103 courses with the subject SYSEN, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
SYSEN 1900Systems Engineering Project for First-Years1-4
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.
SYSEN 3900Systems Engineering Project of Undergraduate Students1-4
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.
SYSEN 4200Inventory, Operations, and Supply Chain Management: Models and Optimization3
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.
SYSEN 4900Systems Engineering Project for Undergraduates1-4
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.
SYSEN 5020Creating Solutions with Embedded Systems1
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 support energy transition and inform climate-change mitigation policies. Students will learn about widely used software tools in the field, such as LEAP and NEMO, to assess energy transition pathways and policies. The course covers the analysis of energy demand and supply for different economic sectors as they navigate the shift toward low-carbon energy systems. Mathematical models will be utilized to identify optimal policies and energy transition 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.
SYSEN 5140Economic and Financial Decisions for Engineers3
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.
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.
SYSEN 5161Spacecraft Technology and Systems Architecture4
This course is a survey of contemporary space technology from subsystems through launch and mission operations, all in the context of spacecraft and mission design. It focuses on the classical subsystems of robotic and human-rated spacecraft, planetary rovers, and other space vehicles, as well as on contemporary engineering practice. This course includes an in-depth design activity suitable for MEng students. Topics covered include subsystem technologies and the systems-engineering principles that tie them together into a spacecraft architecture. Subsystem technologies discussed include communications, thermal subsystems, structure, spacecraft power, payloads (remote sensing, in-situ sensing, human life support), entry/descent/landing, surface mobility, and flight-computer hardware and software. The final project consists of architecting a complete spacecraft system with appropriate subsystems, with designs supported by parametric analysis and simulation.
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.
SYSEN 5200Systems Analysis Behavior and Optimization3
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.
SYSEN 5211Quantitative Methods for Systems Analysis3
This course will cover quantitative methods for analyzing and solving complex problems. Students will learn both classical and advanced methods, including linear and nonlinear programming, integer programming, heuristic algorithms, simulation, and multi-objective optimization. The course will also cover data-driven optimization to integrate uncertainty in decision-making. This leverages data analytics to enhance the accuracy and reliability of parameter estimation in optimization models. Through a combination of theoretical instruction and hands-on case studies, students will gain the skills to model, analyze, and solve optimization problems in various domains. Emphasis is placed on problem formulation, computational efficiency, and practical implementation using industry-standard tools.
SYSEN 5213Designing Robust Ventures Using Profitability as an Engineering Requirement3
This project-based course introduces students to a systems-based methodology for startup ventures. The methodology designs ventures to be robust and resilient at launch—they’re profitable despite higher-than-expected costs, lower-than-anticipated demand, and unforeseen market changes. Today's venture capital-inspired way of innovating disruptive ventures—the lean startup—has a greater than 95% failure rate, requiring years of costly experimentation. For those building ventures to hold for profits, ventures must be designed using a holistic systems approach that enables high probability of success at launch while cutting validation time and cost. Working in a venture-studio style on an emerging technology or big societal problem chosen by sponsoring companies, students will learn to design a robust core business architecture by systematically solving 10 key requirements governing a venture's cost and value curves.
This course introduces students to the theory and practice of agent-based modeling and simulation. Topics to be discussed include: History of agent-based modeling, cellular automata and pattern formation, economic and game theoretic agent-based models, networked agent models, ecological and evolutionary agent-based models, and real-world applications.
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.
SYSEN 5230Energy Efficiency in the Circular Economy1.5
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.
SYSEN 5250Systems Integration, Verification, and Validation3
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.
SYSEN 5260Software Systems Engineering: Design, Develop, and Deliver Software in the Modern Enterprise3
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.
SYSEN 5270Sociotechnical System Modeling and Simulation3
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.
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.
SYSEN 5300Systems Engineering and Six Sigma for the Design and Operation of Reliable Systems3-4
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 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 field of collective intelligence studies how groups solve problems. You’ll learn what creates the wisdom of crowds and intelligent groups; analyze real management, engineering, & computing processes; and design new collective intelligence studies or techniques.
SYSEN 5381Data Science and AI for Systems Engineering: APIs, Apps, and Analytics for Cloud Systems3
Modern engineering systems increasingly rely on cloud-based, AI-driven data processing, automation, and analytics. This course equips Systems Engineering students with the coding skills needed to interact with cloud-hosted data, automate workflows, and build scalable AI-powered apps and analytics. Students will gain hands-on experience in querying web APIs, building APIs, and leveraging AI models for automation. Topics include API calls, querying Generative AI, and automation, all to serve automated data reporting. Participants will learn to examine and critique uses of AI in modern systems, considering risk, value, tradeoffs, and policy issues for the customer and society. By the end of the course, students will have built cloud-hosted, AI-powered applications and reporting systems that can dynamically retrieve, process, and visualize data using generative AI.
SYSEN 5390Cybersecurity for Cyber-Physical and Aerospace Systems3
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.
SYSEN 5400Theory and Practice of Systems Architecture3
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.
SYSEN 5412Creating Solutions with Embedded Systems1
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.
This 1-credit, introductory-level course is designed for on-campus and distance learning students and introduces them to the fundamentals of IoT, connectivity, and smart devices. Students will explore how embedded systems, sensors, and networks combine to create intelligent, connected systems that are now central to industries such as agriculture, healthcare, manufacturing, and consumer technology. Through a blend of lectures and hands-on exercises, learners will gain practical experience programming microcontrollers, collecting and transmitting sensor data, and visualizing information on cloud dashboards. Special emphasis will be placed on understanding IoT-specific protocols, resource constraints, and basic security practices to ensure safe, efficient, and reliable systems. By the end of the course, students will have the confidence to design simple IoT prototypes, understand where IoT technology is headed, and recognize the opportunities and challenges that come with a rapidly expanding connected world.
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.
SYSEN 5422Sustainable Enterprise: Green Lean Operations and Energy Management3
This course covers advanced methodologies for identifying and eliminating operational waste in manufacturing through an environmental lens, with a focus on industrial energy optimization. Students will study ways to measure and reduce ecological impacts related to energy consumption and efficiency, water, raw materials, garbage, logistics, emissions, and biodiversity, utilizing specialized green value stream mapping techniques.
SYSEN 5424Advanced Enterprise Engineering with Simulation3
This course provides an advanced, systems-level approach to the design, planning, scheduling, and control of enterprise systems. By integrating the structural principles of factory physics with powerful discrete-event simulation techniques, students will learn to analyze, model, and optimize complex enterprise/business environments.
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.
SYSEN 5470Network Science and Applications for Systems Engineering3
Modern engineering systems—like supply chains, power grids, and logistics networks—rely on complex, interconnected networks. This course trains Systems Engineers to model, analyze, and optimize these systems using network science, statistics, machine learning, and AI. Students gain hands-on experience with network visualization, clustering, routing optimization, and performance modeling under uncertainty. Case studies include logistics networks, power grids, data centers, and crisis response systems. Participants will learn to identify critical nodes, simulate and optimize flows, and communicate results through interactive visualizations. By course end, students will develop complete network analysis workflows connecting theory to systems engineering practice. Designed for graduate students in technical roles, the course requires 3–4 hours of work per credit hour.
SYSEN 5480Human-Centered Design for Change in Complex Systems3
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.
SYSEN 5490Digital Twins and Model-Based Systems Engineering3
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.
Designing Modern Manufacturing Systems examines manufacturing as a complex sociotechnical system in which products, people, processes, equipment, data, supply networks, and enterprise objectives must be designed and integrated. Students apply systems engineering methods to analyze and design modern production systems, progressing from process flow, capacity, bottlenecks, quality, and continuous improvement to smart manufacturing and Industry 4.0 technologies, including automation, connected sensors, AI/data analytics, cyber-physical systems, and digital twins. Industry 5.0 extends this perspective through human-centered design, human-machine collaboration, resilience, and sustainability. Through industry cases, simulations, applied design activities, and a semester-long project, students evaluate trade-offs among cost, quality, speed, workforce needs, resilience, sustainability, and customer requirements.
SYSEN 5493Coding with Generative AI for Systems Engineers1
Coding with Generative AI for Systems Engineers is a 1-credit course that introduces students to the use of generative artificial intelligence tools for coding tasks in systems engineering. Students learn how to leverage AI coding assistants (such as large language models) to generate, refine, and debug code, and to integrate these models into engineering workflows. Topics include prompt engineering for code generation, the use of AI in model-based systems engineering, and understanding the limitations and ethical considerations of AI-generated code. Through lectures and small hands-on projects, students gain practical experience employing generative AI to solve real-world engineering problems. The course culminates in a final project where students demonstrate AI-assisted coding in a systems context. This course equips future systems engineers with essential skills in AI-augmented software development.
SYSEN 5500Systems Modeling Language: Fundamentals and Practice2
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.
SYSEN 5530Digital Systems Modeling with Generative AI3
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.
SYSEN 5610Introduction to the US Healthcare System, Data, and Interoperability3
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. (MHA-EL)
SYSEN 5630Clinical Natural Language Processing and Large-Language Models3
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.
SYSEN 5650Programming Essentials for Health AI and Data Science3
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.
SYSEN 5690Information Design for Strategic Decision-Making3
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.
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.
SYSEN 5880Industrial Big Data Analytics and Machine Learning4
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.
SYSEN 5920Systems Engineering Management for Virtual Teams1
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.
SYSEN 5930Project Management and Leadership for Complex Systems4
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.
SYSEN 5935Mission Engineering: Designing and Assuring Mission Outcomes3
Mission Engineering applies systems engineering to ensure engineered and socio-technical systems deliver measurable mission outcomes in contested, uncertain environments. Students learn to translate stakeholder objectives into mission-level measures of effectiveness, develop mission threads and operational scenarios, derive capability requirements, and perform architecture and trade studies across systems-of-systems. Topics include mission modeling, digital engineering and MBSE, resilience and mission assurance, cyber and supply-chain risk, experimentation and simulation, and decision support for acquisition and portfolio prioritization. The course uses case studies from national security, critical infrastructure, and humanitarian response. Teams complete a semester-long mission engineering project producing a mission model, architecture recommendations, and an executive-level briefing.
SYSEN 5940Creativity and Innovation within Systems Engineering1
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.
SYSEN 6170Quantitative Foundations for Systems Engineering3
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.
SYSEN 6172Modeling and Analysis of Complex Networks3
This course provides students with modern network science concepts and mathematical/computational tools for modeling and analysis of various complex networks in both their structures and dynamics. Specific topics to be discussed include; mathematical analysis of network structures and dynamics, statistical properties of complex networks, dynamics *of* networks and dynamics *on* networks, co-evolutionary adaptive networks, epidemic thresholds and phase transitions in networks, temporal networks, multiplex/multilayer networks, higher-order networks, and applications to real-world complex systems problems. Python and NetworkX will be used as the primary tools for network modeling and analysis, in addition to other computational tools introduced and used as needed. Students should have a reasonable amount of experience in Python programming and linear algebra.
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.
SYSEN 6230Power System Economics and Electricity Markets3
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.
SYSEN 6270Sociotechnical System Modeling and Simulation3
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.
SYSEN 6300Systems Engineering and Six Sigma for Design and Operation of Reliable Systems3-4
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.
SYSEN 6400Theory and Practice of Systems Architecture3
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.
SYSEN 6410Multiobjective Systems Engineering Under Uncertainty3
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.
SYSEN 6450Policy Meets Design: High-Impact Facilities of the 21st Century3
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. (MPA-TPA)
SYSEN 6480Human-Centered Design for Change in Complex Systems3
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.
SYSEN 6600Applied Model Based Systems Engineering I3
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.
SYSEN 6610Applied Model Based Systems Engineering II3
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
SYSEN 6620Applied Model Based Systems Engineering III3
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.
SYSEN 6880Industrial Big Data Analytics and Machine Learning4
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.
SYSEN 6950Design for Learning: Bridging STEM Education Research and Practice3
Intended for graduate students (and others) interested in developing their skills as educators, mentors, and advisors as part of their future careers in engineering or related fields. It includes discussion and practice of effective learning design techniques and assessments, an overview of current education research and discussions, an introduction to assets-based approaches to designing learning experiences and opportunities to practice design-based approaches to creating learning experiences.
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.
