SIE 500A: Introduction to SIE Methods: Probability and Statistics
Axioms of probability, discrete and continuous distributions, and sampling distributions. Applications of statistical estimation, hypothesis testing, confidence intervals.
SIE 500B: Introduction to SIE Methods: Stochastic Processes
Introduction to probabilistic models commonly used in systems and industrial engineering and related disciplines. Markov chains, Poisson processes, queuing models. Special course fee required.
SIE 500C: Introduction to SIE Methods: Linear Programming
Linear programming models, solution techniques, and duality.
SIE 506: Quality Engineering
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 Baldrige and ISO 9000). Grading: Regular grades are awarded for this course: A B C D E. May be convened with SIE 406.
SIE 508: Reliability Engineering
Determine the probability that a component or system, whether simple or complex, will function as intended. Scope includes Root cause analysis of critical failures, reliability models of components and systems, development of statistical methods for estimating the reliability of a product. May be convened with SIE 408.
SIE 511: Human-Machine Interaction
Students who take this course will get familiar with the basic concepts, methods, principles and skills in designing and evaluating various human-machine interfaces. Machine here is generally defined as any physical systems that can be operated by human operators. This course is composed of a systematic introduction of major principles and methods in human-machine interaction, including: 1) Fundamental concepts and principles of human-machine interaction; 2) User interface design, prototyping and interface analysis methods; 3) Quantitative and qualitative user modeling and interface evaluation methods; 4) Special topics in HMI: ecological and adaptive human-machine interface, speech and handwriting UIs in HMI, engineering aesthetics in HMI, as well as human-machine interaction in transportation.
SIE 512: Human Factors Engineering Research Methods
Students who take this course will become familiar with the state-o-the-art research methods in human factors engineering, including study design, research hypotheses generation, literature search and management in human factors, experimental design and human behavior data analysis in human factors, various human behavior measurement methods, and writing conference and journal papers in human factors.
SIE 514: Law for Engineers and Scientists
Topics covered in this course include patents, trade secrets, trademarks, copyrights, product liability contracts, business entities, employment relations and other legal matters important to engineers and scientists. Graduate-level requirements include an in-depth research paper on a current topic.
SIE 515: Technical Sales and Marketing
Principles of the engineering sales process in technology-oriented enterprises; selling strategy, needs analysis, proposals, technical communications, electronic media, time management and ethics; practical application of concepts through study of real-world examples. Graduate-level requirements include a term paper on a course topic selected from a short list of topics, other graded components of the course and creation of a PowerPoint presentation to the class.
SIE 520: Stochastic Modeling I
Modeling of stochastic processes from an applied viewpoint. Markov chains in discrete and continuous time, renewal theory, applications to engineering processes.
SIE 522: Engineering Decision-Making Under Uncertainty
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. May be convened with SIE 422.
SIE 525: Queuing Theory
Application of the theory of stochastic processes to queuing phenomena; introduction to semi-Markov processes; steady-state analysis of birth-death, Markovian, and general single- and multiple channel queuing systems.
SIE 530: Engineering Statistics
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. Grading: Graduate-level requirements include additionally more difficult homework assignments. May be convened with SIE 430.
SIE 531: Simulation Modeling and Analysis
Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Grading: Regular grades are awarded for this course: A B C D E. May be convened with SIE 431.
SIE 533: Fundamentals of Data Science for Engineers
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.
SIE 536: Experiment Design and Regression
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.
SIE 540: Survey of Optimization Methods
Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. Grading: Regular grades are awarded for this course: A B C D E. May be convened with SIE 440.
SIE 544: Linear Programming
Linear and integer programming formulations, simplex method, geometry of the simplex method, sensitivity and duality, projective transformation methods.
SIE 545: Fundamentals of Optimization
Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.
SIE 546: Algorithms, Graphs, and Networks
Model formulation and solution of problems on graphs and networks. Topics include heuristics and optimization algorithms on shortest paths, min-cost flow, matching and traveling salesman problems.
SIE 550: Theory of Linear Systems
An intensive study of continuous and discrete linear systems from the state-space viewpoint, including criteria for observability, controllability, and minimal realizations; and optionally, aspects of optimal control, state feedback, and observer theory.
SIE 552: Space Systems Engineering
Fundamentals of space systems engineering; The system engineering process for space missions; Model-based design for spacecraft and space flight systems; Elements of mission analysis and design; Elements of analysis and design for spacecraft subsystems (structure and mechanisms, thermal control; attitude control and orbit determination; command and data handling; propulsion; communication; power).
SIE 554A : Systems Engineering Process
Process and Tools for Systems Engineering of large-scale, complex systems: requirements, performance measures, concept exploration, multi-criteria tradeoff studies, life cycle models, system modeling, etc. Graduate-level requirements include extensive sensitivity analysis of their final projects. May be convened with SIE 454A.
SIE 555: Sensor Systems Engineering
Provides students with a system-level understanding of sensor development. The student will see the development of remote sensing techniques beginning with high-level requirements through concept of operations, architecture development, subsystem modeling and culminating in integration, validation and verification. The student will be exposed to key design parameters for radar and Electro Optical sensing systems that drive both system cost and performance. Advanced multi-sensor systems and adaptive signal processing will also be discussed. May be convened with SIE 455.
SIE 556: Fundamental of Guidance for Aerospace Systems
The main objective of the course is to introduce the students with the fundamental principles behind the development of guidance laws for aerospace systems. More specifically, the course will introduce basic and more advanced guidance concepts for aerospace vehicles and discuss their practical implementation on missiles, planetary landers, reentry and launch vehicles.
SIE 557: Project Management
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,Grading: Regular grades are awarded for this course: A B C D E.
SIE 558: Model-Based Systems Engineering
An introduction to model-based systems engineering (MBSE), which is the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases. The course emphasizes practical use of the Systems Modeling Language (SysML) and MBSE methods.
SIE 561: Traffic Modeling & Simulation
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. Grading: Regular grades are awarded for this course: A B C D E. Identical to: C E 561.
SIE 562: Advanced Production Control
Quantitative models in the planning, analysis and control of production systems. Topics include aggregate planning, multi-level production systems, inventory control, capacitated and uncapacitated lot-sizing, just-in-time systems and scheduling.
SIE 563: Integrated Logistics and Distribution Systems
Plan and design of efficient logistics and distribution systems. Topics include: supply chain management, integration of production/inventory/location/transportation decisions, shipment scheduling with incomplete and uncertain information, vehicle routing and scheduling, goods distribution networks with multiple transshipment, terminals and warehouses. Grading: Regular grades are awarded for this course: A B C D E.
SIE 564: Cost Estimation
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. May be convened with SIE 464.
SIE 565: Supply Chain Management
Fundamentals of Supply Chain Management including inventory/logistics planning and management, warehouse operations, procurement, sourcing, contracts and collaboration. Graduate-level requirements include an additional semester research paper. May be convened with SIE 465.
SIE 567: Financial Modeling for Innovation
Strategic, tactical and operational planning; innovation and technological cycles; the elements of entrepreneurship, and human relations topics for technical managers. Graduate-level requirement includes two term papers. Grading: Regular grades are awarded for this course: A B C D E. Identical to: ENGR 567. May be convened with SIE 467.
SIE 570: Intelligent Control Systems & Applications
Architectures and algorithms of intelligent control systems. Concepts, methods and tools for task organization, task coordination, and task executions. Attention will be given to computer simulations and real-world applications.
SIE 571: Systems Cyber Security
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.
SIE 572: Information Security and Research (INSuRE)
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.
SIE 574: Information Analytics and Decision-Making in Engineering
Recent advances in computational and information technology allow the collection and evaluation of vast volumes of data. This explosion in information has amplified the need to understand the value of information and how to use available information to make better decisions that in turn affect the environment.
For example, consider the following questions:
* How should a firm optimally experiment among different website designs before deciding on a single one, with the goal to maximize user traffic or revenue?
* How should a company choose its bid for mining rights if it has access to exclusive probing data, in order to maximize its profit?
* How should a buyer interpret online feedback and ratings before deciding on which product to buy?
* How should a doctor decide which medical tests to perform on a patient to deliver the most effective care?
The course will cover information valuation, decision-making, and information economics in non-strategic and strategic settings.
May be convened with SIE 474.
SIE 575: Bayesian Machine Learning I
We consider optimization problems whose objective functions are unknown and hence have to be learned from data. Such problems are pervasive in science and industry, e.g., when
- designing prototypes in engineering,
- automated tuning of machine learning algorithms, e.g., in deep learning,
- optimizing control policies in robotics,
- developing pharmaceutical drugs, and many more.
Bayesian optimization methods are popular in the machine learning community due to their high sample-efficiency and have become a key technique in the area of "automatic machine learning". We introduce a general framework in which to understand and formulate such optimal learning problems, and provide a survey of problems, methods, and theoretical results.
SIE 577: Introduction to Biomedical Informatics
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.
SIE 578: Artificial Intelligence for Health and Medicine
The practice of modern medicine in a highly regulated, complex, sociotechnical enterprise is a testament to the future healthcare system where the balance between human intelligence and artificial expertise will be at stake. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in medicine. We will explore foundational methods in artificial intelligence (AI) with greater emphasis on machine learning and knowledge representation and reasoning, and apply them to specific areas in medicine and healthcare including, but not limited to, clinical risk stratification, phenotype and biomarker discovery, time series analysis of physiological data, disease progression modeling, and patient outcome prediction. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, clinical/healthcare applications, and relevant tools.
SIE 583: Computer Integrated Manufacturing Systems (CIM)
Modern manufacturing systems with emphasis on information requirements and data management. Includes CAD, CAM, CAP
SIE 596: Special Topics in SIE
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:
- stochastic systems
- systems engineering and design
- human cognition systems
SIE 599: Independent Study
Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699, or 799. May be repeated an unlimited number of times, consult your department for details and possible restrictions. (1-5 units)
SIE 606: Advanced Quality Engineering
Advanced techniques for statistical quality assurance, including multivariate control charting, principal components analysis, economic design of acceptance sampling plans and control charts, inspection errors, and select papers from the recent literature.
SIE 608: Advanced Reliability Engineering
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.
SIE 631: Distributed Multi-Paradigm Simulation Systems
Emphasis on current research problems including random variate generation, modeling, language development and statistical analysis of output.
SIE 640: Large-Scale Optimization
Decomposition-coordination algorithms for large-scale mathematical programming. Methods include generalized Benders decomposition, resource and price directive methods, subgradient optimization, and descent methods of nondifferentiable optimization. Application of these methods to stochastic programming will be emphasized.
SIE 644: Integer and Combinatorial Optimization
Modeling and solving problems where the decisions form a discrete set. Topics include model development, branch and bound methods, cutting plane methods, relaxations, computational complexity, and solving well-structured problems.
SIE 645: Nonlinear Optimization
This course is devoted to structure and properties of practical algorithms for unconstrained and constrained nonlinear optimization. Grading: Regular grades are awarded for this course: A B C D E.
SIE 649: Topics of Optimization
Convexity, optimality conditions, duality and topics related to the instructor’s research interest; e.g., stochastic programming, nonsmooth optimization, interior point methods.
SIE 654: Advanced Concepts in Systems Engineering
Modeling and design of complex systems using the Unified Modeling Language (UML), the Systems Modeling Language (SysML) and Wymorian System Theory. Applications come from systems, hardware and algorithm design. Course will emphasize architecture, requirements, testing, risk analysis and use of various systems design tools.
SIE 678: Transportation Systems
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.
SIE 695A: Colloquium
Contact department for a description of this course. (1-3 units)
SIE 696: Special Topics in Advanced Systems and Industrial Engineering
This course is designed to provide a flexible advanced topics course across several domains in the field of Systems Engineering, Industrial Engineering, and Engineering Management. Students will develop and exchange scholarly information in a small group setting. Selected advanced topics in Systems and Industrial Engineering and Operations Research, such as 1) optimization, 2) stochastic systems, 3) systems engineering and design, 4) human cognition systems, and 5) informatics. Course may be repeated for a maximum of 9 unit(s) or 3 completion(s).
SIE 699: Independent Study
Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699. (1-6 units)
SIE 900: Research
Individual research not related to thesis or dissertation preparation by graduate students. (1-6 units)
SIE 909: Master’s Report
Individual study or special project or formal report thereof submitted in lieu of thesis for certain master's degrees. (1-12 units)
SIE 910: Thesis
Research for the master's thesis (whether library research, laboratory or field observation or research, artistic creation, or thesis writing). Maximum total credit permitted varies with the major department. (1-12 units)
SIE 920: Dissertation
Research for the doctoral dissertation (whether library research, laboratory or field observation or research, artistic creation, or dissertation writing) (1-12 units)