Graduate Courses

This course is designed for students with a background in engineering, technology, or science that have not taken a class in statistics or need a refresher class.  In this class we will apply probability and statistics throughout a system’s life cycle. Topics include the roles of probability and statistics in Systems Engineering, the nature of uncertainty, axioms and properties of probability models and statistics, hypothesis testing, design of experiments, basic performance requirements, quality assurance specification, functional decomposition, technical performance measurements, statistical verification, and simulation.

Application of mathematical optimization techniques, including linear and nonlinear methods, to design and manufacture of devices and systems of interest to mechanical engineers; optimization techniques include: constrained and unconstrained optimization in several variables, problems for structured multi-stage decision, and linear programming; formulation of design and manufacturing problems using computer- based methods; optimum design of parts and assemblies to minimize the cost of manufacture.

The growing complexity of today’s engineered systems presents daunting challenges to those who are charged with creating, operating, enhancing and sustaining them throughout their lifecycles.  While the components of these systems require no less design effort than in the past, attention to the components is not sufficient to ensure overall system success.  This course focuses on the interactions between the elements of a complex system, the context within which they are designed and operate, and the relationships between the technical systems and the organizations that design them and the enterprises that they serve.  Students develop the understanding of techniques and processes that can help them ensure that their individual contributions are not only excellent in themselves, but that they become part of a cohesive, successful whole.  This course may not be applied toward a Master’s of Engineering in Systems Engineering or Engineering Management.

This course is an application oriented with theoretical arguments approached from an intuitive level rather than from a rigorous mathematical approach. This course teaches the student how statistical analyses are performed while assuring the student an understanding of the basic mathematical concepts. The course will focus on "real world" uses of statistical analysis and reliability theory. The student will use the software to solve problems. Included in this course will demonstrate Markov modeling techniques. This course is a perquisite to the System Reliability and Life Cycle Analysis course.

This course will explore and discuss issues related to the integration and testing of complex systems. First and foremost, students will be exposed to issues relating to the formulation of system operational assessment and concept. Subsequently, functional modeling and analysis methods will be used to represent the system functionality and capability, leading to the packaging of these functions and capabilities into high-level system architecture. Specific focus will be given to issues of interface management and testability. The course will also address the related management issues pertaining to integrated product teams, vendors and suppliers, and subcontractors. In addition, selected articles will be researched to demonstrate the techniques explored in class.

This course emphasizes the development of modeling and simulation concepts and analysis skills necessary to design, program, implement, and use computers to solve complex systems/products analysis problems. The key emphasis is on problem formulation, model building, data analysis, solution techniques, and evaluation of alternative designs/ processes in complex systems/products. Overview of modeling techniques and methods used in decision analysis, including Monte Carlo and discrete event simulation is presented.

This course provides a hands-on introduction to the modern techniques for visualizing data and leverages such techniques with the corresponding problem solving skills necessary to complement data visualization into specific strategic decision making.  The student will first learn to use the latest off the shelf software for data visualization.  In specific the student will learn  the following languages: R, D3, Google refine and Spot fire.

This course provides an hands-on introduction to the major techniques and solutions to discover knowledge in data and text.  Traditional data mining along with text mining and network analysis will be presented and will be used by the students via open source software, addressing information mining needs on both structured and unstructured data.

This course enables the Engineering Management student to acquire the knowledge and skills he/she will need to handle the variety and volume of information encountered in today’s workplace.  The course uses Python, which is rapidly becoming the language of choice for information handling and data analysis.  Students will work with both structured and semi-structured data.

This course discusses fundamentals of systems engineering. Initial focus is on need identification and problems definition. Thereafter, synthesis, analysis, and evaluation activities during conceptual and preliminary system design phases are discussed and articulated through examples and case studies. Emphasis is placed on enhancing the effectiveness and efficiency of deployed systems while concurrently reducing their operation and support costs. Accordingly, course participants are introduced to methods that influence system design and architecture from a long-term operation and support perspective.

This will test the knowledge of students who have achieved the equivalent of Level I certification through the Defense Acquisition University or who have completed selected industry training programs. Typically students take 80 hours training for this certification level equivalent. Upon successful completion (graded pass/fail), students will be awarded 3 credits toward a Master of Engineering in Systems Engineering.

This will test the knowledge of students who have achieved the equivalent of Level II certification through the Defense Acquisition University or who have completed selected industry training programs. Typically students take more than 160 hours training for this certification level equivalent. Upon successful completion (graded pass/fail), students will be awarded between 3 and 6 credits toward a Master of Engineering in Systems Engineering.

This course examines the real-world application of the entire space systems engineering discipline. Taking a process-oriented approach, the course starts with basic mission objectives and examines the principles and practical methods for mission design and operations in depth. Interactive discussions focus on initial requirements definition, operations concept development, architecture tradeoffs, payload design, bus sizing, subsystem definition, system manufacturing, verification and operations. This is a hands-on course with a focus on robotic missions for science, military and commercial applications.

This unique course gives students a hands-on opportunity to apply key principles of space systems engineering. In part 1 of the course, students are given a set of customer expectations in the form of broad mission objectives. Using state-of-the-industry mission design and analysis tools (provided), the task is to apply systems engineering process to define top-level system requirements and design key elements of the system. The end result will be a system design review during which students present and defend their design decisions. In part 2 of the course, students experience system realization processes first-hand by integrating, verifying, validating and delivering the shoe box-sized EyasSAT educational satellite. Lecture is combined with hands-on experience. From the part-level to the system level, students will implement a rigorous assembly, integration, verification and validation plan on real hardware and software applying "test like you fly, fly like you test" principles.

This course provides the conceptual framework for developing space missions of human spacecraft starting from a blank sheet of paper. It describes and teaches the human space mission design and analysis process. The entire course is process oriented to equip each participant with practical tools to complete a conceptual design and analyze the impacts of evolving requirements. At the end of this course you will be better able to tie mission elements together and perform tradeoffs between system design and mission operations that must occur, during the early stages of planning, in order to deliver cost-effective results.

This course provides an integrated view of space launch and transportation systems (SLaTS) design and operations. It analyzes customer needs, objectives and requirements, through launch and transportation system design, development, test and manufacturing to creating operations concepts and infrastructure capabilities. Lifecycle cost and the business case will be assessed. The thrust of this course is to identify technical risk and mitigate it in the most cost-effective manner, while maintaining the technical integrity of the vehicle(s) and infrastructure. In the course you will take a fresh look at space launch and transportation systems by emphasizing a process-oriented approach for creating cost-effective concepts to meet customer needs and objectives. This process describes how to translate SLaTS objectives, requirements, and constraints into viable and cost-effective operations concepts. Vehicle design presentations show practical, detailed approaches and tools to analyze and design manned and unmanned, reusable and expendable vehicles for both launch and interplanetary systems, including architecture and configuration, payloads, and vehicle subsystems. Course presentations on launch operations describe the functions to be performed, define and evaluate the key issues, help you develop an appropriate operations concept, and assess the complexity and cost of operations. Special emphasis is placed on describing the interrelationships and tradeoffs between system design and launch operations that must occur during the early stages of planning in order to deliver effective systems.

This course examines the real-world space mission operations. Taking a process-oriented approach, the course provides an in-depth view of the entirety of space mission operations, including the concept of operations and all functions that are performed in support of a space mission. Interactive discussions focus on initial requirements definition, operations concept development, functional allocation among spacecraft, payload, ground system and operators. A detailed model is provided that allows the user to estimate operations complexity and then prepare an estimate of the number of operators required and overall cost. This is a hands-on course with a focus on space missions for science, military and commercial applications.

This unique course gives participants a hands-on opportunity to apply key principles of space systems engineering. Participants are given a set of customer expectations in the form of broad mission objectives for a crew exploration vehicle with the task of applying systems engineering process to define top-level system requirements and design key elements of the system. The end result will be a system design review during which students present and defend their design decisions.  Course participants are given a set of mission objectives in the form of a Request for Proposal (RFP) or Announcement of Opportunity (AO) and divided into competing groups to conceptually design a viable crewed mission that meets the customer expectations at an acceptable lifecycle cost. The groups are guided through a structured space system engineering approach to define a mission concept and supporting space mission architecture, and complete a detailed analysis.

The supportability of a system can be defined as the ability of the system to be supported in a cost effective and timely manner, with a minimum of logistics support resources. The required resources might include test and support equipment, trained maintenance personnel, spare and repair parts, technical documentation and special facilities. For large complex systems, supportability considerations may be significant and often have a major impact upon life-cycle cost. It is therefore particularly important that these considerations be included early during the system design trade studies and design decision-making.

This course provides the participant with the tools and techniques that can be used early in the design phase to effectively influence a design from the perspective of system reliability, maintainability, and supportability. Students will be introduced to various requirements definition and analysis tools and techniques to include quality function deployment, input-output matrices, and parameter taxonomies. An overview of the system functional analysis and system architecture development heuristics will be provided.  Further, the students will learn to exploit this phase of the system design and development process to impart enhanced reliability, maintainability, and supportability to the design configuration being developed. Given the strategic nature of early design decisions, the participants will also learn selected multiattribute design decision and risk analysis methodologies, including Analytic Hierarchy Process (AHP). As part of the emphasis on maintainability, the module addresses issues such as accessibility, standardization, modularization, testability, mobility, interchangeability and serviceability and the relevant methods, tools, and techniques. Examples and case studies will be used to facilitate understanding of these principles and concepts.

This course discusses the fundamentals of system architecting and the architecting process, along with practical heuristics. Furthermore, the course has a strong "how-to" orientation, and numerous case studies are used to convey and discuss good architectural concepts as well as lessons learned. Adaptation of the architectural process to ensure effective application of COTS will also be discussed. In this regard, the course participants will be introduced to an architectural assessment and evaluation model. Linkages between early architectural decisions, driven by customer requirements and concept of operations, and the system operational and support costs are highlighted.

This course is designed to enable engineers, scientists, and analysts from all disciplines to recognize potential benefits resulting from the application of robust engineering design methods within a systems engineering context. By focusing on links between sub-system requirements and hardware/software product development, robust engineering design methods can be used to improve product quality and systems architecting. Topics such as Design and Development Process and Methodology, Need Analysis and Requirements Definition, Quality Engineering, Taguchi Methods, Design of Experiments, Introduction to Response Surface Methods, and Statistical Analysis of Data will be presented.

This course is a study of analytic techniques for rational decision-making that addresses uncertainty, conflicting objectives, and risk attitudes. This course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees and influence diagrams; solving value hierarchies; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.

This course serves as an overview of phenomenology and technologies associated with the development, design, construction, and life cycle management of network centric systems and systems of systems. The goal of this class is to provide the early career engineers and scientists who have been educated in a traditional academic disciplines, visibility into the interdisciplinary methods, processes, terminology, and tools needed to integrate these technologies into an operationally and cost effective adaptive network centric systems of systems.

This course covers the theory and application of modeling aggregate demand, fragmented demand and consumer behavior using statistical methods for analysis and forecasting for facilities, services and products. It also aims to provide students with both the conceptual basis and tools necessary to conduct market segmentation studies, defining and identifying criteria for effective segmentation, along with techniques for simultaneous profiling of segments and models for dynamic segmentation. All of this provides a window on the external environment, thereby contributing input and context to product, process and systems design decisions and their ongoing management.

The first course focuses on the conceptual design portion of the lifecycle of Cyber-Physical systems.  Critical elements include the ideas of systems and design thinking, and elegant design.  An Ideation process, as pioneered by the likes of IDEA and other prominent design firms is used to spark the creative process.  The opportunity is conceived and defined using Kano Maps, marketing segmentation and conjoint analysis techniques.  A QFD process is used to collect, organize and analyze customer needs, and transform these into product specifications.  Concepts are generated, selected and tested.  Finally, these concepts are specified using concept of operation, conceptual design and using case scenarios and technical requires.  A workshop on Model-Based Systems Engineering (MBSE), particularly, SysML is used as a means for specification and also to provide a foundation for future modeling work.  Lectures are interspersed with individual and group project based activities.  the students go through a design review process in preparation for their final report.

Ensuring systems work and are robust educates students on the transition from cyber-physical system concept and preliminary requirements to detailed architecture and design based on prioritized, allocated and traceable architecturally significant requirements.  Students will create models of system structure and activities, make appropriate technology selections, and perform analyses for reliability, performance, safety and security.  Trade space analyses will be performed.

This third course focuses on the continuous implementation, integration, testing, analysis, and verification and validation of cyber systems (CPS).  This course builds on Course 2 using a metaphor where the students plan on a successful product launch, using our project to work through continuous integration and test and ultimately bringing a robust solution to life in the form of a working CPS system.  We intersperse lectures with individual and group project based activities to ensure that the developed system is functional and robust.  We discuss and use the most effective techniques for fault and failure tolerance, analysis, and testing method and principles.  We plan to capitalize on simulation and physical systems resources for continuous and automated testing and discuss the balance of testing versus analysis.  The students will continuously collect evidence of quality, performance measures and traceability information.

This course focuses on managing the evolution of a cyber-physical system after its initial release to the market until its retirement.  The course approaches this topic on three levels.  The foundation is to put in place policies, processes and infrastructure to support, maintain and respond to quality issues for released instances of the system.  The second level is to drive the evolution of the system's capabilities and characteristics based on evolving needs and enabling technologies.  The third level is to proactively "disrupt" the market by reframing the opportunity and reinventing they system based on internal innovation, or responding to external disruptions in the marketplace or the technology space.

Dynamic pricing is defined as the buying and selling of goods and services in free markets where the prices fluctuate in response to supply and demand and changing. This course illustrates the difference between static and dynamic pricing, and covers various dynamic pricing models and methodologies for successful pricing. This course also illustrates the fact that effective pricing optimization is based on modeling of demand and elasticity of demand at a very granular level. It will explore various dynamic pricing models and explore and identify factors relevant in choosing dynamic pricing models that best support the operational effectiveness, external environment and business strategy of a particular firm.

The course introduces students to system dynamics models of business policy analysis and forecasting of associated management problems of complex systems and enterprise. The course covers advanced techniques of policy and strategy development applications: system thinking and modeling dynamics of growth and stability, including interaction of human factors with the technology. The tools of increasing power and complexity are offered for student’s business and management applications: causal feedback diagrams, technology process graphs, information processing flowcharts, decision scenarios. Students will get hands-on training in systems modeling by STELLA and AnyLogic software languages and perform their own case studies of real system of technology and/or business development. Prerequisite: Course in statistics.

Trusted systems are dependable, safe, and secure.  None of this happens by accident – it all must be engineered in.  The course goes beyond the traditional software engineering, quality and development courses to focus on the theory and practical techniques required to create trusted systems, be they software-only systems or systems that contain software.  The course covers software reliability engineering, software security engineering, control systems concepts, hazard analysis and management, trusted architecture patterns, fault and failure tolerance and management.  Specific techniques such as analysis of attack patterns, degraded operation, simplex architectures and rejuvenations, operational profiles, and Bayesian Networks, are studied in depth to understand their usefulness and contribution to an overall trusted system solution.  Case studies (e.g.  Mars Rover) and team projects are used throughout.

International graduate students may arrange an educationally relevant internship or paying position off campus and receive Curricular Practical Training (CPT) credit via this course.  Students must maintain their full time status while receiving CPT. Prior approval of the program director is required for enrollment.  To justify enrollment, the student must have a concrete commitment from a specific employer for a specific project, and must provide to the program director for his/her approval a description of the project plus a statement from the employer that he/she intends to employ the student.  This information must be provided to the program director with sufficient advance notice so that the program director has time to review the materials and determine if the project is appropriate. The project must be educationally relevant; i.e., it must help the student develop skills consistent with the goals of the educational program.  During the semester, the student must submit written progress reports.  At the end of the semester, the student must submit for grading a written report that describes his/her activities during that semester, even if the activity remains ongoing.  The student must also present his/her activities in an accompanying oral presentation that is also graded.  This is a one-credit course that may be repeated up to a total of three credits.

Research philosophy, ethics, and methodology will be discussed. Each student will, under the guidance of the instructor, formulate a problem, search the literature, and develop a research design. In addition, the student will examine and criticize research reports with specialemphasis on the statement of the problem, the sampling and measuring techniques that are used, and theanalyses and interpretation of the data. Emphasis is on applying research methodology to real-world organizational problems.

The discipline of Systems Engineering (SE) provides us with necessary engineering and management guidance to successfully design and develop a system rather than focus on its separate individual components. However, due to the rapidly increasing complexity of today’s dynamic environment, we are faced with the need to engineer multiple integrated complex systems. In response to this emerging paradigm shift, a new discipline of System of Systems Engineering (SoSE) has evolved.  This course serves as an overview of the advances in SoSE and provides the students the capability to apply this knowledge in the synthesis, analysis, and evaluation of activities during the lifecycle of a System of Systems (SoS) through case study analysis.

This data driven course focuses on the subjects of both traditional and modern data analysis and mining techniques. The course emphasizes the analysis of business and engineering data using a combination of theoretical techniques and commercially available software to solve problems. Topics such as data analysis and presentation, linear and nonlinear regression, analysis of variance, factor analysis, cluster analysis, neural networks, and classification trees will be presented. The course will make extensive use of the Splus software packages. However, students will be encouraged to use a wide variety of industry standard data analysis and mining tools including SPSS, SAS, MATLAB, and BrainMaker.

This course presents the fundamentals of complex systems architecting using the Object Modeling Group’s (OMG) SysML. It addresses the differences between functional decomposition and object oriented decomposition while architecting complex systems. Emphasis is placed on modeling mission objectives to the definition of the system level architecture. Topics include identification of system level architecture alternatives and considerations, including definition of objectives for physical (hardware) and logical (software) structure, information and system assurance, behavior, cost, performance and human integration based on the system concept at every level of system decomposition. System of System (SoS) architecture is examined, addressing composition of multiple systems and engineering new, emergent behavior in the SoS. Examples used will come from a variety of operational environments (e.g. communications systems, space systems, weapon systems, etc) Special consideration is given to the importance of effective construction and transitioning of the SysML models to software engineering for software intensive systems projects.

This course is the advanced study of analytic techniques for rational decision making that addresses uncertainty, conflicting objectives, and risk attitudes. This course covers advanced techniques for modeling uncertainty; values and risk preference. The advanced techniques for modeling uncertainty include Bayesian networks and the various approaches for both representing joint probability distributions and computing posterior distributions given new evidence. The techniques for modeling preferences address various degrees of preferential dependence among objectives. Finally, the risk preference techniques address non-exponential risk preference and the associated computation of value of information. These techniques are valuable as part of the risk management process, conduct of systems engineering trade-offs, and managing systems engineering projects

Three credits for the degree of Master of Engineering (Systems Engineering). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master’s degree program. A department technical report is required as the final product for this course.

Three credits for the degree of Doctor of Philosophy. This course is typically conducted as a one-on-one one investigation of a topic of particular interest between a faculty member and a student and is often used to explore topical areas that can serve as a dissertation. A student may take up to two special problems courses in a Ph.D. degree program. A department technical report is required as the final product for this course. Students enrolled in the SDOE program should enroll in course number SDOE 801.

Selected topics from various areas within Systems Engineering. This course is typically taught to more than one student and often takes the form of a visiting professor’s course. Prerequisite: consent of instructor.

A minimum of six credit hours is required for the thesis. Hours and credits to be arranged. Students enrolled in the SDOE program should enroll in course number SDOE 900.

Original work, which may serve as the basis for the dissertation, required for the degree of Doctor of Philosophy. A minimum of 30 hours of SYS 960 research is required for the Ph.D. degree. Students enrolled in the SDOE program should enroll in course number SDOE 960.

EM 585 builds on EM 385 and gives the student a practical introduction to Systems Architecture and Design. Lectures will introduce the students to the motivation for System Architecture and Design, the different views on a System Architecture, as well as theory and best practices on behavioral definition, logical and physical partitioning, and interface definitions. Key aspects of system verification and validation will also be discussed. Tutorials will give the students practical experience using SySML and a commercial modeling tool to model system architectures. The students will apply the principles on a team project, designing and building a robot. Pre-requisite EM 385 or instructor approval. 

This course provides an introduction to Smart Cities at conceptual,           
technological and social technical levels.  Its aim is to develop an understanding of the concept of a city and the concomitant needs of the citizens and of the city management to make the city smarter and explore how this can be accomplished both from a social and technical level.  It also will relate Smart Cities to smart homes, neighborhoods and campuses.

 This course presents advanced techniques and analysis   designed to permit  managers to estimate and use cost information in decision   making.  Topics  include: historical overview of the management accounting   process, statistical  cost estimation, cost allocation, and uses of cost   information in evaluating  decisions about pricing, quality, manufacturing processes   (e.g., JIT, CIM),  investments in new technologies, investment centers, the   selection process for  capital investments, both tangible and intangible, and how   this process is  structured and constrained by the time value of money, the   source of funds,  market demand, and competitive position.

This course brings a strong modeling orientation to bear on the process of obtaining and utilizing resources to produce and deliver useful goods and services so as to meet the goals of the organization. Decision-oriented models such as linear programming, inventory control, and forecasting are discussed and then implemented utilizing spreadsheets and other commercial software. A review of the fundamentals of statistical analysis oriented toward business problems will also be conducted.

This project-based course exposes students to tools and methodologies useful for forming and managing an effective engineering design team in a bussiness environment.  Topics covered will include: personality profiles for creating teams with balanced diversity; computational tools for project coordination and management; real time electronic documentation as a critical design process variable; and methods for refining project requirements to ensure that the team addresses the right problem with the right solution.

This course provides a hands-on introduction to the modern techniques for visualizing data and leverages such techniques with the corresponding problem solving skills necessary to complement data visualization into specific strategic decision making.  The student will first learn to use the latest off the shelf software for data visualization.  In specific the student will learn  the following languages: R, D3, Google refine and Spot fire.

This course provides an hands-on introduction to the major techniques and solutions to discover knowledge in data and text.  Traditional data mining along with text mining and network analysis will be presented and will be used by the students via open source software, addressing information mining needs on both structured and unstructured data.

This course enables the Engineering Management student to acquire the knowledge and skills he/she will need to handle the variety and volume of information encountered in today’s workplace.  The course uses Python, which is rapidly becoming the language of choice for information handling and data analysis.  Students will work with both structured and semi-structured data.

This course will introduce and explore complexity and chaos, and investigate a broad range of examples from both natural (biological) and man-made systems.  Complex systems, distinct from, but related to, complicated systems, display a range of behaviors.  One of the most distinguishing of these behaviors is emergence - behavior that is not predictable from knowledge of specific, individual agent capabilities.  In addition, systems are said to be complex when emergent behavior is not resulting from a central control, when they display chaotic characteristics, and when the systems have more frequent occurrences of rare events, especially when initial conditions vary.  This course will study and model complex and adaptive (as well as non-adaptive) systems, and will present and discuss the characteristics of complex and chaotic systems.

Principles and techniques of total quality management (TQM) with emphasis on their application to technical organizations. Topics include management philosophy, concepts and critique of quality "Gurus"; TQM modeling and strategy; TQM tools and techniques; Dept. of Defense 5000.51-G TQM guides; review and critique of the Deming and Baldrige Awards; concurrent engineering; quality function, deployment and design for cost. Students will form teams to analyze a case study involving TQM concepts and techniques (Formerly EM750)

Transportation systems are the backbone of cities, communities, and the        economy.  This course aims at teaching students how to use transportation technologies and management strategies to develop effective policies and to achieve sustainable transportation systems.  Throughout the course various quantitative decision making methods and tools including decision trees, benefit-cost and cost-effective analyses, and more advanced decision and risk analysis methods including sensitivity analysis, and multi-attribute simulations will be examined.  Transportation case studies will be assessed and analyzed using these techniques and tools.  By the end of the course students should be able to quantitatively assess transportation systems and their implications on environment, energy issues, land-use, economic development and equity as well as stakeholders’ roles and responsibilities to make holistic decisions and policy choices.  Integration of risk and uncertainty into formal methods is a fundamental component of this course, which tells us how confident we should be in our analyses while formulating/revising policies.

This course illustrates the theory and practice of designing and analyzing supply chains. It provides tool sets to identify key drivers of supply chain performance such as inventory, transportation, information and facilities. Recognizing the interactions between the supply and demand components, the course provides a methodology for implementing integrated supply chains, enabling a framework to leverage these dynamics for effective product/process design and enterprise operations.

This course addresses the design of the peopled-system   that is responsible for  designing and testing a product or operational system.   There are three keys  to designing the development system that are emphasized as   part of this  course: the fact that the design process should be a   discovery process, the  critical feedback and control activities that must be   implemented for  cost-discovery process, the critical feedback and control   activities that must  be implemented for cost-effective success, and the design   of risk  management(with an emphasis on adaptive testing)   activities.  This course will  focus on the functional processes that must be performed   by the development  system, but will also address physical resources(people   and software) and  associated organizational structures.

 Selected topics from various areas within Engineering Management.

Three credits for the degree of Master of Engineering (Engineering Management). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems course in a master's degree program. A department technical report is required as the final product for this course.

Three credit for the degree of Doctor of Philosophy. This course is typically conducted as a one-on-one investigation of a topic of particular interest between a faculty member and a student and is often used to explore topical areas that can serve as a dissertation. A student may take up to two special problems course in a Ph.D. degree program. A department technical report is required as the final product for this course

Selected topics from various areas within Engineering Management. This course is typically taught to more than one student and often takes the form of a visiting professor’s course. Prerequisite: consent of instructor.

For the degree of Master of Engineering (Engineering Management). A minimum of six credit hours is required. Hours and credit to be arranged.

Original work, which may serve as the basis for the dissertation, required for the degree of Doctor of Philosophy. A minimum of 30 hours of EM 960 research is required for the Ph.D. degree. Hours and credits to be arranged.

Aspects of entrepreneurship and business most relevant for technical people  and the practice of Technogenesis.  Investigates business-related  considerations in successfully commercializing new technology.  Exposes  technologists to five critical aspects of creating a successful new venture  and/or a successful product or service business within an existing enterprise:  (1) market and customer analysis, (2) beating the competition, (3) planning  and managing for profitability, (4) high-tech marketing and sales, and (5)  business partnerships and acquisitions.  Students should take this course if  they: (1) desire to maximize their effectiveness as technologists by  understanding the business and customer considerations that impact the work of  technologists, (2) intend to lead or participate in a technology-based new  venture/start-up, or (3) contemplate an eventual transition from a technical  to a business management career.  It is intended for either advanced  undergraduate (junior or senior) or graduate students in engineering or  science curricula.

A participating seminar on topics of current interest and importance in  Technogenesis.

The course deals with the management of software projects using objective metrics that help developers and managers to understand the scope of the work to be accomplished, the risks that will occur, the tasks to be performed, the resources and effort to be expended, and the schedule to be observed.  It provides the student with a thorough introduction to facility with, and understanding of such industry-standard software sizing metrics as Function, Feature, and Object Points and their relationship to the lines-of-code metric.  It provides the student with a thorough introduction to and understanding of such industry-standard software estimation tools such as COCOMO II used in cost estimation.

This course introduces the subject of software engineering, also known as software development process or software development best practice from a quantitative, i.e., analytic- and metrics-based point of view. Topics include introductions to: software life-cycle process models from the heaviest weight, used on very large projects, to the lightest weight, e.g., extreme programming; industry-standard software engineering tools; teamwork; project planning and management; object-oriented analysis and design. The course is case history and project oriented.

This course teaches the fundamentals of software and software engineering for those who need to understand how software systems are developed, but are not expected to have direct responsibility for software development themselves.    Cross-listing:  SOC 541, SYS 541

In software problem areas that require exploratory development efforts, those with complex requirements and high levels of change, agile software development practices are highly effective when deployed in a collaborative, people-centered organizational culture.  This course examines agile methods, including Extreme Programming (XP), Scrum, Lean, Crystal, Dynamic Systems Development Method and Feature-Driven Development to understand how rapid realization of software occurs most effectively.  The ability of agile development teams to rapidly develop high quality, customer-valued software is examined and contrasted with teams following more traditional methodologies that emphasize planning and documentation.  Students will learn agile development principles and techniques covering the entire software development process from problem conception through development, testing and deployment, and will be able to effectively participate in and  manage agile software developments as a result of their successfully completing this course.  Case studies and software development projects are used throughout.Prerequisites:  Programming experience in an object-oriented language, preferably Java.

Software systems need to be free from security vulnerabilities, such as buffer overflow and stack smashing. Unfortunately, avoiding these weaknesses when programming in popular languages like C and C++ requires special discipline and attention to details not often stressed in introductory courses. This course teaches students to recognize security weaknesses and other vulnerabilities in existing software and to use techniques that avoid those vulnerabilities when developing new software. They practice using secure coding standards and disciplined development methods on industrial case studies and a course project. Prerequisite:  Programming experience in C or C++, or permission of the instructor.

This course provides an introduction to Smart Cities at conceptual,           
technological and social technical levels.  Its aim is to develop an understanding of the concept of a city and the concomitant needs of the citizens and of the city management to make the city smarter and explore how this can be accomplished both from a social and technical level.  It also will relate Smart Cities to smart homes, neighborhoods and campuses.

Requirements Acquisition is one of the least understood and hardest phases in the development of software products, especially because requirements are often unclear in the minds of many or most stakeholders. This course deals with the identification of stakeholders, the elicitation and verification of requirements from them, and translation into detailed requirements for a new or to-be-extended software product. It deals further with the analysis and modeling of requirements, the first steps in the direction of software design. The quality assurance aspects of the software requirements phase of the software development process is studied. Also an introduction to several formal methods for requirements specification is presented.

This course introduces students to the software design process and it's models; representations of design/architecture; software architectures and design plans; design methods; design state assessment; design quality assurance; and design verification.

This course introduces students to systematic testing of software systems, software verification, symbolic execution, software debugging, quality assurance, measurement and prediction of software reliability, project management, software maintenance, software reuse and reverse engineering.

Students will learn how to deal with issues impacting industrial software developments. A broad range of topics will be covered, emphasizing large project issues. Large software projects are those employing 50 or more software developers for three years or more. Throughout the course, emphasis will be placed on quantitative evaluation of alternatives. Specific examples and case histories from real projects in the telephone industry are provided. Students will learn how to create architectures for large systems based on the '4+1' model; how to use modern software connector technology; module decomposition; scaling of agile methods to large projects, the use of work flows to drive software process and database designs, test plans, and implementation; and configuration control and software manufacturing. The special issues of database conversion data consistency, database maintenance, and performance tuning will be addressed for large data bases. The physical environment of the computer systems, including multisite deployment, software releases, and special management report generation, are examined.

Trusted systems are dependable, safe, and secure.  None of this happens by accident – it all must be engineered in.  The course goes beyond the traditional software engineering, quality and development courses to focus on the theory and practical techniques required to create trusted systems. The course covers software reliability engineering, software security engineering, control systems concepts, hazard analysis and management, trusted architecture patterns, and software fault and failure tolerance and management. Specific techniques such as analysis of attack patterns, degraded operation, simplex architectures and rejuvenations, are studied in depth to understand their usefulness and contribution to an overall trusted system solution. Case studies (e.g. Mars Rover) and team projects (e.g. analyzing and reengineering a system to be trustworthy) are used throughout.

Selected topics from various areas within Software Engineering. This course is typically taught to more than one student and often takes the form of a visiting professor’s course. Prerequisite: consent of instructor.

This course teaches financial engineers how to write well-constructed, persuasive technical papers, and how to make oral presentations more effectively.  It uses practical examples, in-class assignments, and homework exercises.  This course reduces the anxiety that is frequently associated with technical writing and speaking.  It emphasizes the collaborative aspects of the technical writing and revision process.  It teaches the use of the LaTeX typesetting system for preparing technical manuscripts and presentations. In addition, the course teaches students how to present their work to both technical and not-technical audiences by creating cogent, striking, and well-designed figures and presentation slides.

This course is designed to teach students the nature and availability of the financial data available at Stevens. The focus of the course will be on equity, futures, FX, options, swaps, CDS’s, interest rate swaps etc. They will learn to how use a Bloomberg terminal. As part of the course the students will be certified in the 4 areas that Bloomberg offers certification. We will cover the Thomson–Reuters Tick history data and basics of using this data. The course also introduces basics of applied statistics. Bloomberg terminal access will be required for any student taking the course on the web.

The course provides an introduction to SQL databases and NoSQL databases as available to the Hanlon Financial Systems Lab. At the end of the course the students will be familiar with all the lab resources as well as a working knowledge on how to use them. The students will receive hands on instructions about setting up and working with databases. Most of the software will be introduced using case studies or demonstrations, followed by a lecture of related fundamental knowledge.  The course covers SQL (MySQL, WinSQL, PosgreSQL), NoSQL (IBM DB2, OneTick) and database managers Aqua. The course will cover accessing databases using API, SQLConnect and Access methods for DB2.

The course provides a practical introduction to SQL databases and Hadoop cluster systems as available in the Hanlon Financial Systems Lab. Students will receive hands on instruction about setting up and working with databases. Most of the software will be introduced using case studies or demonstrations, followed by a lecture of related fundamental knowledge.  The course covers SQL, NoSQL, and database management systems. The course will cover accessing databases using API.

This course is an introduction to programming with VBA - the Visual Basic for Applications language.  In particular, we will be using VBA within MS Excel, and time permitting, MS Access as well.  Excel is used everywhere in finance, and VBA allows practitioners to go beyond standard spreadsheet calculation and modeling.  Programming with VBA (and using macros) enhances the versatility and power of Excel.  The goal of this course is to teach our students Excel usage at a high level using VBA, for front office applications in financial institutions.  Financial and mathematical applications will be presented and studied throughout the course.

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance.

In this course the students will learn the basics of Matlab programming using financial data and applications. The language will be introduced using financial data and applications. This short course is intended for students with little or no experience with the software covering Matlab’s basic operations and features. In addition, the course works through several simple applications, to give the students the necessary knowledge on developing their own projects. Topics covered include iteration, functions, arrays, and Matlab graphics. Assignments are designed to build an appreciation for randomness, simulation, and the role of approximation.

In this course the students will learn the basics of SAS programming using financial data and applications. The course provides an introduction to programming, graphics, and data analysis using SAS Software. The course concentrates on fundamental components of SAS Software: data processing, managing SAS libraries, graphical and statistical procedures, creating, formatting and exporting reports. In addition, several advanced topics will be introduced: SAS SQL procedures and SAS Macro Language. The supporting applications illustrate financial data analysis with special emphasis on large data sets.

The course provides an introduction to programming, graphics, and financial data analysis using Mathematica. Students will learn programming in Mathematica Software, starting with elementary but quickly moving to advanced programming. They will learn it as an integrated quantitative methodology for analysis of markets, and optimal trading in stocks and options. The course is based on “hands-on” projects dealing with contemporary topics in financial mathematics and it complements theoretical courses of finance.

This course is a primer on Python (language syntax, data structures, basic data processing, Python functions, modules and classes). The remainder of the course covers open source Python tools relevant to solving financial programming problems. The lecture, supporting examples, and practical applications are intertwined. The content will be delivered in a fully equipped financial computing laboratory where the students are immersed in case studies of real life applications. There will be reading assignments of the corresponding chapters in the textbook and additional materials will be provided.

This course is designed to teach students how to configure and code using PHP Hypertext Processor. Students will also learn how to create dynamically generated web pages using PHP and how to connect to databases.

This course is a hands on C++ introduction for Financial applications. The course will teach the basics of C++ and will teach the student how to program for finance. Very little time will be spent on the philosophy and much more time on the actual programming. QT and Visual Studio will be used as IDE’s throughout the course. The course will be designed as a prerequisite for other advanced courses at Stevens.

In this course the students will learn the basics of CUDA programming using financial data and applications. They will learn how to use C++, Matlab and R to access the GPU in their computer and to use the Stevens GPU cluster. The course is designed for Nvidia CUDA but the basics are easily transferable to Open CL.

This course introduces a range of topics that the current scope of financial engineering encompasses. Topics include basic terminology and definitions, markets, instruments, positions, conventions, cash flow engineering, simple derivatives, mechanics of options, derivatives engineering, arbitrage-free theorem, efficient market hypothesis, introductory pricing tools, and volatility engineering.

This course deals with risk management concepts in financial systems. Topics include identifying sources of risk in financial systems, classification of events, probability of undesirable events, risk and uncertainty, risk in games and  gambling, risk and insurance, hedging and the use of derivatives, the use of Bayesian analysis to process incomplete information, portfolio beta and diversification, active management of risk/return profile of financial enterprises, propagation of risk, and risk metrics.

Topics include discrete and continuous distributions, multivariate probability, transformations, pattern appearance, moment generating functions, Laws of large numbers, Markov chains and diffusion processes, prices in markets as random variables and processes, filtrations and information. Applications target financial engineering examples.

The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc. Prerequisites: Any undergraduate probability class such as MA222 or equivalent. 

In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic  models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR. 

This course introduces the stochastic calculus to students of finance and financial engineering. The course deals with Markov chains, Poisson processes, random walks, Brownian motion, asset prices as processes, limits of stochastic sequences, Ito sums and integral, fundamental models in modern finance, price dynamics and elementary examples of stochastic differential equations.

This course covers the design and implementation of financial models using object oriented programming.  It discusses advanced applications on quantitative finance with special emphasis on derivatives pricing.  Prerequisite: C++ refresher or equivalent

Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 & CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs.

Building effective and efficient tools for next generation integration of data analysis into strategic decision-making requires knowledge of existing software packages as well as the ability to build or extend software when needed.  This course will address strategies for representing complex data through coverage of responsive web technologies, programming methods, libraries, and current techniques for transforming local and distributed data sets into meaningful visualizations using data acquisition and machine learning techniques.

The course offers an overview of modern financial markets for various securities: equities, FX, and fixed income, different types of traders, orders, and market structures, market microstructure models used for describing price formation in dealer markets (inventory models and information-based models), models of the limit-order markets, optimal order execution: optimal order slicing, and maker-versus-taker strategies.  The course introduces several typical trading strategies by introducing technical analysis, including trend, momentum, and oscillator-based strategies, arbitrage trading strategies, including pair trading, implementation and methods of strategies back-testing.

Hedge funds are among the most influential participants in the financial markets with unique features. They are subject to less regulation and their strategies vary significantly from one another. Examples of common strategies include discretionary investing, quantitative equity investing, global macro, managed futures and exploitation of arbitrage opportunities. This course provides an overview of the hedge fund industry by going into the mechanics of the industry and then follows with detailed descriptions of the different strategies employed. Furthermore, it provides the economic intuition behind each of these strategies as well as the implementation along with practical considerations. It also discusses various examples of how financial engineering toolsets can be utilized to enhance hedge fund performance. In addition, this course emphasizes on the practical techniques of building a quantitative trading system using R programming language. Topics such as the lifecycle of developing a sound trading strategy and strategy validation in the form of back-testing are introduced.

The course will apply certain concepts from statistical physics to the description of real-life financial time series. It will introduce the notion of Random Walk from the physicist stand-point and propose various statistical tests as comparisons of real-life financial time series properties with those of a Random Walk. The course will introduce statistical description of financial data with emphasis on long-memory correlation functions. The course will introduce Levy stochastic processes and their analytical properties and use them to parameterize the real-life financial time series probability density functions. Through homework’s and final project, the course will stress phenomenological hands-on work with financial data. The course will culminate with the final project in which students will learn to extract the learned price anomalies through development of basic trading strategies. The dangers of over fitting of financial data will be studied through walk-forward out-of-sample trading simulations, which will teach student to become more prudent practical quantitative analysis.

This course provides a theoretical and practical analysis of the asset-backed security market.  Topics include: Duration And Convexity of Bond Yields, Price Dynamics of Mortgages and Cash Flows, Default Risk, Interest Rate Volatility, Interest Rate Risk Management of Mortgage-Backed Securities, Securitization, Corporate Debt and The Securitization Markets, Asset-Backed Commercial Paper, Collateralized Loan Obligations, Structuring Synthetic Collateralized Loan Obligations, Securitization of Revolving Credit, Financial Derivatives and Their Use as Hedging Tools.  Half of the course is in the Hanlon Financial Systems Lab, where theoretical models are illustrated with real scenarios.

This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, data flows, and data encryption. Topics will include data abstractions and integration, enterprise level data issues, data management issues with collection, warehousing, preprocessing and querying. Furthermore, the Hadoop based programming framework for big data issues will be introduced along with any governance and policy issues.
These concepts will be applied to areas such as digital marketing and computational advertising, energy and healthcare analytics, social media and social networks, and capital markets financial data.
A one credit Hanlon lab course, FE 513: Practical Aspects of Database Design will be attached to this course in order to facilitate learning of the practical side of data management.  

Introduction to information theory: the thermodynamic approach of Shannon and Brillouin. Data conditioning, model dissection, extrapolation, and other issues in building industrial strength data-driven models. Pattern recognition-based modeling and data mining: theory and algorithmic structure of clustering, classification, feature extraction, Radial Basis Functions, and other data mining techniques. Non-linear data-driven model building through pattern identification and knowledge extraction. Adaptive learning systems and genetic algorithms. Case studies emphasizing financial applications: handling financial, economic, market, and demographic data; and time series analysis and leading indicator identification.

This course deals with financial technology underlying activities of markets, institutions and participants. The overriding purpose is to develop end-to-end business decision making data analytics tools along with enterprise level systems thinking. Statistical learning algorithms will be connected to financial objects identification and authentication along with the appropriate databases to create enterprise level financial services analytics systems.

This course provides the mathematical foundation for understanding modern financial theory. It includes topics such as basic probability, random variables, discrete continous distributions, random processes, Brownian motion, and an introduction to Ito's calculus. Applications to financial instruments are discussed throughout the course.

This course deals with basic financial derivatives theory, arbitrage, hedging, and risk. The theory discusses Ito’s lemma , the diffusion equation and parabolic partial differential equations, and the Black-Scholes model and formulae. The course includes applications of asset price random walks, the log-normal distribution, and estimating volatility from historic data. Numerical techniques, such as finite difference and binomial methods, are used to value options for practical examples. Financial information and software packages available on the Internet are used for modeling and analysis.

This course provides computational tools used in industry by the modern financial analyst. The current financial models and algorithms are further studied and numerically analyzed using regression and time series analysis, decision methods, and simulation techniques. The results are applied to forecasting involving asset pricing, hedging, portfolio and risk assessment, some portfolio and risk management models, investment strategies, and other relevant financial problems. Emphasis will be placed on using modern software.

 This course covers the basics of Emerging Markets instruments, models, risks, hedging and trading practices. Emerging Markets have seen a dramatic increase in volume, especially since the latest crisis in the developed markets. Geographically the course will be focused on the 4 BRIC countries (Brazil, Russia, India and China) and Mexico. The student should develop a deep understanding of the main differences between Developed Markets and Emerging Markets risk and trading. Many of the unique attributes and models in Emerging Markets have now been adopted by Developed Markets since the 2008 crisis, given students an edge in understanding the latest trends in the markets. Main topics to be covered include: funding in EM; XC basis markets; OIS and local collateralization; Credit Valuation Adjustment (CVA); Extinguishable XC swaps; Inflation indexes and inflation currencies; Capital Constraints, Convertibility and Transferability.Prerequisites:  Probability, Statistics, and Calculus

This course introduces the modern portfolio theory and optimal portfolio selection using optimization techniques such as linear programming. Topics include contingent investment decisions, deferral options, combination options and mergers and acquisitions. The course introduces various concepts
of financial risk measures.    

This course deals with risk assessment and engineering in financial systems. It covers credit risk, market risk, operational risk, liquidity risk, and model risk. Topics include classical measures of risk such as VaR, methods for monitoring volatilities and correlations, copulas, credit derivatives, the calculation of economic capital, and risk-adjusted return on capital (RAROC). The nature of bank regulation and the Basel II capital requirements for banks are examined. Case studies illustrate risk engineering successes and failures in financial enterprises.

This course deals with aspects of systemic risk in financial systems. It covers a review of classical risk measures and introduces non-classical risk measures such as Extreme Value Theory. It also covers the study of financial systems as a system of complex adaptive systems, agent-based modeling, history and analysis of bubble formations as a systemic risk, the role of rating agencies, the financial systems ecosystem, risk and regulatory environment, risk and the socio-political environment. It also studies international financial inter-system risk propagation and containment and its impact on international financial systems, the International Monetary Fund assessments and the effect of extreme risk on poverty, international instability and globalization.
Also FE 699 and FE 700 were renumbered to FE 800 and 900 to be consitent with the rest of the school

This course investigates statistical methods implemented in multiple quantitative trading strategies with emphasis on automated trading and based on combined technical-analytic and fundamental indicators to enhance the trade-decision making mechanism.  Topics explore high-frequency finance, markets and data, time series, microscopic operators, and micro-patterns. Methodologies include, but not limited to, Bayesian classifiers, weak classifiers, boosting and general meta-algorithmic emerging methods of machine learning applied to trading strategies.  Back-testing and assessment of model risk are explored. 

This course deals with fixed-income securities and interest-rate sensitive instruments. Topics include term structure of interest rates, treasury securities, strips, swaps, swaptions, one-factor, two-factor interest rate models, Heath-Jarrow-Merton (HJM) models and credit derivatives: credit default swaps (CDS), collateralized debt obligations (CDOs), and Mortgage-backed securities (MGS).

A student is given a particular problem in financial engineering to be completed in one semester. The nature of the problem may be computational or theoretical depending on the student’s track. It is encouraged that the problems be related and, in some instances, posed by the financial engineering industry.

This is the thesis option equivalent to one elective and FE 699. The thesis option requires the approval of the advisor and is recommended only for full-time students. The student will produce a Master’s thesis in financial engineering.

Topics include Ito calculus review, linear stochastic differential equations (SDE's), examples of solvable SDE's, weak and strong solutions, existence and uniqueness of strong solutions, Ito-Taylor expansions, SDE for Markov processes with jumps, Levy processes, forward and backward equtions and the Feynman-Kac representation formula, and introduction to stochastic control. Applications are mostly from fianncial engineering but applications in areas such as population dynamics, energy, climatology and seismology may also be presented.                                                                    

In this course students will understand the implied volatility, and the empirical static and dynamic behavior of the volatility surface formed using option prices for all strikes and expirations. The students will also examine the volatility risk, stochastic volatility and local volatility models, numerical methods for volatility surface calibration, Monte Carlo simulation of stochastic volatility models, and pricing options through fast Fourier transform. Topics include: the Black-Scholes implied volatility, empirical statics and dynamics of the volatility surface, volatility risk premium,  stochastic volatility models (Heston, Hull-White, Stein-Stein, SABR, Bates, Scott, etc), Dupire’s local volatility model, Heston-Nandi GARCH model, arbitrage-free properties of the volatility surface, volatility surface parametrization and calibration, simulation of the Heston model, stochastic volatility model with jumps, option pricing based on fast Fourier transforms, and volatility derivatives (Variance swap, CBOE VIX futures and options, etc). Other advanced current research topics will be introduced as well. The students are required to have a solid working knowledge of stochastic calculus, and FE610 is a pre-requisite for this course. The course uses statistical softwares such as Matlab or R throughout. A companion one credit of a relevant lab course is recommended if this knowledge is not acquired before.

Three credits for the degree of Master of Science (Financial Engineering). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master’s degree program. A department technical report is required as the final product for this course. Prerequisite: consent of instructor. 

Selected topics from various areas within Financial Engineering. This course is typically taught to more than one student and often takes the form of a visiting professor’s course. Prerequisite: consent of instructor.

For the degree of Master of Science (Financial Engineering). A minimum of six credit hours is required for the thesis. Hours and credits to be arranged.  

Parametric modeling links mathematical modeling with spatial modeling.  The course provides a practical introduction to the concepts and application of parametric modeling, using examples from the built environment to apply those techniques at a number of scales ranging from a city-scale urban grid to the individual building elements within it.  The course will be structured around the practical, hands-on application of the techniques addressed, using Grasshopper 3d as the primary parametric programming platform.  The history, theory, and broader applications of computational parametric modeling and design will be periodically addressed where needed and more in-depth reading swill be provided as supplementary texts.

Traditional systems engineering techniques must be adapted to understand a broader class of human designed systems that we refer to as an enterprise, of which a technical system is only one part.   Students will learn how describe the value of systems engineering on complex projects, provide a (common) global view of the system and enterprise, elicit and write good requirements, and understand how to develop robust and efficient architectures. Students should complete this class with “next steps” knowledge of tools, templates, capability patterns, and community.  Case studies and examples are used throughout to give students an appreciation of how systems engineering tools, techniques, and thinking can be applied to the real world enterprises that we encounter daily.

Cities, even large global mega-cities, can best be understood as a collection of communities in neighborhoods, all served by common urban infrastructures.  To many citizens, those infrastructures were invisible until they failed in times of crisis.  Their lack of resilience brought home the point that community needs drive the functional requirements for buildings and infrastructure.  Delivered as a combination of lectures and seminars, we can begin to address questions such as "How do we measure functionality at the many scales that affect community?" and "How are their benefits and costs delivered to each significantly different community within the city?"  Finally, "How do we measure the resilience we seek to achieve at a community level?"

For a variety of business reasons, today’s business and government organizations are demonstrating a heightened interest in governance. Development programs and organizations have unique governance concerns due to inherent uncertainty of development efforts. Moving beyond platitudes, this course introduces modern concepts of organizational governance and their application to organizations that develop systems and products. Course topics include the business climate forcing an emphasis on governance; a general governance framework, including definitions of governance elements; governance as a process; governance solutions for the development teams; development governance styles; and advanced topics.

Real-time responsiveness characterizes systems at the forefront of competition, enterprise, strategy, warfare, governance, innovation, engineering, development, information, integration, and virtually anything designed today for purpose. This course covers fundamental objectives, performance metrics, analysis frameworks, and design principles for engineering agile and resilient systems. Real examples are analyzed in case studies for their change proficiency and response ability. Response capability frameworks are applied in analysis and requirements development. Architecture and design principles which enable resilient and innovative response are illuminated and then applied in synthesis exercises. Hands-on, minds-on exercises prepare and guide the participant in applying the knowledge. Systems for case study and focus can run the range from products and processes to governance and infrastructure to enterprises and systems-of-systems.

This course presents a systems architecting process to achieve enterprise integration both within and between corporate boundaries. The process leverages systems thinking - the antithesis of scientific reductionism, which fails to appreciate the interrelationships between components that make-up a system. Systems thinking has proven to be successful in the delivery of integrated technology products, and is now being applied to understanding the structure and dynamics of organizations for which communications and co-stuff in general is a key to business success; in other words inter-relationships are prime in managing an enterprise. The systems approach further emphasizes emergence, wider systems and the environment. These concepts are crucial to architecting an enterprise in consideration of issues of decentralization, alliance advantage, and market phenomena.

The frontier of systems engineering today seeks new levels of system capability and behavior, and expects to find that benefit in higher forms of systems that elude traditional control and creation concepts. Common themes converge here in a study of agility across a seemingly wide variety of interesting system types, characterized principally by aspects of self-organization and systems of systems. Esthetic quality in systems and enterprises makes the difference between enforced compliance and embraced experience; and determines the positive or negative vectors of self-organization and emergence. This module explores the value and nature of esthetic design quality, principles and architectures for harnessing self organized systems of systems, agility as risk management and reality confrontation, and similar issues at the edge of agile system and enterprise knowledge. (Formerly SYS790)

It takes something special for the term system to have such ubiquity. The downside is that it is overused, improperly so, detracting from its power. This class builds upon a solid conceptual foundation to ensure that the system/enterprise is properly defined, conceived, and realized. Uniquely, the class shows how it is possible to use systems in order to think more deeply and to act more decisively. This approach is made possible by emphasizing the simultaneity of perspectives, the role of paradox, and the centrality of soft issues in resolving complexity. The SystemitoolTM is used to structure and conduct analysis of decisions. This class is aimed at policy and decision-makers at all levels in an organization.

Selected topics from various areas within Enterprise  Systems. This course is typically taught to more than one student and often takes the form of a visiting professors course.

Building on the topics presented in ES 690, this course introduces advanced topics in infrastructure systems, focusing on tools and methodologies crucial to infrastructure systems analysis and planning. Topics discussed include CLIOS analysis and dynamic modeling of infrastructure systems, fundamentals of network analysis, decision analysis for infrastructure systems, and infrastructure resiliency.

Three credits for the degree of Master of Science (Enterprise Systems). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master’s degree program. A department technical report is required as the final product for this course.

Three credits for the degree of Doctor of Philosophy. This course is typically conducted as a one-on-one one investigation of a topic of particular interest between a faculty member and a student and is often used to explore topical areas that can serve as a dissertation. A student may take up to two special problems courses in a Ph.D. degree program. A department technical report is required as the final product for this course.

Selected topics from various areas within Enterprise Systems. This course is typically taught to more than one student and often takes the form of a visiting professor’s course.

For the degree of Master of Science (Engineering Systems). A minimum of six credit hours is required for the thesis.

Original work, which may serve as the basis for the dissertation, required for the degree of Doctor of Philosophy. A minimum of 30 hours of ES 960 research is required for the Ph.D. degree.

Information systems contain a variety of risks and organizations establish control mechanisms to mitigate the risks.  This course focuses on how information systems controls can be designed, monitored, and tested.  Control standards will be examined as well as industry-specific regulations that require information systems auditing.  Students will learn how their organizations measure, mitigate, and monitor risks related to the information systems they rely on.

The management of risks in information systems (IS) requires robust Enterprise Risk Management programs and practices to protect critical business processes and ensure continuous operation and service delivery.  This course focuses on how information systems managers establish comprehensive IS controls architectures that manage enterprise risks in systems development, acquisition, service management, resiliency, information asset protection, regulatory compliance, and technical architecture.  Enterprise audit and control programs will be analyzed.  Students will learn how the management of their organizations manage the risks in their business information systems.

This course focuses on the analysis and management of information security architectures. Information security architectures consist of organizational, process, and technology (e.g., data, applications, network, systems) domains. The integration and effective management of such architectures is essential to effectively responding to technical risk dynamics. The course will focus on evaluating the architectural domains and their integration. The course will rely on management research on information security, risk, IT strategic planning, and distributed computing. The student will learn the relationships between business requirements, technical requirements and technical risk, and make appropriate choices for risk mitigation. The course will provide insights on the continuous management of the information security function in organizations.

This course provides a working knowledge of risk analysis and management for enterprise security.   The emphasis is on modeling, analysis and economic evaluation of technology risks.   The students learn about business continuity and disaster recovery planning, security risks, tangible and intangible consequences of security failures, risk mitigation options and economic trade offs. The first part of the course covers the basics of risk identification, assessment, control and mitigation using a system framework. The second part covers application of decision theory and engineering economics to security options based on models that consider risk profile and uncertainty in enterprise security problems.  The learning is reinforced through case reviews and team projects.

SES 602 encompasses all aspects of systemic security issues. Systemic security components include infrastructure as well as information attributes, disruption profiles, identity management, security information management, and recovery alternatives. The course also addresses human and workforce components of security such as governance processes, technology management, and enterprise systems operations. SES 602 provides a solid background in systemic methods, tools, and procedures for value preservation in an environment of changing threats. It covers all concepts important in evaluating enterprise security design alternatives.

SES 603 extends the Secure Systems Foundations course, SES 602 by providing a hands-on environment to explore the concepts learned in SES 602. It includes exposure the methods, processes and tools that are commonly used to implement security features as well as those used by attackers to breach system security. Students will be divided into teams which alternately assume role and responsibilities of enterprise management, enterprise administration, enterprise operations, and enterprise adversary. Challenging lab scenarios will provide students with experience in executing the responsibilities associated with each role.

Presents principles and processes for designing secure systems, including how to approach stakeholder needs analysis, to distinguish between needs and solutions, and to translate security requirements into design specifications. Students will learn how the fundamental organization of a system contributes to or detracts from the engineer’s ability to provide secure design, and to recognize how security-related components compose a system of interest within any system. The course will provide an understanding of the difference between functional and nonfunctional requirements for security features as well as an understanding of how security requirements may be derived from unintended inputs and undesired outputs.

This course enhances the systems security knowledge base introduced in SES 622 with project experience in security design and architecture. It covers systems security considerations in functional analysis, decomposition, and requirements processes, and teaches practical heuristics for developing secure architectures. It demonstrates how to incorporate threat and vulnerability analysis into the architecture and design process. The students execute multiple phases of a project wherein a system security strategy is proposed, designed, architected, and supplemented with operational guidelines.