Master of Science in Engineering Management

Topics covered in this course include patents, trade secrets, trademarks, copyrights, product liability contracts, business entities, employment relations, and other legal matters important to engineers and scientists. Graduate-level requirements include an in-depth research paper on a current topic.

Principles of the engineering sales process in technology-oriented enterprises; selling strategy, needs analysis, proposals, technical communications, electronic media, time management, and ethics; practical application of concepts through study of real-world examples. Graduate-level requirements include a term paper on a course topic selected from a short list of topics, other graded components of the course and creation of a PowerPoint presentation to the class.

Application of principles of probability and statistics to the design and control of engineering systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis. Graduate-level requirements include a semester research project.

Processes and tools used to plan and control large scale projects. Topics include organizational design alternatives, formation and management of teams, construction and control of project schedules, risk assessment, and issues specific to global ventures and software development. 2ES, 1ED.

Financial modeling and simulation of new technology ventures. Topics include Pro Forma financial statements construction, time value of money, accounting, valuation, and technology ownership issues. Entrepreneurship issues related to forming a company will be discussed. This course is intended for graduate students in science or engineering with little or no prior background in engineering economics.

Quality, improvement, and control methods with applications in design, development, manufacturing, delivery, and service. Topics include modern quality management philosophies, engineering/statistical methods (including process control, control charts, process capability studies, loss functions, experimentation for improvement), and TQM topics (customer driven quality, teaming, Malcolm Baldridge, and ISO 9000). Graduate-level requirements include additional readings and assignments/projects.

It is concerned with determining the probability that a component or system, whether simple or complex, will function as intended. The scope of this course includes: (1) Root cause analysis of critical failures, (2) reliability models of components and systems, (3) development of statistical methods for estimating the reliability of a product, (4) use of software tools to perform model development and analysis, and (5) methodologies to influence system designs. Graduate-level requirements include a term project that focuses on real-world implementations of the course material and/or original theoretical developments in the form of a technical paper. Project topics (e.g., system reliability optimization, physics-based reliability models, warranty data analysis) must be approved by the instructor.

Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods, and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments.

Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Graduate-level requirements include a library research report.

This course provides fundamental analytical skills necessary to analyze data and make decisions using sports examples. These skills include critical thinking, statistical analysis, computer programming, and data visualization which are generally applicable to other areas of engineering and business.

This course will provide senior undergraduate and graduate students from diverse engineering disciplines with fundamental concepts, principles and tools to extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics and machine learning, along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamental topics of data science study, including: 1) principles of data processing and representation, 2) theoretical basis and advances in data science, 3) modeling and algorithms, and 4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency, and help students to understand and identify the circumstances under which the algorithms are most appropriate.

Planning and designing experiments with an emphasis on factorial layout. Includes analysis of experimental and observational data with multiple linear regression and analysis of variance.

Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. Graduate-level requirements include additional assigned readings and a project paper.

Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.

Model formulation and solution of problems on graphs and networks. Topics include heuristics and optimization algorithms on shortest paths, min-cost flow, matching, and traveling salesman problems.

An intensive study of continuous and discrete linear systems from the state-space viewpoint, including criteria for observability, controllability, and minimal realizations; and optionally, aspects of optimal control, state feedback, and observer theory.

The course will cover various modeling and simulation approaches used in studying traffic dynamics and control in a transportation network. The model-based simulation tools discussed include dynamic macroscopic and microscopic traffic flow simulation and assignment models. Models will be analyzed for their performance in handling traffic dynamics, route choice behavior, and network representation.

Quantitative models in the planning, analysis, and control of production systems. Topics include aggregate planning, multi-level production systems, inventory control, capacitated and uncapacitated lot-sizing, Just-in-time systems, and scheduling.

Plan and design of efficient logistics and distribution systems. Topics include: supply chain management, integration of production/inventory/location/transportation decisions, shipment scheduling with incomplete and uncertain information, vehicle routing and scheduling, goods distribution networks with multiple transshipment, terminals, and warehouses. Grading: Regular grades are awarded for this course: A B C D E.

Focuses on principles of cost estimation and measurement systems with specific emphasis on parametric models. Approaches from the fields of hardware, software and systems engineering are applied to a variety of contexts (risk assessment, judgment & decision making, performance measurement, process improvement, adoption of new tools in organizations, etc.). Material is divided into five major sections: cost estimation fundamentals, parametric model development and calibration, advanced engineering economic principles, measurement systems, and policy issues. The graduate-level requirements include a final paper.

Fundamentals of Supply Chain Management including inventory/logistics planning and management, warehouse operations, procurement, sourcing, contracts, and collaboration. Graduate-level requirements include an additional semester research paper.

This course will provide senior undergraduate and graduate students the conceptual, methodological, and scientific bases to quantify and improve the impact of engineering decisions on the environment, with a focus on applying life cycle analysis (LCA). The course will foster students to assess the environmental sustainability early on in their research to help design and develop more sustainable materials, products, and processes including manufacturing, logistics, and supply chain. Main topics covered include concept of life cycle thinking, computational structure of LCA, process based LCA, economic input-output LCA, LCA software tools and databases, case studies, recent development, and advanced topics in LCA. The students will be able to approach problems with life cycle perspectives, conduct LCA according to the ISO 14040 standards, and understand the strengths and weaknesses of LCA studies.

The purpose of this course is to introduce selected topics, issues, problems, and techniques in the area of System Cyber Security Engineering (SCSE), early in the development of a large system. Students will explore various techniques for eliminating security vulnerabilities, defining security specifications/plans, and incorporating countermeasures to achieve overall system assurance. SCSE is an element of system engineering that applies scientific and engineering principles to identify, evaluate, and contain or eliminate system vulnerabilities to known or postulated security threats in the operational environment. SCSE manages and balances system security risk across all protection domains spanning the entire system engineering life cycle. The fundamental elements of cyber security will be explored, including human cyber engineering techniques, penetration testing, mobile and wireless vulnerabilities, network mapping and security tools, embedded system security, reverse engineering, software assurance and secure coding, cryptography, vulnerability analysis, and cyber forensics. After a fundamental understanding of the various cyber threats and technologies are understood, the course will expand upon the basic principles, and demonstrate how to develop a threat/vulnerability assessment on a representative system using threat modeling techniques (i.e. modeling threats for a financial banking system, autonomous automobile, or a power distribution system). With a cyber resilience focus, students will learn how to identify critical use cases or critical mission threads for the system under investigation, and how to decompose and map those elements to various architectural elements of the system for further analysis. Supply chain risk management (SCRM) will be employed to enumerate potential cyber threats that could be introduced to the system either unintentionally or maliciously throughout the supply chain. The course culminates with the conduct of a realistic Red Team/Blue Team simulation to demonstrate and explore both the attack and defend perspectives of a cyber threat. The Red Team will perform a vulnerability assessment of the prospective system, with the intention of attacking its vulnerabilities. The Blue Team will perform a vulnerability of the same system with the intention of defending it against cyber threats. A comparison will be made between the outcomes of both teams to better understand the overarching solutions to addressing the threats identified. Upon completion of the course, students will be proficient with various elements of cyber security and how to identify system vulnerabilities early on in the system engineering lifecycle. They will be exposed to various tools and processes to identify and protect a system against those vulnerabilities, and how to develop program protection plans to defend against and prevent malicious attacks on large complex systems. Graduate students will be given an additional assignment to write a draft Program Protection Plan (PPP) for the system that the class performed the threat analysis for. Program protection planning employs a step-by-step analytical process to identify the critical technologies to be protected; analyze the threats; determine program vulnerabilities; assess the risks; and apply countermeasures. A PPP describes the analysis, decisions, and plan to mitigate risks to any advanced technology and mission-critical system functionality. May be convened with SIE 471.

This course engages students in diverse and varied national cybersecurity/information systems security problems, under an existing and very successful umbrella program called "INSuRE," that enables a collaboration across several universities, Cyber professionals, and cross-disciplined Cyber related technologies. Led by Purdue University, and made possible by a grant from the NSA and NSF, INSuRE has fielded a multi-institutional cybersecurity research course in which small groups of undergraduate and graduate students work to solve unclassified problems proposed by NSA, other US government agencies, and/or private organizations and laboratories. Students will learn how to apply research techniques, think clearly about these issues, formulate and analyze potential solutions, and communicate their results with sponsors and other participating universities. Working in small groups under the mentorship of technical experts from government and industry, each student will formulate, carry out, and present original research on current cybersecurity/information assurance problems of interest to the nation. This course will be run in a synchronized distance fashion, coordinating activities with other INSuRE technical clients and sponsors, along with partnering universities which are all National Centers of Academic Excellence in Cyber Defense Research (CAE-R), i.e., Purdue University, Carnegie Mellon University, University of California Davis, and several others.

The purpose of this course is to explore widely accepted security frameworks, industry standards, and techniques employed in engineering trustworthy secure and resilient systems. We will study and explore several National Institute of Standard and Technology (NIST) frameworks such as the Cyber Security Framework (CSF), the Risk Management Framework (RMF), and other standards. These widely adopted standards have been developed to ensure that the appropriate security principles, concepts, methods, and practices are applied during the system development life cycle (SDLC) to achieve stakeholder objectives for the protection of assets — across all forms of adversity characterized as disruptions, hazards, and threats. We will also explore case studies within the Department of Homeland Security’s (DHS) 16 Critical Infrastructure elements (shown in the figure below), to understand how government and private sector participants within the critical infrastructure community work together to manage risks and achieve security and resilient outcomes. Cyber resiliency is the ability to anticipate, withstand, recover from, and adapt to adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by cyber resources regardless of the source.

Driven by efforts to improve human health and healthcare systems, this course will cover relevant topics at the intersection of people, information, and technology. Specifically, we will survey the field of biomedical informatics that studies the effective uses of biomedical data, information, and knowledge from molecules and cellular processes to individuals and populations, for scientific inquiry, problem solving, and decision-making. We will explore foundations and methods from both biomedical and computing perspectives, including hands-on experiences with systems, tools, and technologies in the healthcare system. Graduate students will be required to submit an additional assignment or project.

The practice of modern medicine in a highly regulated, complex, sociotechnical enterprise is a testament to the future healthcare system where the balance between human intelligence and artificial expertise will be at stake. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in medicine. We will explore foundational methods in artificial intelligence (AI) with greater emphasis on machine learning and knowledge representation and reasoning, and apply them to specific areas in medicine and healthcare including, but not limited to, clinical risk stratification, phenotype and biomarker discovery, time series analysis of physiological data, disease progression modeling, and patient outcome prediction. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, clinical/healthcare applications, and relevant tools.

This course is designed to provide a flexible topics course across several domains in the field of Systems Engineering, Industrial Engineering, and Engineering Management. Students will develop and exchange scholarly information in a small group setting. Selected advanced topics in Systems and Industrial Engineering and Operations Research, such as:

• optimization stochastic systems
• systems engineering and design
• human cognition systems
• informatics

Advanced techniques for statistical quality assurance, including multivariate statistical inference, multiple regression, multivariate control charting, principal components analysis, factor analysis, multivariate statistical analysis for process fault diagnosis, and select papers from the recent literature.

This is a three-credit course for well-qualified graduate students who have taken graduate-level statistics courses. The course provides a comprehensive introduction to the statistical principles and methods for reliability data analysis. This course will cover parametric, nonparametric, and semiparametric methods for modeling degradation data and failure time data with different types of censoring.

Emphasis on current research problems including simulation based control, distributed federation of simulations, and multi-paradigm (system dynamics, discrete event based, agent-based) simulations. Course Requisites: SIE 431 or MIS 521.

Decomposition-coordination algorithms for large-scale mathematical programming. Methods include generalized Benders decomposition, resource and price directive methods, subgradient optimization, and descent methods of nondifferentiable optimization. Application of these methods to stochastic programming will be emphasized. Course Requisites: SIE 544 or SIE 545.

Modeling and solving problems where the decisions form a discrete set. Topics include model development, branch and bound methods, cutting plane methods, relaxations, computational complexity, and solving well-structured problems. Course Requisites: SIE 544.

This course is devoted to structure and properties of practical algorithms for unconstrained and constrained nonlinear optimization. Course Requisites: SIE 544 or SIE 545.

Convexity, optimality conditions, duality, and topics related to the instructor’s research interests; e.g., stochastic programming, nonsmooth optimization, interior point methods.

Modeling and design of complex systems using the Unified Modeling Language (UML), the Systems Modeling Language (SysML), and Wymorian System Theory. Applications come from systems, hardware and algorithm design. Course will emphasize architecture, requirements, testing, risk analysis and use of various systems design tools. Course Requisites: SIE 554A.

Special topics in the analysis and design of transportation systems, including advanced traffic management, network routing, dynamic traffic estimation and assignment, network design, intermodal distribution and transportation, and intelligent transportation systems. Course Requisites: SIE 305, SIE 321; SIE 540 or SIE 544; some knowledge of network modeling.