performance of algorithms and analyzing the solutions for decision making.
SIE 649: Topics of Optimization
Units: 3
Usually offered: Fall
SIE 649 Syllabus (PDF)
Convexity, optimality conditions, duality and topics related to the instructor’s research interest; e.g., stochastic programming, nonsmooth optimization, interior point methods.
SIE 654: Advanced Concepts in Systems Engineering
Units: 3
Prerequisite(s): SIE 554A
Usually offered: Periodically
SIE 654 Syllabus (PDF)
Modeling and design of complex systems using the Unified Modeling Language (UML), the Systems Modeling Language (SysML) and Wymorian System Theory. Applications come from systems, hardware and algorithm design. Course will emphasize architecture, requirements, testing, risk analysis and use of various systems design tools.
SIE 695A: Colloquium
Units: 3
Prerequisite(s): Consult department before enrolling.
Usually offered: Fall, Spring
SIE 695A Fall Syllabus (PDF) | SIE 695A Spring Syllabus (PDF)
The Graduate Seminar meets in-person, once a week. Guest speakers from different SIE related domains are invited to present their research work.
SIE 696: Special Topics in Advanced Systems and Industrial Engineering
Units: 3
Prerequisite(s): 1) Backgrounds in high-level programming language, probability and statistics, linear algebra, and math analysis, or , 2) consent of instructor.
Usually offered: Periodically
SIE 696 Syllabus (PDF)
This course is designed to provide a flexible advanced topics course across several domains in the field of Systems Engineering, Industrial Engineering, and Engineering Management. Students will develop and exchange scholarly information in a small group setting. Selected advanced topics in Systems and Industrial Engineering and Operations Research, such as 1) optimization, 2) stochastic systems, 3) systems engineering and design, 4) human cognition systems, and 5) informatics. Course may be repeated for a maximum of 9 unit(s) or 3 completion(s).
SIE 699: Independent Study
Units: 3
Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699. (1-6 units)
Usually offered: Spring, Summer
SIE 900: Research
Units: 3
Usually offered: Fall, Spring, Summer
Individual research not related to thesis or dissertation preparation by graduate students. (1-6 units)
SIE 909: Master's Report
Units: 3
Usually offered: Spring, Summer
Individual study or special project or formal report thereof submitted in lieu of thesis for certain master's degrees. (1-12 units)
SIE 910: Thesis
Units: 3
Usually offered: Fall, Spring, Summer
Research for the master's thesis (whether library research, laboratory or field observation or research, artistic creation, or thesis writing). Maximum total credit permitted varies with the major department. (1-12 units)
SIE 920: Dissertation
Units: 3
Usually offered: Fall, Spring, Summer
Research for the doctoral dissertation (whether library research, laboratory or field observation or research, artistic creation, or dissertation writing) (1-12 units)
Coming Soon
SIE 502: Ontologies for Systems Engineering
Units: 3
This course presents critical skills for building information-intensive applications for engineering applications. It teaches ontologies as a formalism to create a common vocabulary for engineering development with precise syntax and logical semantics. Such vocabulary is then used to improve collaboration and communication among engineers and other stakeholders. Moreover, ontologies enable the development of effective engineering-specific solutions with reduced time and effort. Such solutions support modeling, analysis, and review of engineering and programmatic information while enabling federated and collaborative work that can evolve incrementally and be integrated continuously. By mastering these skills, you can tackle real-world problems in your chosen engineering application.
SIE 503: Digital Thread and Model Management
Units: 3
This course presents the foundations of the design and management of digital threads. Students will learn the fundamental characteristics of digital engineering and related approaches such as model-based engineering, before exploring key concepts such as the digital thread and the digital twin. The course will present an approach to capture the need for a digital thread as a use case. Students will then learn the concepts of data interoperability and technical interoperability and will explore the advantages and disadvantages of various approaches that support interoperability. These concepts will include APIs, data standards, data transformations, and ontologies. The course will also present the notion of change management as a crucial consideration of digital thread design. Finally, students will be required to review these considerations in parallel and describe how a digital thread could be deployed in response to a particular problem.
SIE 504: Federated Modeling and Simulation
Units: 3
This course delves into the integration of heterogeneous models and simulations to enhance system design, analysis, and lifecycle management. It covers interoperability standards like HLA and TENA, techniques for integrating diverse models, the development and application of digital twins, and the strategic use of federated M&S across the system lifecycle. Students will learn best practices for deploying federated M&S in various industries, preparing them to implement these cutting-edge techniques in real-world engineering projects
SIE 516: Mission Engineering
Units: 3
This course covers a broad spectrum of essential topics, starting with the foundational principles of mission engineering and systems of systems (SoS), and moving onto the limitations of traditional engineering methods. Participants will delve into the use of mission threads for identifying operational needs, the development of mission architectures and governance structures, and the crucial aspects of safety, security, integration, and interoperability. The curriculum also emphasizes cost estimation, risk analysis, experimentation, and the pivotal role of modeling and simulation in framing and executing missions. Through these topics, learners will acquire a comprehensive understanding of how to navigate the complexities of modern engineering challenges, ensuring the success of mission-critical systems in various domains.
SIE 517: Systems Engineering Strategy and Implementation
Units: 3
This course offers a deep dive into the strategic and practical aspects of implementing Systems Engineering (SE) across various projects. It equips students with the knowledge to adapt SE principles to diverse contexts, enabling them to apply SE strategies effectively to facilitate successful system development outcomes. Students will explore the Systems Engineering Management Plan (SEMP) as the cornerstone of the implementation strategy, emphasizing its customization to project-specific needs. Key aspects such as defining SE principles and approaches, integrating technical efforts, managing key activities, and tailoring SE to project demands will be thoroughly examined. The course also delves into technology insertion and integration with project plans and other disciplines across the lifecycle.
SIE 521: System Integration
Units: 3
This course offers a comprehensive exploration of system integration principles and practices, focusing on general systems beyond software-intensive frameworks. It addresses the challenges and methodologies involved in planning the integration and integrating various subsystems into a coherent and functional whole, ensuring compatibility, interoperability, and integrated performance. Participants will learn about integration strategies, tools, and techniques applicable to a wide range of industries, including the use of digital twins and digital models as pivotal tools for early system integration, enabling engineers to simulate, analyze, and evaluate integrated functionality and performance, as well as integration procedures, before physical integration occurs
SIE 559: Engineering Entrepreneurship
Units: 3
The Engineering Entrepreneurship course for Masters CAPSTONE equips students with the essential skills and mindset required to navigate the complex landscape of entrepreneurial ventures within the engineering domain. By blending theoretical frameworks with practical applications, this course guides students through the journey of identifying problems, validating ideas, launching, and scaling successful businesses. Through interactive sessions, case studies, and hands-on projects, students will develop entrepreneurial acumen essential for driving innovation and value creation in today's competitive market. The course also focuses on evaluating the market viability of new ideas, shaping these ideas into the right products or services for the right markets, and developing strategies for product positioning, marketing, and operations. Additionally, students will learn how to acquire necessary resources such as people, financing, and strategic partners, as well as assuming leadership roles in high-tech ventures.
Not Currently Offered
SIE 511: Human-Machine Interaction
SIE 512: Human Factors Research Methods
SIE 525: Queuing Theory
SIE 536: Experiment Design and Regression
SIE 544: Linear Programming
SIE 555: Sensor Systems Engineering
SIE 556: Fundamental of Guidance for Aerospace Systems
SIE 561: Traffic Modeling & Simulation
SIE 570: Intelligent Control Systems & Application
SIE 573: Engineering of Trustworthy Secure Systems
SIE 583: Computer Integrated Manufacturing Systems (CIM)
SIE 608: Advanced Reliability Engineering (is listed as Quality on our RCS sheet)
SIE 631: Distributed Multi-Paradigm Simulation Systems
SIE 678: Transportation Systems