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Project experience for students who want to pursue a particular analytical, computational, or experimental project experience outside of regular courses. An engineering report on the project is required of each student. Intended for first-year students on project teams and for project team members performing non-technical tasks.

Independent project experience for students pursuing analytical, computational, or experimental work outside of regular courses. Students define their own goals, timelines, and deliverables, which must be approved by a faculty advisor. Regular meetings with the advisor ensure guidance and feedback throughout the project. Grades are based on project planning, execution, and the quality of deliverables—rather than solely on achieving a working solution. Projects often address real-world problems with real stakeholders, and students are expected to demonstrate how their work meets stakeholder needs. A final report is required, detailing the project’s objectives, methods, design decisions, and recommendations for future work, written clearly enough for others to replicate and build upon.

Systems Engineering is one of the most rapidly growing, sought-after skills sets in industry. This course introduces students to fundamental ideas of Systems Engineering that can be applied to virtually any project, whether it be a traditionally engineering focus project or otherwise. This course gives students some of the most popular, versatile, and valued ready-to-use Systems Engineering tools along the Vee-diagram covering key aspects of initial problem definition and defining of system requirements, thru systems design & architecture, to integration, evaluation & setting design targets, and risk identification & mitigation.

This course will provide a rigorous coverage of the (stochastic and deterministic) models commonly used in the study of inventory, operations, and supply chain management. This includes the multi-period newsvendor model and its many variants, as well as more sophisticated models which arise in supply chain management, logistics, and the study of operations more broadly. We will study tools for analyzing and optimizing such systems, as well as operational insights which can be extracted from such models. The course will in general have a fairly mathematical orientation, focusing on using tools from stochastic modeling, optimization, and dynamic programming/algorithms to formulate and analyze these models.

Project experience for students who want to pursue a particular analytical, computational, or experimental project experience outside of regular courses, or for informal instruction supplementing that given in regular courses. An engineering report on the project is required of each student. Students are expected to spend 3-4 hours per week per credit hour working on the project. Intended for sophomores, juniors, and seniors on project teams performing technical tasks.

With the number of embedded systems in the world far exceeding the number of humans, nearly every major piece of technology involves one, if not several embedded systems. Students will understand the capabilities and challenges that come in creating embedded systems and the larger systems that rely upon them -- not becoming experts in embedded systems, but being able to work more effectively with those experts. This hands-on course will also not only introduce core embedded systems concepts, such as hardware peripherals, interrupt service routines, realtime programming, digital i/o, A2D converters, and interfacing with sensors, etc., but will also introduce related systems engineering principles to aid students in making smart and well-supported design choices, manage design interfaces, and provide justification on whole solution trade-offs.

Systems Engineering is one of the most rapidly growing, sought-after skills sets in industry. This course introduces students to fundamental ideas of Systems Engineering that can be applied to virtually any project, whether it be a traditionally engineering focus project or otherwise. This course gives students some of the most popular, versatile, and valued ready-to-use Systems Engineering tools along the Vee-diagram covering key aspects of initial problem definition and defining of system requirements, thru systems design & architecture, to integration, evaluation & setting design targets, and risk identification & mitigation.

Fundamental ideas of systems engineering, and their application to design and development of various types of engineered systems. Defining system requirements, creating effective project teams, mathematical tools for system analysis and control, testing and evaluation, economic considerations, and the system life cycle.Content utilizes model-based systems engineering, which is the integration of systems modeling tools, such as SysML, with tools for systems analysis, such as Matlab and Modelica. The vision for this integration is the ability to create and analyze complete parametric representations of complex products and systems. These systems make it possible to investigate the impact of changing one aspect of a design on all other aspects of design and performance. This course will familiarize students with these modeling languages. Off-campus students must provide their own Windows 7, internet-connected, computer with administrator access in order to install the commercial software used in this course.

This course examines quantitative methods to develop energy and climate-change mitigation policies. Students will learn about widely used software tools in the filed, such as LEAP and OSeMOSYS, to assess these policies. The course covers the analysis of energy demand and supply for different economic sectors. Mathematical models will be utilized to identify optimal policies and energy scenarios. Finally, students will learn how to perform a cost-benefit analysis to quantify the effects of the identified scenarios.

Service systems arise primarily from the service sector of the economy. Examples are contact centers (also known as call centers), airlines, insurance and healthcare. This course describes techniques that are useful in the analysis and design of such systems. The class is structured around a number of cases. The emphasis is on modeling, solving the models, and interpreting the results. Both operational and strategic decisions are covered through appropriate examples.

This course aims at empowering engineers with training in accounting, finance and engineering economics needed to make financial decisions concerning capital investments that have prolonged monetary impacts. These methods are suitable for problems involving capital allocations and investments as well as evaluation and selection of engineering projects. Students learn how to define and select appropriate economic criteria for the evaluation of alternatives as well as how to use proper methods for evaluating alternatives under deterministic conditions.

In today's rapidly changing environment, management personnel, whether in companies, in non-profit organizations or within governmental departments, rely on analysis using quantitative models from the discipline of management science to make the right decisions. Management science tools, techniques and concepts (e.g., data, models, simulation, regression, linear programming, and optimization) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation, and finance. When used wisely, management science models and tools have tremendous value to support managers and systems engineers making right decisions to solve complex problems. In particular, we will focus on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills in complex settings.

Systems Engineering is a rapidly growing transdisciplinary field, and the range of areas systems engineers can specialize in is expanding. Traditionally, students have come from engineering or science backgrounds. However, emerging needs now require students with undergraduate knowledge in quantitative fields like mathematics, statistics, finance, economics, and data science. These skills are essential for applying systems methods to consumer products, service-oriented offerings, supply chains, and large data set analytics. Gaining expertise in these areas creates a strong foundation for a career in the analytical side of Systems Engineering. This course is designed to build core systems analytical skills on an existing mathematical background while offering a introduction to the fundamental systems engineering tools essential for all systems engineers, especially those with limited prior engineering experience.

This course introduces methods and tools from Systems Thinking and Engineering to prospective organizational leaders committed to shaping successful companies in a competitive landscape. The course is structured into biweekly modules covering various decision-making problems that organizational leaders will face in different roles and stages in their careers. We will cover problems from engineering, product development, marketing, sales, operations, finance, and customer behavior. The class will be hands-on and project-based, relying on real world case studies. Where possible, the modules in this course are facilitated by Jupyter notebooks, hence the programming language of choice is Python. The students will apply the learning from module and homeworks within a final group project of their choice.

How are policies defined and implemented? What role do policies play in the systems transition? Where and how to seize opportunities and foster sustainable solutions? How do technology developers, markets, companies and households respond to policy incentives? This course provides an understanding about the strategic role of policies, strategies and planning processes in international and national contexts with a strong focus on the world-critical topic of energy as the course's primary application area.Students will explore synergies between various sectors and learn how energy policies can help address broader environmental and socio-economic agendas. The course provides a solid basis for professionals from different backgrounds to understand energy policy either as planners, analysts or implementers of energy strategies with different stakeholder perspectives.

This transdisciplinary course provides an in-depth learning experience on the fundamentals of sociotechnical systems, and welcomes students from all academic disciplines. Sociotechnical systems increase system complexity of engineered systems/technology, and this course teaches how to embrace them as part of systems engineering practice. An important dimension of sociotechnical systems is technology policy, and this course brings theory from social science disciplines and systems science together to evaluate how technical products interact with policy systems using systems engineering tools/analysis. Policy development, implementation and analysis in an engineering and technology context and its relationship with life cycle management, risk and quality are important themes in this course. There will be an application component to this class to illustrate the relevance for students from all backgrounds.

This course examines fundamental modern techniques of project management from a systems perspective, including project planning, organization, and control. Project management identifies, plans and coordinates activities required in order to deliver a satisfactory system, product, or service of appropriate quality, within the constraints of schedule, budget, resources, infrastructure, available staffing and technology. Topics include the management process, risk management, scheduling methodologies, management of project activities, and project control mechanisms. Complex systems are addressed at larger scales including combinations of novel, evolving, and legacy systems, implementing adaptive strategies and approaches. Case studies are used to examine application of principles. Complex system management, risk analysis and mitigation, integrated product and process development, quality management, and contracting are addressed. Complex system management also includes consideration of multiple disciplines, varying scales, uncertainty, and nonlinearity. Covers application of fundamental project management processes from traditional systems to more complex systems.

This is an advanced course in the application of analytical methodologies and tools to the analysis and optimization of complex systems. On completion of this course, students should be able to use probability and statistics as a modeling and analysis tool for systems exhibiting uncertainty; be able to use algorithms and dynamic programming to model and optimize systems with a recursive structure; be able to use optimization tools to optimize complex systems and tune parameters.

In response to the risks posed by global climate change, many states and countries have set emissions reductions goals necessitating a rapid transition toward zero-carbon energy resources. Achieving these goals entails unprecedented investment in civil infrastructure systems combined with large-scale consumer and industry adoption of clean energy solutions. This course will explore the economic challenges and opportunities associated with this transition, with an emphasis on the electric power sector. The course is broken into two halves. The first focuses on the economic viability of individual projects. The second develops system level models and considers interactions between competing energy sources.

This course focuses on the design and development of computational models to understand and predict the dynamic behavior of systems. In particular considerable emphasis will be placed on the development of systems thinking skills in the analysis of systems, the translation of those skills into the creation of computational tools to support modeling of these systems and the testing of those models. Students will build realistic models in commercial software packages including Vensin.

On a net-zero emissions pathway, energy efficiency is a key component. This course discusses different angles to tackle energy efficiency, from energy-efficient technologies to energy and resource-efficient systems. Working with various sectors and case examples, students learn how to address energy efficiency within different systems' boundaries, tracking energy and resources in the circular economy. Students learn how to promote energy efficiency using a system approach and identifying entry points for building a circular economy. The course is designed for students from different backgrounds interested in the multiple dimensions of energy efficiency and implementation of a circular economy, particularly those from engineering.

Scientific knowledge on climate change has expanded, policies have been put in place and a global agenda has evolved to engage multiple stakeholders in climate action. Various mechanisms and tools provide a framework for action. Understanding these mechanisms helps stakeholders define strategies, start projects, and become protagonists in climate mitigation and adaptation actions. This course provides analytical and practical knowledge on climate mechanisms and equips students to design climate projects and evaluate implementation pathways in the climate agenda. The focus is on energy-related projects and mitigation measures towards a low-carbon future. The course is designed for students from different backgrounds.

This course examines methods and approaches to integration, verification, and validation of complex systems. Systems integration focuses on the interfaces, data flows, and control mechanisms. Verification is the formal process of obtaining objective evidence that a system or system element, product or service fulfils its specified requirements and characteristics during the lifecycle. Validation is used to provide objective evidence that the system, product or service fulfills its business or mission objectives and stakeholder requirements, achieving its intended use in its intended operational environment. Topics include the processes for integrating, verifying, and validating a set of system elements into a system, system-of-systems, product or service that satisfies system stakeholder requirements, system requirements, architecture, and design. Statistical methods and modeling and simulation are presented as quantitative methods applied to system integration, verification, and validation. Complex systems are addressed at larger scales including integration, verification, and validation of combinations of novel, evolving, and legacy components, subsystems, systems, and systems-of-systems. Covers the application of methods and techniques from traditional systems to more complex systems including consideration of multiple disciplines, varying scales, and uncertainty.

Students will learn how software is designed, developed, and delivered in the modern enterprise. This class will give project leaders the ability to effectively incorporate Software Engineering into a broader multidisciplinary Systems-Engineering project. Through a mix of principles and hands-on projects students will master the essential concepts and techniques for successful delivery of the software component of a broader Systems Engineering project. We will cover topics in software project management, development tools and environment, techniques for software integration and delivery, software systems architecture, cloud-computing, and designing for security and reliability.

This transdisciplinary course provides an in-depth learning experience on the fundamentals of the modeling and simulation of social and sociotechnical systems. This course welcomes students from all academic disciplines (especially outside of engineering) if they have had experience with system architecture and/or systems thinking in any domain application. System perspectives are increasingly being used to engage with the complexity of environmental, social, economic and cultural dimensions of technology, and simulation is a powerful tool in the development of policies that affect these dimensions. Students do not need to have strong computational experience before taking this course, but they need to have a strong systems background. Using system dynamics modeling, this course teaches how to develop simulations of the sociotechnical dimensions of technology design, development and implementation with a strong focus on inclusive, participatory, stakeholder-centric modeling (e.g., Living Labs). In a hybrid asynchronous lab-based environment, students in this course will gain practical skills in using a modeling process to develop models and simulations of sociotechnical systems.

This course focuses on the design and development of self-awareness and learning models to understand and predict the dynamic behavior of systems. In particular considerable emphasis will be placed on the development of critical thinking skills in the analysis of time-varying systems in response to system data. Students will be provided prototype computer code to help them build realistic models from first principles in MATLAB/SIMULINK without commercial software packages. The goal is for students to leave the course with the independent ability to utilize learning systems to analyze and predict behavior of systems without the aid of any tailored commercial software package.

Transdisciplinarity involves gaining insights about patterns that occur across or connect disciplines. The course presents multiple perspectives to study the nature, scope, value, and potential of transdisciplinarity as there is no existing universal definition, theory, or methodology. The course examines several kinds of disciplinarity as approaches to problem solving. Problems are investigated in the areas of society, engineering, and nature. Topics include characteristics, management, methodologies, analysis, and tools for understanding and applying approaches. Complex systems are addressed with consideration of interdisciplinary systems, systems-of-systems, varying scales, uncertainty, and nonlinearity.

Develops skills in the design, operation and control of systems for reliable performance. Focuses on four key themes; risk analysis (with a particular emphasis on risk assessment and risk characterization), modeling system reliability (including the development of statistical models based on accelerated life testing), quality control techniques and the optimization of system design for reliability. Six Sigma Green or Blackbelt can be earned through activities associated with course. Students in distance-learning programs enroll in SYSEN 5100.

In this course students learn how to setup a mathematical simulation model to predict the system outcomes based on variable data. To be an expert in DT one need to know about data science, predictive analytics, machine learning, and artificial intelligence. Digital Twin can be thought of as a bridge between the physical and digital system. The virtual presentation of a physical system enables us to link all steps of a process including input supply, production process, performance evaluation, and customer satisfaction. DT allows us to analyze the data and monitor a system, so to head off problems before they occur. Thus, mangers will be able to consistently improve efficiency of a system by reducing its failure rates. This can open up new business opportunities.

This course presents a rigorous, quantitative multidisciplinary design methodology that incorporates the creative side of the design process. Through a topic of your choice, learn how to use multidisciplinary design optimization (MDO) to create advanced and complex engineering systems that must be competitive in performance and life-cycle value. Multidisciplinary design aspects appear frequently during the conceptual and preliminary design of complex new systems and products, where different disciplines (e.g. structures, aerodynamics, controls, optics, costing, manufacturing, environmental science, marketing, etc.) have to be tightly coupled in order to arrive at a competitive solution. This course is designed to be fundamentally different from most traditional university optimization courses which focus mainly on the mathematics and algorithms for search. Focus will be equally strong on all three aspects of the problem: (i) the multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. Students will demonstrate mastery of the subject by working in small teams on a term project to apply the multidisciplinary design optimization principles to design and optimize an engineering system of their choice.

This class provides a hands-on introduction to the design of Internet-of-Things (IoT) devices using microprocessor-based embedded controllers. Students will design, debug and construct real-time IoT digital systems that illustrate and employ techniques of systems engineering. Special emphasis will be placed on communications hardware, network connectivity and network security.

This transdisciplinary project-based course features small teams advancing technology and policy for the circular economy using the Cornell Campus as a living lab. Students are first grounded in concepts of circular economy, industrial ecology and systems engineering. Using this knowledge, students will be developing simulation models as part of the course using system dynamics modeling. All modeling and simulation are taught from the ground up, and students do not need to have a strong computational background before taking this course. Please note that the distance learning is only available for the graduate level.

The goal is to identify and analyze the unique cybersecurity challenges faced by cyber-physical systems, particularly within the constraints of assets like space systems and their infrastructure. The course will then focus on applying practical mitigation techniques to enhance the security of such critical infrastructure operations.

Every system has an architecture (its essence, or DNA), i.e., a high-level abstraction of its design that provides a unifying concept for detailed design and commits most of the system's performance and lifecycle cost. This course presents the frameworks, methods, and tools required to analyze and synthesize system architectures. The course has a theory part that emphasizes synergies between humans and computers in the architecture process, and a practical part based on a long project and guest lectures by real system architects. The theory part covers topics such as architecture views, layers and projections, stakeholder networks, dealing with fuzziness, automatic concept generation, architecture space exploration, patterns and styles, heuristics, and knowledge engineering. The practice part focuses on special topics such as commonality, platforming, reuse, upstream and downstream influences, and software architecture.

Cyber-Physical Systems (CPS) is a comprehensive course designed for students interested in the integration of computational and physical systems. It covers core concepts such as sensors, actuators, communication protocols (e.g., I2C, SPI, Wi-Fi, Bluetooth), signal processing, computer vision, and control algorithms essential for modern systems like autonomous vehicles, smart grids, and robotics. Through hands-on labs, students will gain practical experience in programming microcontrollers (Raspberry Pi Pico W), system integration, and problem-solving by implementing an autonomous robotic platform (Sparkfun XRP). The course emphasizes design trade-offs, systems architecture, and adaptability to new technologies, preparing students for careers in industries where CPS is increasingly critical. Whether you're a systems engineer, or someone passionate about integrating emergent technologies to solve real-world problems, this course equips you with the tools to tackle complex, interdisciplinary challenges in CPS.

This introductory course is designed for on-campus and distance learning students to learn the fundamentals of embedded systems, emphasizing microcontroller-based design, programming, and debugging. Students will explore the role of embedded systems in modern technology, learning how to interface with sensors, actuators, and communication protocols such as UART, SPI, and I2C. The course follows a hands-on approach, culminating in a project where students build a digital theremin using Python. Topics include GPIO control, interrupts, real-time operating systems, pulse-width modulation, filtering, and direct memory access. The course also integrates SYSEN principles, on how to define user stories, interface requirements, and performance metrics. Through lessons and practical applications, students will develop a foundation in embedded system development, preparing to collaborate effectively with embedded systems experts in industries.

Network systems pervade our society in both social and technological contexts. On the one hand, social networks play a central role in the transmission of information and viruses with fundamental consequences for product marketing, technology adoption, voting decisions, spread of false news and epidemiology. On the other hand, network topology fundamentally affects the performance and resilience properties of large-scale multi-agent systems, such as the electric power grid, the internet of things, traffic or robotic sensor networks.The main objective of this course is to introduce fundamental mathematical tools to model and control the behavior of both social and engineered network systems. Questions of interest will be how the network structure impacts the dynamics of network systems, how network properties can be exploited to maximize system performance or resilience and how one can address these questions while accounting for strategic human behavior. The course will introduce tools that can be used to address these questions and overcome challenges related to the coupled, distributed, and large-scale nature of network systems.

This course examines the time-dependent behavior of managed systems to describe, analyze, and understand them. Students will first learn concepts of feedback and system structure and apply these to problem identification. Qualitative and quantitative mental models will be used to define and describe interrelationships that define the system behavior. Principles of how to intervene in a system will also be discussed in this course. Simulation approaches for both discrete and continuous processes will be utilized to set baseline performance metrics and likewise quantify the effects of the identified interventions.

Bringing about change in ourselves, our teams, our organizations and the complex systems we inhabit and interact with each day can be a challenging undertaking. This interdisciplinary course brings together conversations, theories, and methodologies from design, organizational behavior, human factors, and systems engineering to consider how we might approach change efforts in a manner that considers the complexity of the system and the individuals who affect and are affected by it. Individually and in small teams, students will critically examine stakeholders and systems at a range of scales, starting from a single person to large-scale systems with diverse stakeholder groups. A variety of large-scale systems will be explored, including but not limited to education, healthcare, and energy. Throughout the course, students will engage with methods for deeply understanding system stakeholders, approaches to systems analysis and modeling, change theories, and well-documented change processes.

This course introduces students to the concepts of Digital Twins and Model-Based Systems Engineering, exploring their applications in modern engineering practices. Students will learn how to create and utilize digital representations of physical systems to optimize design, testing, and operation processes.

Fundamentals and practice of Systems Modeling Language (SysML) for graphically specifying, analyzing, designing, and verifying complex systems. The focus and vision is on building unambiguous and computable models of systems behavior and structure that capture systems requirements and specifications.

Examines the operations of electric power systems, the smart grid, and electricity markets. Topics include modeling of power systems, power flow analysis, economic dispatch, optimal power flow, unit commitments, electricity markets, demand response, smart grid technology, and transactive energy.

Digital Systems Modeling with Generative AI focuses on the integration of digital technologies in the design, simulation, and management of complex systems. The course covers key concepts such as Digital Twins, Cyber-Physical Systems (CPS), and Digital Threads, with a strong emphasis on real-time simulation, data-driven modeling, and the application of Artificial Intelligence. Students will collaboratively address complex engineering challenges, allowing them to refine their problem-solving skills, apply recent technologies, and explore the practical implications of their designs in real-world scenarios. The course includes guest lectures from industry experts, providing insights into emerging trends in smart cities, IoT, and industrial applications of Digital Twins. By the end of the course, students will have gained practical skills in building and optimizing digital systems for modern engineering challenges.

The System Integration, Verification, and Validation course covers the realization part of the systems engineering process. This course has been designed to be taken concurrently with SYSEN 5100, following the same top-down approach. This allows the students to work on the same type of problems, from two different perspectives, at the same time in the Fall semester. While an introduction to the definition part of the systems engineering process is provided, the focus is to equip the students with the tools and mindset that allow them to be sure that the system of interest is built correctly and satisfies the stakeholder needs. The successful student will understand the importance of proper planning for verification and validation activities and will learn best practices for design and management of large-scale testing. The course is also built with an eye on innovative approaches to system realization and state-of-the-art practices such as continuous early validation, and continuous integration.

In a modern healthcare system, exchange of clinical data across multiple stakeholders - between healthcare organizations, between providers and patients, and among agencies and governmental entities - is pivotal. Health information standards provide the backbone to achieve uniform data interoperability and exchange across multiple heterogeneous systems. This course will introduce various existing and emerging clinical data modeling, terminology and knowledge representation standards that are part of Meaningful Use Regulations, and discuss scenarios and use cases where the standards have been applied for routine clinical practice and research. Through lectures, case studies, midterm, and a final project, students will gain insights into topics such as disease classification systems, health information exchanges, and electronic health records (EHR) interoperability.

Natural Language Processing (NLP) is a pivotal technology in artificial intelligence. Its significance has noticeably amplified within the medical field in recent years, as vast amounts of unstructured text data await analysis from databases such as Electronic Medical Records, biomedical literature, and clinical trials. Moreover, the advent of technologies like ChatGPT and other Large Language Models (LLMs) holds the promise of vastly transforming research methodologies and clinical practice. This course aims to provide students comprehensive knowledge of Natural Language Processing, generative AI, and related health applications. Students will learn about various text data sources, integral linguistic structures, and a range of processing methods.

The purpose of this class is to teach students the fundamentals of artificial intelligence (AI) technologies and how they can be applied in various healthcare system engineering problems. We will introduce conventional AI technologies including supervised learning for tasks like clinical risk prediction and computer assisted diagnosis, unsupervised learning methods for subtype identification and pattern discovery; as well as deep learning methods, including the basic perceptron and feedforward neural networks for standard vectorized data, convolutional neural networks for analyzing medical images, recurrent neural networks and transformer for analyzing event sequences and temporal signals, and graph neural networks for analyzing networks and relational data. The class includes both lectures introducing algorithms and theories, and programming exercises to get hands-on experience on implementing these algorithms with Python.

The course “Programming Essentials for Health AI and Data Science” is designed for advanced students and clinicians seeking to develop programming expertise in healthcare applications. This course provides hands-on experience with Python, Pytorch, data science/machine learning libraries, LLM and agents techniques, focusing on real-world health data, including EHRs, medical imaging, and clinical text. Participants will explore AI models for disease prediction, causal inference, and natural language processing etc. The course emphasizes practical implementation, from preprocessing messy health data to deploying AI models in clinical settings. Capstone projects will apply AI to real-world health challenges.

Covers the basic models and solution approaches for individual and team decision-making problems under uncertainty and provide a unified mathematical treatment of the subject, suitable for a broad engineering audience. The material will consider optimal decision-making of systems over a finite- and an infinite-time horizon. Topics include: (1) Stochastic optimization: finite- and infinite-horizon problems with complete or partial state information, separation principle, dual control; (2) Team Theory: mathematical framework of cooperating members in which all members have the same objective yet different information; (3) Reinforcement learning: approximate dynamic programming, forward references to the approximate dynamic programming formalism, learning policies.

The course covers the basic models and solution approaches in problems that involve interactions among strategic agents distilling the key results in mechanism design theory. Over the last seventy years, the theory of mechanism design was developed as an approach to efficiently align the individuals' and system's interests in problems where individuals have private preferences. It can be viewed as the art of designing information and protocols to achieve a desired outcome. Mechanism design has broad applications spanning many fields, including transportation routing, smart grid, communication networks, social media, online advertising, and resource allocation problems. The objective of this course is to gain a sound understanding of the science behind the use of mechanism design in solving modern problems that involve strategic interactions among agents. The course will provide a unified treatment of the subject, suitable for a broad engineering audience.

Supervised study by individuals or small groups of one or more specialized topics not covered in regular courses.

In this project-based class, interdisciplinary teams will combine design thinking and systems engineering techniques to define and solve complex systems and organization problems. Throughout the entire design process (from fieldwork to brainstorming, prototyping and testing) students will benefit from systems engineering tools in capturing and organizing essential information. Direct interaction with real sponsors, stakeholders and final users is an important part of this class.

Systems optimization modeling, computation, and applications. Includes theory and algorithms of linear, nonlinear, mixed-integer linear, mixed-integer nonlinear, and deterministic global optimization, as well as stochastic programming, robust optimization and optimization methods for big-data analytics. Real-world applications of large-scale computational optimization in process manufacturing, bioengineering, energy systems, and sustainability.

This course provides a brief review of several topics in sufficient detail to amplify student success: estimation, allocation, and control; classical feedback; sensor noise; and Monte Carlo analysis. The review leads to application of the methods of Pontryagin applied to examples including single-gimballed rocket engines, guidance, and control problems including least squares estimation, and the famous Brachistochrone problem as a motivating example illustrating the minimum time solution is not necessarily the minimum path-length solution, particularly in a gravity field. After taking this course, students will be able to apply their expertise to actual systems in advanced courses or in laboratory settings leveraging analytic (non-numerical) nonlinear programming and real-time optimal control. Graduates will understand the application of constrained (smooth constrained, box constrained, with brief introduction to inequality constrained) and unconstrained optimization; linear-quadratic programming; and Bellman's principle of optimality.

This course is taught from the perspective of a chief technology officer and is targeted at M. Eng. and management students interested in real world problems. It provides an introduction via case examples to the technical, management, and organizational issues of developing and marketing products in high-tech businesses. The focus is on the unique nature of this type of business, including managing with high risk/uncertainty levels, learning to manage very diverse project teams, and recognizing technical versus market success in order to make good business decisions.

This course covers the basic concepts, models and algorithms of Bayesian learning, classification, regression, dimension reduction, clustering, density estimation, artificial neural networks, deep learning, and reinforcement learning. Application and methodology topics include process monitoring, fault diagnosis, preventive maintenance, root cause analysis, soft sensing, quality control, machine learning for process optimization, data-driven decision making under uncertainty, missing data imputation, data de-noising, and anomaly/outlier detection.

Covers the basic concepts, models, methods, and applications of deep learning. Topics include basics of artificial neural networks, training of neural networks, convolutional neural networks, recurrent neural networks, generative models, deep reinforcement learning, and deep learning hardware and software packages. Application and methodology topics include deep learning for pharmaceutical discovery, deep learning for process control, deep learning for molecular design, deep learning for material screening, deep learning for product yield and quality estimation, and deep learning for optimization.

A design project that incorporates the principles of systems engineering for a complex system. Projects are performed by teams of students working together to meet the requirements of the project.

First of two one-week intensive experiential courses (35 hours) in systems engineering management, with emphasis on laying the social groundwork for students to conduct projects in geographically dispersed teams. Course involves a significant design challenge that must be completed within the week. A leadership laboratory is run simultaneously with the design experience to encourage students to self-assess their leadership style and practices in systems engineering projects.

This course examines fundamental modern techniques of project management from a systems perspective, including project planning, organization, and control. Topics include leadership, the management process, risk management, scheduling methodologies, management of project activities, and project control mechanisms. Complex systems are addressed at larger scales including combinations of novel, evolving, and legacy systems, implementing adaptive leadership and management strategies and approaches. Complex system leadership, management, risk analysis and mitigation, integrated product and process development, quality management, and contracting are addressed. Complex system leadership and management also includes consideration of communications, interpersonal skills, emotional intelligence, ethics, team dynamics, multiple disciplines, varying scales, uncertainty, and nonlinearity. Covers application of fundamental project leadership and management processes from traditional systems to more complex systems.

Second of two one-week intensive courses (35 hours) in systems engineering management with emphasis on understanding individual creativity and organizational innovation and on developing the required systems engineering leadership skills to foster both.

Students spend a semester in a Systems Engineering relevant internship. Working with their advisor they draft a paper about their systems engineering experience and the systems engineering educational benefits from that experience. In the semester after the internship, students work with their advisor on the final version of the paper, helping the student to crystalize the value of their experience in developing their systems engineering knowledge and careers.

This course covers the scientific foundations of the behavior of complex systems. The course is organized in a number of modules motivated by real-life examples of complex systems including aerospace systems, transportation systems, infrastructure systems, or biological systems among others. Each module will introduce, in the context of a specific problem, a number of concepts such as complexity, emergence, collective behavior, self-organization, evolution and adaptation, and pattern formation. Students will learn to use a variety of mathematical tools to analyze these systems, such as non-linear dynamics and controls, game theory, agent-based simulation, network theory, genetic algorithms, neural networks, and cellular automata. Students will be required to do a substantial amount of reading, and there will be roughly biweekly homework and a final project related to one or more of the topics covered.

This is a weekly seminar course designed to give graduate students experience in improving their skills in presenting their research, judging peers' and field experts' research, as well as learning to accurately dissect and summarize the main points of a research talk. Being part of the Systems Engineering program, this course will strongly focus on developing the ability to present research in a broader context or as it relates to a larger system or process, and communicate the importance of the students' work to a more varied audience; a particularly important skill for both future job talks and grants' importance paragraphs. Students' presentations will also include some research depth as part of developing stronger conference talks and to aid classmates in expanding their knowledge.Graduate students will be expected to give one presentation on their research as well as attend the majority of their peers' presentations and the field experts' presentations that are part of the Erza's Roundtable seminar series.Students will also be given the opportunity to receive critiques and feedback from not only their colleagues but from faculty, including potential individual or small groups meetings with the instructing faculty.

Fundamental ideas of systems engineering, and their application to design and development of various types of engineered systems. Defining system requirements, creating effective project teams, mathematical tools for system analysis and control, testing and evaluation, economic considerations, and the system life cycle. Content utilizes model-based systems engineering, which is the integration of systems modeling tools, such as SysML, with tools for systems analysis, such as Matlab and Modelica. The vision for this integration is the ability to create and analyze complete parametric representations of complex products and systems. These systems make it possible to investigate the impact of changing one aspect of a design on all other aspects of design and performance. This course will familiarize students with these modeling languages. Off-campus students must provide their own Windows 7, internet-connected, computer with administrator access in order to install the commercial software used in this course.

This course integrates systems thinking in analyzing and solving complex problems. Students will first learn to conduct situation appraisals using a systems approach. This will be followed by concepts of feedback and system structure and the application of these to problem identification. Mental models will be used to define and describe interrelationships and system behavior. Principles of how to intervene in a system using optimization techniques will also be discussed. Optimization models will be utilized to set baseline performance metrics and quantify the effects of the identified interventions.

This transdisciplinary course provides an in-depth learning experience on the fundamentals of sociotechnical systems, and welcomes students from all academic disciplines. Sociotechnical systems increase system complexity of engineered systems/technology, and this course teaches how to embrace them as part of systems engineering practice. An important dimension of sociotechnical systems is technology policy, and this course brings theory from social science disciplines and systems science together to evaluate how technical products interact with policy systems using systems engineering tools/analysis. Policy development, implementation and analysis in an engineering and technology context and its relationship with life cycle management, risk and quality are important themes in this course. There will be an application component to this class to illustrate the relevance for students from all backgrounds.

This is an advanced course in the application of the systems engineering process to the design and operation of complex systems. Topics include techniques for design, simulation, optimization, and control of complex systems. Case studies and system simulations in diverse areas provide context for the application of these techniques. PhD students will be required to complete additional work.

This graduate-level course covers the fundamentals of power systems economics and electricity markets, emphasizing the application of microeconomics and optimization tools. The course introduces basic microeconomic concepts in the context of retail and wholesale electricity markets: consumer preference, consumer choice, production costs and profit maximization, individual and market demand and supply functions, competitive markets and equilibria, welfare optimization, market power, monopoly and price discrimination, regulated monopoly and utility pricing. The course covers electricity market design: energy markets, reserve markets, ancillary service markets, capacity markets, ancillary service markets, scarcity pricing, capacity markets, financial transmission rights, inter-regional transaction markets, and virtual transaction markets. Advanced topics on distributed energy resources and energy aggregation are discussed and assigned as part of class projects.

This transdisciplinary course provides an in-depth learning experience on the fundamentals of the modeling and simulation of social and sociotechnical systems. This course welcomes students from all academic disciplines (especially outside of engineering) if they have had experience with system architecture and/or systems thinking in any domain application. System perspectives are increasingly being used to engage with the complexity of environmental, social, economic and cultural dimensions of technology, and simulation is a powerful tool in the development of policies that affect these dimensions. Students do not need to have strong computational experience before taking this course, but they need to have a strong systems background. Using system dynamics modeling, this course teaches how to develop simulations of the sociotechnical dimensions of technology design, development and implementation with a strong focus on inclusive, participatory, stakeholder-centric modeling (e.g., Living Labs). In a hybrid asynchronous lab-based environment, students in this course will gain practical skills in using a modeling process to develop models and simulations of sociotechnical systems.

Develops skills in the design, operation and control of systems for reliable performance. Focuses on four key themes; risk analysis (with a particular emphasis on risk assessment and risk characterization), modeling system reliability (including the development of statistical models based on accelerated life testing), quality control techniques and the optimization of system design for reliability. Six Sigma Green or Blackbelt can be earned through activities associated with course.

Every system has an architecture (its essence, or DNA), i.e., a high-level abstraction of its design that provides a unifying concept for detailed design and commits most of the system's performance and lifecycle cost. This course presents the frameworks, methods, and tools required to analyze and synthesize system architectures. The course has a theory part that emphasizes synergies between humans and computers in the architecture process, and a practical part based on a long project and guest lectures by real system architects. The theory part covers topics such as architecture views, layers and projections, stakeholder networks, dealing with fuzziness, automatic concept generation, architecture space exploration, patterns and styles, heuristics, and knowledge engineering. The practice part focuses on special topics such as commonality, platforming, reuse, upstream and downstream influences, and software architecture.

Exploration of engineering design frameworks that effectively exploit simulation, optimization, and uncertainty assessments when balancing large numbers of conflicting performance objectives. Students will learn and advance software frameworks that combine evolutionary multiobjective optimization, high performance computing, uncertainty modeling techniques, and visual design analytics. The primary focus will be improving multi-stakeholder design of complex engineered systems. Course concepts will be demonstrated using case studies and projects drawn from the disciplines of the students enrolled.

This course has been co-created and co-led by forward-thinking community, national and global leaders. This course challenges our notions on the ways policy and design can empower people, organizations, and communities to achieve health-related operational and business objectives. In this class, we think critically together and apply a systems approach to analyze policies, identify environmental strategies, and propose and design solutions to improve lives. Course modules examine how concepts such as environmental psychology, behavioral economics, spatial analyses, human centered design, LEAN, and systems approach can be used to optimize environments and policies to produce healthy, and sustainable facilities and communities. We study the issues from multiple stakeholder perspectives, explore bottom up and top down approaches to catalyze positive change. The lessons learned and case studies shared are from health and healthcare and can be applied by the students to their area and sector of interest. Students contribute actively to the course content by customizing their readings and leading discussion sessions on a weekly basis.

Bringing about change in ourselves, our teams, our organizations and the complex systems we inhabit and interact with each day can be a challenging undertaking. This interdisciplinary course brings together conversations, theories, and methodologies from design, organizational behavior, human factors, and systems engineering to consider how we might approach change efforts in a manner that considers the complexity of the system and the individuals who affect and are affected by it. Individually and in small teams, students will critically examine stakeholders and systems at a range of scales, starting from a single person to large-scale systems with diverse stakeholder groups. A variety of large-scale systems will be explored, including but not limited to education, healthcare, and energy. Throughout the course, students will engage with methods for deeply understanding system stakeholders, approaches to systems analysis and modeling, change theories, and well-documented change processes.

This course introduces fundamental ideas of Systems Engineering and their application to the design and development of various types of engineered systems.  The systems engineering technical design process is emphasized, following the entire Vee-diagram process from initial problem definition and defining of system requirements, thru systems design & architecture, to implementation planning, and & testing and evaluation. The course also provides an applied introduction to a variety of areas that are commonly a part of Systems Engineering including creating effective project teams, customer needs evaluation, mathematical tools for system analysis, control & optimization, risk analysis and mitigation, decision making strategies, project organization & management, and the system life cycle. The course requires students to combine their work in this course with an outside-of-course MEng project.

This course introduces fundamental ideas of Systems Engineering and their application to the design and development of various types of engineered systems.  The systems engineering technical design process is emphasized, following the entire Vee-diagram process from initial problem definition and defining of system requirements, thru systems design & architecture, to implementation planning, and & testing and evaluation. The course also provides an applied introduction to a variety of areas that are commonly a part of Systems Engineering including creating effective project teams, customer needs evaluation, mathematical tools for system analysis, control & optimization, risk analysis and mitigation, decision making strategies, project organization & management, and the system life cycle. The course requires students to combine their work in this course with an outside-of-course MEng project

This course introduces fundamental ideas of Systems Engineering and their application to the design and development of various types of engineered systems.  The systems engineering technical design process is emphasized, following the entire Vee-diagram process from initial problem definition and defining of system requirements, thru systems design & architecture, to implementation planning, and & testing and evaluation. The course also provides an applied introduction to a variety of areas that are commonly a part of Systems Engineering including creating effective project teams, customer needs evaluation, mathematical tools for system analysis, control & optimization, risk analysis and mitigation, decision making strategies, project organization & management, and the system life cycle. The course requires students to combine their work in this course with an outside-of-course MEng project.

Covers the basic models and solution approaches for individual and team decision-making problems under uncertainty and provide a unified mathematical treatment of the subject, suitable for a broad engineering audience. The material will consider optimal decision-making of systems over a finite- and an infinite-time horizon. Topics include: (1) Stochastic optimization: finite- and infinite-horizon problems with complete or partial state information, separation principle, dual control; (2) Team Theory: mathematical framework of cooperating members in which all members have the same objective yet different information; (3) Reinforcement learning: approximate dynamic programming, forward references to the approximate dynamic programming formalism, learning policies.

Systems optimization modeling, computation, and applications. Includes theory and algorithms of linear, nonlinear, mixed-integer linear, mixed-integer nonlinear, and deterministic global optimization, as well as stochastic programming, robust optimization and optimization methods for big-data analytics. Real-world applications of large-scale computational optimization in process manufacturing, bioengineering, energy systems, and sustainability.

This studio-style course emphasizes collaborative learning and innovation in sustainability. Students will explore foundational and cutting-edge literature, research, and potential future directions in AI for Sustainability. The course will cover a range of topics related to the use of AI and machine learning in sustainability science and engineering, including energy systems decarbonization, sustainable agriculture, climate modeling, resource optimization, and biodiversity conservation. Students will gain hands-on experience with AI/ML methodologies, tools, and software and engage in discussions on the latest advancements and applications of AI in addressing global sustainability challenges.

This course focuses on the application of artificial intelligence (AI) to energy systems over four weeks. Students will explore how AI techniques can optimize the performance of energy and power systems, with a particular focus on sustainable energy systems and renewable energy transition. Key topics include the optimization of energy generation, distribution, storage, and consumption. Specific case studies will cover topics such as optimizing solar and wind energy integration into the grid, improving battery storage management for renewable energy, and enhancing energy efficiency in smart grids. The course will also highlight AI applications in balancing supply and demand for renewable energy systems.

This course provides a brief review of several topics in sufficient detail to amplify student success: estimation, allocation, and control, classical feedback, sensor noise, and Monte Carlo analysis. The review leads to application of the methods of Pontryagin applied to examples including single-gimballed rocket engines, guidance, and control problems including least squares estimation, and the famous Brachistochrone problem as a motivating example illustrating the minimum time solution is not necessarily the minimum path-length solution, particularly in a gravity field. After taking this course, students will be able to apply their expertise to actual systems in advanced courses or in laboratory settings leveraging analytic (non-numerical) nonlinear programming and real-time optimal control. Graduates will understand the application of constrained (smooth constrained, box constrained, with brief introduction to inequality constrained) and unconstrained optimization; linear-quadratic programming; and Bellman's principle of optimality.

This course focuses on the application of artificial intelligence (AI) in the digital transformation of agriculture. Students will explore how AI techniques are applied to optimize and automate agricultural systems, improve productivity, and enhance sustainability. The course covers a broad range of topics, including AI-driven crop management, precision farming, livestock monitoring, and data analytics for sustainable agriculture. Case studies on AI applications in plant and animal production systems, as well as food supply chains, will provide practical insights into the future of farming. Students will engage in discussions on the ethical, social, and economic implications of AI in agriculture, while hands-on projects will offer experience in applying AI tools to real-world agricultural challenges.

This course covers the basic concepts, models and algorithms of Bayesian learning, classification, regression, dimension reduction, clustering, density estimation, artificial neural networks, deep learning, and reinforcement learning. Application and methodology topics include process monitoring, fault diagnosis, preventive maintenance, root cause analysis, soft sensing, quality control, machine learning for process optimization, data-driven decision making under uncertainty, missing data imputation, data de-noising, and anomaly/outlier detection.

This course provides a comprehensive overview of deep learning, covering basic concepts, models, algorithms, and applications. Topics include artificial neural networks, training techniques, convolutional neural networks, recurrent neural networks, generative deep learning, deep reinforcement learning, and deep learning hardware and software. Recent advances in deep learning, such as graph neural networks, attention, Transformer, ViT, BERT, and GPT, will also be discussed. The course explores deep learning-based applications in optimization, sensing, control, and automation, and in AI for Science, including molecular design, material discovery, and pharmaceutical development.

Intended for graduate students who are interested in teaching engineering or related fields as part of their future careers. Includes both discussion and practice of effective teaching techniques, assessments and technologies, an overview of current engineering education research, equity and inclusion in the undergraduate engineering classroom, and action research methods using qualitative/quantitative/mixed methodologies to develop teacher scholars.

Students will be exposed to different research methodologies and engage in critical reviews and discussion of selected research articles in the fields of systems science and engineering. A substantial amount of reading and presentation will be required.

This is a weekly seminar course designed to give graduate students experience in improving their skills in presenting their research, judging peers' and field experts' research, as well as learning to accurately dissect and summarize the main points of a research talk. Being part of the Systems Engineering program, this course will strongly focus on developing the ability to present research in a broader context or as it relates to a larger system or process, and communicate the importance of the students' work to a more varied audience; a particularly important skill for both future job talks and grants' importance paragraphs. Students' presentations will also include some research depth as part of developing stronger conference talks and to aid classmates in expanding their knowledge.