Major dimensions of systems engineering will be covered and demonstrated through case studies: (1) The history, philosophy, art, and science upon which systems engineering is grounded; including system thinking and guiding principles and steps in the `systems engineering approach¿ to problem solving; and (2) The basic tools of systems engineering analysis, including; goal definition and system representation, requirements analysis, system assessment and evaluation, mathematical modeling, and decision analysis.

This course provides students with the background necessary to model, store, manipulate, and exchange information to support decision making. It covers Unified Modeling Language (UML), SQL, and XML; the development of semantic models for describing data and their relationships; effective use of SQL; web-based technologies for disseminating information; and application of these technologies through web-enabled database systems. Corequisite: CS 2100 or CS 2110.

This course examines the lifecycle of engineered systems (ES) and the public policies developed to regulate them. It covers risks, costs, benefits, and equity as common evaluation criteria for ES and their regulatory policies. It uses case studies and basic tools of decision analysis to critically evaluate the tradeoffs involved in developing and regulating ES through public policy. Pre-reqs: (STS 1500 or ENGR 1020 or ENGR 2595 - Engineering Foundations II) and (APMA 1110 or MATH 1320), and (CHEM 1410 or CHEM 1810), and (PHYS 1425 or PHYS 1420 or PHYS 1710).

Introduction to deterministic optimization models: theory, algorithms, and applications. Coverage begins with highly structured network optimization models and ends with unstructured linear optimization models. Applications include (1) telecommunications network planning and design, (2) design and utilization of transportation and distribution networks, and (3) project management and scheduling. Corequisite: SYS 2001 and APMA 3080.

An introduction to the fundamentals for the analysis, design and evaluation of human-centered systems. For example, user interaction can be designed to leverage the strengths of people in controlling automation and analyzing data. Course topics include Task, User and Work Domain Analysis, User Interface Design Principles, Human Cognition and Information Processing (Top-Down Design), Human Perception (Bottom-Up Design), and Usability Testing. Corequisite: SYS 2001.

Focuses on the evaluation of candidate system designs and design performance measures. Includes identification of system goals; requirements and performance measures; design of experiments for performance evaluation; techniques of decision analysis for trade-studies; presentation of system evaluation and analysis results. Illustrates the concepts and processes of systems evaluations using case studies. Pre-reqs: APMA 3120, SYS 2001, & SYS 3021.

What is a globally sustainable standard of living? How should governments balance the desire to create national income and provide for the human needs of their citizens, against the desire to conserve natural resources and the environment for future generations? This course takes a systematic approach to answering these questions by evaluating sustainability in the context of human needs.

Focuses on the practice of systems engineering directly from current systems engineers. A variety of topics are covered by invited speakers from industry, government, and the academy. Discussions include engineering design projects, alternative career paths, graduate studies, professional development, and more immediate options with opportunities for summer internships and capstone projects. Prereq: 3rd Year standing in systems engineering.

This is an introductory course on modeling probabilistic systems. The emphasis will be on model formulation and probabilistic analysis. Topics to be covered include general stochastic processes, discrete and continuous time Markov chains, the Poisson Process, Non-Stationary Poisson Processes, Markov Decision Processes, Queueing Theory, and other selected topics. Prerequisite: APMA 3100 or MATH 3100.

A first course in the theory & practice of discrete-event simulation. Monte Carlo methods, generating random numbers & variates, spreadsheet add-ins & applications, sampling distributions & confidence intervals, input analysis & distribution fitting. Discrete-event dynamic systems, modeling, simulation logic & data structures, output analysis, model verification & validation, comparing alternative systems, simulation optimization, case studies. Prerequisite: CS 2100, APMA 3100, and APMA 3120

A third-year level undergraduate course focused on a topic not normally covered in the course offerings. The topic usually reflects new developments in the systems and information engineering field. Offering is based on student and faculty interests. Prerequisites: Instructor Permission

A third-year level undergraduate course focused on a topic not normally covered in the course offerings. The topic usually reflects new developments in the systems and information engineering field. Offering is based on student and faculty interests.Prerequisites: Instructor Permission

This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Prerequisite: CS 2100, APMA 3100 and APMA 3120.

A case-based approach to the design of user interfaces with a focus on iterative project experiences. Display design concepts are related to ecological factors, situational awareness, attention, vision, and information processing. Project cases are tied to real-world problems of decision support on mobile platforms, large scale command and control, and data visualization, among others. Prerequisites: SYS 3023 or CS 3205.

"This course is an introduction to the theory of the industrial organization (from a game-theoretic perspective) and its applications to industries with strong engineering content (electricity, telecommunications, software & hardware etc.) Topics include: congestion pricing in networks, pricing and efficiency in electricity markets, planned obsolescence in software development, ""network"" effects and the dynamics of technology adoption etc. Prerequisites: ECON 2010 and a course in probability (either APMA 3100, APMA 3110, or Math 3100)."

This course is an introduction to the theory, methods, and applications of risk analysis and systems engineering. The topics include research and development priorities, risk-cost-benefit analysis, emergency management, human health and safety, environmental risk, extreme events, infrastructure resilience, system interdependencies, and enterprise systems. Corequisites: a course in probability (APMA 3100 or APMA 3110 or Math 3100).

A design project extending throughout the fall semester. Involves the study of an actual open-ended situation. Includes problem formulation, data collection, analysis and interpretation, model building & analysis, and generation of solutions. Prerequisite: must have successfully completed 6 or more courses in the standard SYS curriculum (SYS 2001, SYS 2202, and 4 of the following: SYS 3021, SYS 3023, SYS 3034, SYS 3060, and SYS 3062).

A design project extending throughout the fall and spring semesters. Involves the study of a real-world, open-ended situation, including problem formulation, data collection, analysis and interpretation, model building and analysis, and generation of solutions. Students work on the same project with the same team in SYS 4053 and 4054 in subsequent semesters.  Pre-requisite:  SYS 4053

This is a colloquium that allows fourth-year students to learn about engineering design, innovation, teamwork, technical communication, and project management in the context of their two-semester systems capstone design project. Prerequisite: must have successfully completed 6 or more courses in the standard SYS curriculum (SYS 2001, SYS 2202, and 4 of the following: SYS 3021, SYS 3023, SYS 3034, SYS 3060, and SYS 3062)

Detailed study of a selected topic determined by the current interest of faculty and students. Offered as required. Prerequisite: As specified for each offering.

Detailed study of a selected topic determined by the current interest of faculty and students. Prerequisite: As specified for each offering.

Independent study or project research under the guidance of a faculty member. Offered as required. Prerequisite: As specified for each offering.

Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.

An integrated introduction to systems methodology, design, and management. An overview of systems engineering as a professional and intellectual discipline, and its relation to other disciplines, such as operations research, management science, and economics. An introduction to selected techniques in systems and decision sciences, including mathematical modeling, decision analysis, risk analysis, and simulation modeling. Elements of systems management, including decision styles, human information processing, organizational decision processes, and information system design for planning and decision support. Emphasizes relating theory to practice via written analyses and oral presentations of individual and group case studies. Prerequisite: Admission to the graduate program.

Provides an introduction to the problems encountered when integrating large systems, and also presents a selection of specific technologies and methodologies used to address these problems. Includes actual case-studies to demonstrate systems integration problems and solutions. A term project is used to provide students with the opportunity to apply techniques for dealing with systems integration. Prerequisite: SYS 6001 or instructor permission.

This course is an introduction to theory and application of mathematical optimization. The goal of this course is to endow the student with a) a solid understanding of the subject's theoretical foundation and b) the ability to apply mathematical programming techniques in the context of diverse engineering problems. Topics to be covered include a review of convex analysis (separation and support of sets, application to linear programming), convex programming (characterization of optimality, generalizations), Karush-Kuhn-Tucker conditions, constraint qualification and Lagrangian duality. The course closes with a brief introduction to dynamic optimization in discrete time. Prerequisite: Two years of college mathematics, including linear algebra, and the ability to write computer programs.

Covers basic stochastic processes with emphasis on model building and probabilistic reasoning. The approach is non-measure theoretic but otherwise rigorous. Topics include a review of elementary probability theory with particular attention to conditional expectations; Markov chains; optimal stopping; renewal theory and the Poisson process; martingales. Applications are considered in reliability theory, inventory theory, and queuing systems. Prerequisite: APMA 3100, 3120, or equivalent background in applied probability and statistics.

An introduction to the analysis, design and evaluation of human-centered systems. User interaction can be designed to leverage the strengths of people in controlling automation and analyzing data. Topics include Task, User and Work Domain Analysis, User Interface Design Principles, Human Cognition and Information Processing, Human Perception, and Usability Testing. Graduate version includes separate project review sessions.

Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system's response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining. Prerequisite: SYS 6021, SYS 4021, or STAT 5120.

This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Course lectures concentrate on theory and practice.

A case-based approach to the design of user interfaces with a focus on iterative project experiences. Display design concepts are related to ecological factors, situational awareness, attention, vision, and information processing. Project cases are tied to real-world problems of decision support on mobile platforms, large scale command and control, and data visualization, among others. Graduate version includes 4-5 advanced discussion sessions.

A first graduate course covering the theory and practice of discrete-event stochastic simulation. Coverage includes Monte Carlo methods and spreadsheet applications, generating random numbers and variates, specifying input probability distributions, discrete-event simulation logic and computational issues, review of basic queueing theory, analysis of correlated output sequences, model verification and validation, experiment design and comparison of simulated systems, and simulation optimization. Emphasis includes state-of-the-art simulation programming languages with animation on personal computers. Applications address operations in manufacturing, distribution, transportation, communication, computer, health care, and service systems. Prerequisite: SYS 6005 or equivalent background in probability, statistics, and stochastic processes.

The goals of this course are to educate graduate students in SEAS in the ethical conduct of research & publication, and to facilitate the thoughtful integration of ethics into their engineering research & practice. This is done by i) engaging students in deliberative readings, discussion, & writing about EERP, and ii) using cases to consider the ethical dimensions of engineering and resources to support the engineer facing ethical dilemmas.

This course is an introduction to the theory of the industrial organization (from a game-theoretic perspective) and its applications to industries with strong engineering content (electricity, telecommunications, software and hardware, etc.). Topics include: congestion pricing in networks, pricing and efficiency in electricity markets, planned obsolescence in software development, "networks" effects and the dynamics of technology adoption.Prerequisite: ECON 2010, APMA 3100 or 3110.

A study of technological systems, where decisions are made under conditions of risk and uncertainty. Topics include conceptualization (the nature, perception, and epistemology of risk, and the process of risk assessment and management) systems engineering tools for risk analysis (basic concepts in probability and decision analysis, event trees, decision trees, and multiobjective analysis), and methodologies for risk analysis. Prerequisite: APMA 3100, SYS 3021, or equivalent.

This course will teach students the required skills, concepts, and algorithms to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and kinematics, control, sensing, localization, mapping, path planning, and motion planning. Besides theory, students are exposed to simulation environments and lab exercises with real robotic systems.

For master's students.

Interactions between robots and humans are influenced by form, function and expectations. Quantitative techniques evaluate performance of specific tasks and functions. Qualitative techniques are used to evaluate the interaction and to understand expectations and perceptions of the human side of the interaction. Students use humanoid robots to develop and evaluate interactions within a specific application context.

Special Topics in Distance Learning

Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.

Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.

Cyber-physical systems (CPS) are smart systems that include co-engineered interacting networks of physical and computational components. This course will teach students the required skills to analyze the CPS that are all around us, so that when they contribute to the design of CPS, they are able to understand important safety and security aspects and feel confident designing and analyzing CPS systems.

This course is designed to develop cross-competency in the technical, analytical, and professional capabilities necessary for the emerging field of Cyber-Physical Systems (CPS). It provides convergence learning activities based around the applications, technologies, and system designs of CPS as well as exploring the ethical, social, and policy dimensions of CPS work. The course also emphasizes the importance of communication as a necessary skill.

Detailed study of graduate course material on an independent basis under the guidance of a faculty member.

Formal record of student commitment to project research under the guidance of a faculty advisor. Registration may be repeated as necessary.

Bayesian theory of forecasting and decision making; judgmental procedures and statistical models for probabilistic forecasting, post-processors of deterministic forecasts; sufficient comparisons of forecasters, verification of forecasts, combining forecasts; optimal decision models using probabilistic forecasts including static decision models, sequential decision models, stopping-control models; economic value of forecasts. Prerequisite: SYS 6005 or STAT 6190.

Weekly meeting of graduate students and faculty for presentation and discussion of contemporary systems engineering problems and research. This seminar is offered every spring and fall semesters.

Detailed study of an advanced or exploratory topic determined by faculty and student interest. Offered as required.

Detailed study of graduate course material on an independent basis under the guidance of a faculty member.

Formal record of student commitment to project research for Master of Engineering degree under the guidance of a faculty advisor. Registration may be repeated as necessary.

Formal record of student commitment to master's research under the guidance of a faculty advisor. Registration may be repeated as necessary.

For doctoral students.

For doctoral students.