Graduate Courses

Units: 1
Axioms of probability, discrete and continuous distributions, sampling distributions. Applications of statistical estimation, hypothesis testing, confidence intervals.

 

Prerequisite(s): Calculus and SIE 305

Usually offered: Fall, Spring

Units: 1
Introduction to probabilistic models commonly used in systems and industrial engineering and related disciplines. Markov chains, Poisson processes, queuing models.

 

Units: 1
Linear programming models, solution techniques, and duality.

 

Prerequisite(s): Matrix methods for systems of linear equations

Usually offered: Fall, Spring

Units: 3
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.

Usually offered: Fall

Units: 3

This is a three-credit course offered for well-qualified seniors, graduate students, and engineering professionals and practitioners. This is an introductory text and will be supplemented with material on many everyday reliability engineering problems e.g. root cause analysis. The course will make use of Minitab™ software. The scope of this course includes: (1) failure distributions, (2) failure rate models and reliability concepts, (3) reliability systems and state-space models, (4) accelerated testing, (5) repair process and availability, (6) Bayesian reliability estimates, (7) case studies. May be convened with SIE 408.

Prerequisite(s): SIE 430/530 or its equivalent would be useful but is not required

Usually offered: Fall

Units: 3

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.

Prerequisite(s): Background in visual basic application in Excel or similar programming languages is recommended but not required.

Units: 3

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.

Prerequisite(s): It is recommended, but not required, that students have taken SIE 411/511 Human-Machine Interaction.

Units: 3
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.

 

Units: 3
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.

 

Units: 3
Modeling of stochastic processes from an applied viewpoint. Markov chains in discrete and continuous time, renewal theory, applications to engineering processes.

 

Prerequisite(s): SIE 321

Usually offered: Spring

Units: 3

Foundations of decision analysis contextualized for engineering work. Students will learn to frame and model engineering problems as decisions that traverse physics by incorporating the firm’s objectives and the personal preferences of the engineer. In addition, the course will present formal and informal limitations of decision methods traditionally used in engineering, such as rank matrices, and will provide students with alternative theories and methods that foster better decisions. Finally, the course will present the notion of risk assessment and management as inherent to engineering decision-making, instead of as an independent engineering process.

Usually offered: Fall

Units: 3

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. 

Prerequisite(s): SIE 520

Usually offered: Spring

Units: 3
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.

 

Units: 3
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.

 

Units: 3
This course provides fundamental analytical skills necessary to analyze data and make decisions using sports examples. These skills include critical thinking, statistical analysis, computer programming, and data visualization which are generally applicable to other areas of engineering and business. May be convened with SIE 432.

 

Prerequisite(s): SIE 305

Usually offered: Summer

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

 

Prerequisite(s): SIE 530, equivalent courses such as SIE 500A taken in parallel or consent of instructor.

Usually offered: Spring

Units: 3

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. 

Prerequisite(s): SIE 530

Units: 3
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.

 

Units: 3

Linear and integer programming formulations, simplex method, geometry of the simplex method, sensitivity and duality, projective transformation methods.

Prerequisite(s): SIE 340

Units: 3

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.

Prerequisite(s): SIE 340

Usually offered: Fall

Units: 3

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.

Prerequisite(s): SIE 340. Credit allowed for only one of these courses: SIE 546, MIS 546.

Usually offered: Spring

Units: 3

This course will provide senior undergraduate and graduate students an introduction to mathematical nonlinear optimization with applications in machine learning and data science. This course will involve analysis of optimization algorithms, in particular, scalability of algorithms to large datasets will be discussed in theory and in implementation. The fundamental algorithms for nonlinear optimization are studied and applied to supervised learning models, including but not limited to nonlinear regression, logistic regression, support vector machines, and deep neural networks. Students will write their own implementation of the algorithms in the MATLAB/Python programming language and explore their performance on realistic data sets. May be convened with SIE 449.

 

Prerequisite(s): SIE 270, SIE 305, SIE 340

Usually offered: Spring

Units: 3

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.

Prerequisite(s): SIE 350

Usually offered: Spring

Units: 3

Designing a system for a space mission (e.g. spacecraft, smallsat, cubesat) is a complex endeavor that requires the understanding of a variety of specialized subjects: orbital mechanics, attitude determination and control, space communications, thermal control, propulsion, power systems, structure and mechanisms. The course structure is conceived to provide the students with the skills and methodologies required to complete a preliminary design of a space system at both system and subsystem levels. Fundamentals of spacecraft subsystem design are introduced and embedded in a model-based system engineering process that will drive the preliminary design of a full-scale space system. The lectures will provide the technical content that drives the system and subsystem design that will be accomplished throughout the semester.

Usually offered: Spring

Units: 3

Processes and tools for engineering large-scale, complex systems: resources, architecture, requirements, risk management, concept design, preliminary design, detail design, decision making, tradeoff studies, life-cycle models, requirements decomposition, verification planning, life cycle planning, product maintenance, teamwork, and documentation. May be convened with SIE 554A. May be convened with SIE 454A. 

Usually offered: Fall, Spring

Units: 3

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.

Prerequisite(s): PHYS 241, SIE 305, or consent of instructor and Advanced Standing

Units: 3

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.

Units: 3

Processes and tools used to plan and control large scale projects. Topics include organizational design alternatives, formation and management of teams, construction and control of project schedules, risk assessment, and issues specific to global ventures and software development. May be convened with SIE 457.

Prerequisite(s): SIE 305 or consent of instructor

Usually offered: Fall, Spring

Units: 3

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.

Prerequisite(s): Co-Requisite: SIE-454A/554A or consent of the instructor.

Usually offered: Spring

Units: 3

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.

Units: 3

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.

Prerequisite(s): SIE 540, SIE 544

Usually offered: Spring

Units: 3

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.

Prerequisite(s): SIE 305, 321, 440/540

Units: 3

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.

Prerequisite(s): SIE 305, SIE 340

Usually offered: Spring

Units: 3

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.

Prerequisite(s): Working knowledge of a programming language is required.

Usually offered: Spring

Units: 3

Economic analysis of technology/business development for commercialization. Topics include Pro Forma financial statements, time value of money, valuation approaches and entrepreneurship.

Prerequisite(s): Graduate Student Status in Engineering or Science. No prior knowledge of engineering economics required. Reasonably proficient with Microsoft Excel.

Usually offered: Fall

Units: 3

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. 

Units: 3

The purpose of this course is to introduce selected topics, issues, problems and techniques in the area of System Cyber Security Engineering (SCSE), early in the development of a large system. Students will explore various techniques for eliminating security vulnerabilities, defining security specifications/plans and incorporating countermeasures to achieve overall system assurance. SCSE is an element of system engineering that applies scientific and engineering principles to identify, evaluate, and contain or eliminate system vulnerabilities to known or postulated security threats in the operational environment. SCSE manages and balances system security risk across all protection domains spanning the entire system engineering life cycle. 

The fundamental elements of cyber security will be explored, including human cyber engineering techniques, penetration testing, mobile and wireless vulnerabilities, network mapping and security tools, embedded system security, reverse engineering, software assurance and secure coding, cryptography, vulnerability analysis, and cyber forensics. After a fundamental understanding of the various cyber threats and technologies are understood, the course will expand upon the basic principles, and demonstrate how to develop a threat/vulnerability assessment on a representative system using threat modeling techniques (i.e. modeling threats for a financial banking system, autonomous automobile or a power distribution system). May be convened with SIE 471.

Prerequisite(s): ECE 175 or instructor approval

Usually offered: Fall

Units: 3

This course engages students in diverse and varied national cybersecurity/information systems security problems, under an existing and very successful umbrella program called “INSuRE”, that enables a collaboration across several universities, Cyber professionals and cross-disciplined Cyber related technologies. Led by Stevens Institute of Technology, and made possible by a grant from the NSA and NSF, INSuRE has fielded a multi-institutional cybersecurity research course in which small groups of undergraduate and graduate students work to solve unclassified problems proposed by NSA, other US government agencies, and/or private organizations and laboratories. Students will learn how to apply research techniques, think clearly about these issues, formulate and analyze potential solutions, and communicate their results with sponsors and other participating universities.

Working in small groups under the mentorship of technical experts from government and industry, each student will formulate, carry out, and present original research on current cybersecurity/information assurance problems of interest to the nation. This course will be run in a synchronized distance fashion, coordinating activities with other INSuRE technical clients and sponsors, along with partnering universities which are all National Centers of Academic Excellence in Cyber Defense Research (CAE-R).

Prerequisite(s): Background in computer science, computer engineering, information technology or related technical field. One of the following is strongly recommended: SIE 471/571, ECE 478/578, ECE 509, or MIS 416/516

Usually offered: Spring

Units: 3

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.

Prerequisite(s): A basic course in computing or computer applications (ECE 175, CSC127A, or equivalent) or consent of the instructor.

Usually offered: Fall, Spring

Units: 3

Driven by efforts to improve human health and healthcare systems, this course will cover relevant topics at the intersection of people, information, and technology. Specifically, we will survey the field of biomedical informatics that studies the effective uses of biomedical data, information, and knowledge from molecules and cellular processes to individuals and populations, for scientific inquiry, problem solving, and decision-making. We will explore foundations and methods from both biomedical and computing perspectives, including hands-on experiences with systems, tools, and technologies in the healthcare system. Graduate students will be required to submit an additional assignment or project. May be convened with SIE/BME 477.

Usually offered: Fall

Units: 3

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.

Prerequisite(s): Course suitable for majors APPL, BME, ECE, MEE, CSC, SIE, STAT, IS or MIS, or with instructor consent. Basic foundations in linear algebra, discrete mathematics, probability and statistics, and data structures are recommended for this course.

Usually offered: Spring

Units: 3

Prerequisite(s): SIE majors: Advanced Standing; SIE 383, Other ENGR majors: Advanced Standing; (AME 211 or BE 221) and (AME 324A or MSE 370)

Usually offered: Fall

Units: 3

Modern manufacturing systems with emphasis on information requirements and data management. Includes CAD, CAM, CAP

Prerequisite(s): Real-time scheduling, networking and system justification.

Usually offered: Fall

Units: 3

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

Usually offered: Fall, Spring, Summer

Units: 3

Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699, or 799. May be repeated an unlimited number of times, consult your department for details and possible restrictions. (1-5 units)

Usually offered: Fall, Spring, Summer

Units: 3

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.

Prerequisite(s): SIE 530, SIE 506

Usually offered: Spring

Units: 3

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. 

Prerequisite(s): SIE 508

Usually offered: Fall

Units: 3

Emphasis on current research problems including random variate generation, modeling, language development and statistical analysis of output.

Prerequisite(s): SIE 431 or MIS 521A or MIS 521B

Usually offered: Spring

Units: 3

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.

Prerequisite(s): SIE 544 or 545

Usually offered: Fall

SIE 644 Spring Syllabus (PDF)
Units: 3

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.

Prerequisite(s): SIE 544

Usually offered: Spring

Units: 3

This course is devoted to basic theory and algorithms for nonlinear optimization (unconstrained and constrained), including: understanding how algorithms work; choosing appropriate method to solve the problem in different situations; interpreting the performance of algorithms and analyzing the solutions for decision making.

Prerequisite(s): SIE544 or SIE 545

Usually offered: Fall, Spring

Units: 3

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

Usually offered: Fall

Units: 3

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. 

Prerequisite(s): SIE 554A

Usually offered: Spring

Units: 3

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.

Prerequisite(s): SIE 305, SIE 321; SIE 540 or SIE 544; some knowledge of network modeling.

Usually offered: Spring

Units: 3

The Graduate Seminar meets in-person, once a week. Guest speakers from different SIE related domains are invited to present their research work. 

Prerequisite(s): Consult department before enrolling.

Usually offered: Fall, Spring

Units: 3

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).

Prerequisite(s): 1) Backgrounds in high-level programming language, probability and statistics, linear algebra, and math analysis, or , 2) consent of instructor.

Usually offered: Fall, Spring, Summer

Units: 3

Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699.  (1-6 units) 

Usually offered: Spring, Summer

Units: 3

Individual research not related to thesis or dissertation preparation by graduate students. (1-6 units) 

Usually offered: Fall, Spring, Summer

Units: 3

Individual study or special project or formal report thereof submitted in lieu of thesis for certain master's degrees. (1-12 units) 

Usually offered: Spring, Summer

Units: 3

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) 

Usually offered: Fall, Spring, Summer

Units: 3

Research for the doctoral dissertation (whether library research, laboratory or field observation or research, artistic creation, or dissertation writing) (1-12 units) 

Usually offered: Fall, Spring, Summer