Graduate Courses

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.

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.

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.

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.

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. 

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.

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.

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. 

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.

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.

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.

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.

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.

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.

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

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.

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).

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. May be convened with SIE/BME 477.

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.

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

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 control charting, principal components analysis, economic design of acceptance sampling plans and control charts, inspection errors, and select papers from the recent literature.

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.

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.

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.

Convexity, optimality conditions, duality and topics related to the instructor’s research interest; 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. 

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

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).