Graduate Courses

The School of Systems Science and Industrial Engineering (SSIE) at Binghamton University offers graduate courses on a number of topics. You can review the course descriptions below. Please contact the department if you have questions.

• SSIE 500 - Computational Tools

  This course will introduce students to several programming languages and basic programming techniques, with the focus on developing practical code-writing skills for scientific/engineering problem solving. Topics to be covered include: manipulation with numbers, strings, variables, lists, and arrays; creating functions; flow control; data manipulation; imperative, functional, and object-oriented programming; visualization; and presentation. LaTeX will also be introduced for typesetting professional technical documents. This course will also discuss information theory as a sample application area of computational tools. Topics include: information and entropy, mutual information, information coding and compression, Markov information source model, statistical complexity, and computational complexity. Students will write codes in their preferred language to calculate various information theoretic measurements of real-world data. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 501 - Intro to Systems Science

  Includes a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions and historical development of the field; an overview of fundamental systems concepts, principles and laws; and a survey of application areas of systems science and its implications for other fields of study. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 440. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 505 - Applied Probability & Statist.

  Basic concepts in probability and statistics required in the modeling of random processes and uncertainty. Bayes' formula, Bayesian statistics, independent events; random variables and their descriptive statistics; distribution functions; Bernoulli, Binomial, Hypergeometric, Poisson, normal, exponential, gamma, Weibull and multinomial distributions; Chebyshev's theorem; central limit theorem; joint distributions; sampling distributions; point estimation; confidence intervals; student-t, x squared and F distributions; hypothesis testing; contingency tables, goodness of fit, non-parametric statistics, regression and correlation. Prerequisite: one year of calculus. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 510 - Enterprise Systems Engineering

  Global competition is serving as a catalyst for continuous process improvement and the methodical enhancement of system-wide efficiencies. This is true in disciplines ranging from the medical arena and service related systems to manufacturing. The underlying science that contributes to the systematic analysis of complex enterprise-wide systems is the focus of this course. Concepts that can be used in a synergistic manner to enhance an enterprise's efficiency and profitability will be addressed. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 511 - Production & Schedule Control

  This course covers general concepts of production planning and control but goes more into detail of the concepts and mathematics of scheduling applications and theory. Upon completion of the course, the student will be able to apply mathematical techniques in developing solutions to “production scheduling” problems. The “production scheduling” terminology may seem like these techniques are applicable only to manufacturing, however, they have broad applications across many industries and sectors including, but not limited, to healthcare, service industries, food processing, etc. Prerequisites: SSIE 510 or permission of the instructor. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 515 - Operations Mgmt Supply Chains

  This course deals with management of Supply chains, in particular, with the operational aspects. A broad overview of supply chains of a company is introduced, together with performance measures and needed critical success factors. The course concentrates on supplies, inventories, manufacturing, and logistics of distribution. Managerial aspects as well as mathematical modeling for better planning and control will be covered. Enabling the supply chains by enterprise resource planning modules and e-commerce will also be discussed. Prerequisite: SSIE 505 or equivalent. Crosslisted with ISE 415. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 516 - Funds of Energy Sys Analytics

  This course provides fundamental knowledge of the application of Industrial and Systems Engineering in the Energy field. It focuses on the modeling, design, simulation, and optimization of alternative and conventional energy technologies, the societal and environmental impact of technology developments, and the economic benefits of those developments. Recent literature that covers a wide range of ISE topics related to energy systems such as transportation energy, building energy, manufacturing energy, solar energy, wind energy, bioenergy, greenhouse gas emission, and smart grid operations will be discussed in this course. US has recently passed the Inflation Reduction Act that contains the largest-ever energy and climate spending. The contents of this class will give students breadth and depth of knowledge, and an interdisciplinary understanding of the ongoing energy transition. Prerequisites: Graduate Standing. Offered in the fall. Credits: 3 Levels: Graduate, Undergraduate

• SSIE 519 - Applied Soft Computing

  Covers relatively new approaches to machine intelligence known collectively as soft computing. Introduces various types of fuzzy inference systems, neural networks and genetic algorithms, along with several synergistic approaches for combining them as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction, design, control, databases and data mining. The undergraduate students are not required to do projects on the same level as the graduate students, and are not required to place the degree of emphasis on hybrids. Prerequisites: basic knowledge of calculus and discrete mathematics, and competence in at least one programming language, or consent of the instructor. Crosslisted with ISE 419. Offered in the fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 520 - Modeling And Simulation

  Stochastic processes, review of probability and statistics, covariance, input data selection, random number generators, non-parametric tests for randomness, generation of random variates, output data analysis, terminating and non-terminating simulations, model validation, comparison of alternatives, variance reduction techniques, sensitivity analysis, experimental design and predictive models. Prerequisite: SSIE 505 or equivalent. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 522 - Advanced Decision Modeling

  Course provides a broad foundation in decision models and techniques used in industry and research for technical and managerial problems. Topics include decision theory, risk and uncertainty, value of information, preference measurements, prioritization of alternatives, multiple objectives and hierarchical decisions. Prerequisite: SSIE 505 or equivalent. Crosslisted with ISE 422. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 523 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisites: Graduate standing and basic knowledge of calculus, linear algebra and probability theory, or permission of instructor. Crosslisted with ISE 423. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 524A - Grad Sem in Complex Sys Sci

  This course is a weekly seminar series that serves students as a venue of active discussion on current research topics and interdisciplinary networking. It is formed using the Collective Dynamics of Complex Systems seminar series as a structural basis. Faculty, graduate students and external speakers discuss complex systems related research topics. Students are required to present either literature review or their own research at least once a semester and will be graded based on class attendance, presentation and participating in discussions as well as reflection essays. Prerequisites: Graduate standing or permission of instructor. Crosslisted with SSIE 524B. Offered fall and spring semesters. 1 credit. Levels: Graduate, Undergraduate

• SSIE 524B - Grad Sem in Complex Sys Sci

  This course is a weekly seminar series that serves students as a venue of active discussion on current research topics and interdisciplinary networking. It is formed using the Collective Dynamics of Complex Systems seminar series as a structural basis. Faculty, graduate students and external speakers discuss complex systems related research topics. Students are required to present either literature review or their own research at least once a semester and will be graded based on class attendance, presentation and participating in discussions as well as reflection essays and. Prerequisites: Graduate standing or permission of instructor. Crosslisted with SSIE 524A. Offered fall and spring semesters. 1 credit. Levels: Graduate, Undergraduate

• SSIE 525 - Princip Of Systems Engineering

  Basic principles of systems engineering applied in transforming client requirements into an operational system. Topics cover the full system life cycle: planning, integrated product/process development, system architecture and design, modeling, requirements analysis, development, integration, test and evaluation. Specialized concepts involved in engineering complex systems are reinforced through case studies and student exercises. Prerequisite: graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 530 - Healthcare Policy Analysis

  This course provides an overview of issues and policies related to health care systems, with emphasis on health care systems within the United States. The course will apply various engineering tools and approaches for policy analysis and program planning in this area. Some potential future policy changes will also be discussed, as well as challenges with implementation and change management techniques. Crosslisted with ISE 430. Prerequisites: Graduate standing or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 533 - Human Factors Engin And Design

  Introduction to Human Factors and systems: design for human use; Human Factors research methodologies; information about human performance, abilities, and limitations will be surveyed and applied: physical work and manual handling, applied anthropometry and workplace design, human control of systems, control and data entry devices, and environmental conditions; Human Factors applications including human error, accidents, safety, Human Factors and the automobile, and Human Factors in Systems Design. Prerequisite: basic course in probability and statistics or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 534 - Fundamentals of Health Systems

  One of the growing systems in our society is that of the healthcare delivery system. The purpose of this course is to introduce the concepts behind the healthcare delivery systems and to focus upon the systems improvement or continuous improvement techniques available for complex systems. Topics would include improvement to, and problems with: organizational structure, managing change, the financial structure, the responsibility structure, quality data and implications of quality measures, use of clinical decisions support systems and the care givers role in the system. There will also be a focus upon suppliers to the healthcare delivery system and the unique requirements placed upon their products and processes. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 434. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 535X - Agile Eng & Design Thinking

  In this course, the fundamental and advanced concepts of agile engineering and design thinking will be introduced and students will understand, evaluate, and improve upon complex problems that exist in systems, products, processes and services; and they will learn to solve these problems by way of innovative and new system designs. System design, development and delivery methods are evolving rapidly in business today; with faster time to market and delivering a superior end-user experience as primary goals for any system, product or service. In this iterative process, teams will collaborate on solving complex problems to existing and non-existing systems. The course will use Design Thinking practices to discover and define end-user needs; wherein agile system development, engineering and project management practices will be used to deliver on those needs. System dynamics such as the introduction of new technologies; social and environmental issues; diverse and volatile markets, a fluctuating business environment; will be consider as they influence the complex problems and the underlying systems being studied. A comprehensive and concurrent engineering approach will be used to introduce students to leading practice in systems design, development and delivery that result in signature end-user experiences and superior business outcomes. Complex system problems will be analyzed, and practices that can fundamentally change the system structure and behavior will be applied by students. Prerequisites: Graduate standing. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 537 - Ind & Sys Eng in Healthcare

  The application of industrial and systems engineering principles to continuous process improvement in the healthcare domain will be studied. Concepts that will be addressed will include, but not be limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. Prerequisite: graduate standing in the department or permission of the instructor. Crosslisted with ISE 437. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 538 - Healthcare Financial Eng

  This course is intended to provide an overview of healthcare finance and the current financial environment for the healthcare industry and to learn how to apply engineering economics to healthcare financial management. It will provide information on financial and managerial accounting and how the use of Systems Science and Industrial and Systems Engineering principles can be applied to financial management concepts to allow for health-related organizations to make sound business decisions. Crosslisted with ISE 438. Prerequisites: SSIE 534 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 539 - Human Factors Eng Healthcare

  This course introduces and emphasizes the role that human factors engineering/ergonomics plays in healthcare systems, with a focus on its applications to help improve quality, safety, efficiency, and effectiveness of patient care. Focused topics include human factors in workflow models; work system design for patient safety; human error analysis/taxonomies to reduce medical errors; task analysis and data collection methods in healthcare environments; clinical staff workload and patient safety; physical ergonomics in healthcare and human performance modeling; and diffusion and adoption of technology in healthcare, with emphasis on the usability and design of medical devices and information systems. Prerequisite: Basic human factors knowledge or permission of the instructor. Crosslisted with ISE 439. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 548 - Healthcare Data Sci&Analytics

  Data analytics plays an increasingly greater role in healthcare organizations. However, many healthcare professionals lack training in this vital area. This course will cover advanced data analytics with applications in healthcare systems engineering. In specific, the course will cover the concepts and methods of the data analytics in three particular areas: (i) descriptive analytics, which includes data description and aggregation; (ii) predictive analytics, which uses machine learning and statistical modeling for prediction and forecasting; and (iii) prescriptive analytics, which makes use of the results of predictive analysis to optimize the decision making process. All introduced methods will be demonstrated along with a variety of data modalities collected from healthcare applications such as pervasive health, disease diagnosis, fraud detection, and pharmaceutical discovery along with medical decision support systems. Through the provided coursework, students will be able to learn a variety of methodologies on data processing and modeling and make the best use of healthcare data to facilitate medical decision making. Prerequisites: ISE 437/SSIE 537 or consent of the instructor. Crosslisted with ISE 448. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 553 - Operations Research

  Operations research (OR) is devoted to the determination of the best course of action of a decision problem, given resource restrictions. Course provides the engineer with a firm grounding in the use of OR (mathematical) techniques devoted to the modeling and analysis of decision problems. Techniques include the following: decision modeling; linear, integer and dynamic programming; emerging optimization techniques (e.g., genetic algorithms, simulated annealing, etc.); game theory; and queueing theory. Problem areas include the following: transportation models; project/production scheduling; inventory models; assignment problems. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 561 - Quality Assurance For Engineer

  Statistical quality control, designing for quality, process control, vendor and customer quality issues, quality costs and production. Prerequisites: SSIE 505 or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 565 - Engineering Project Management

  Effective Engineering Project Management is critical to business and organizational success. This course will teach students both the technical and the sociocultural dimensions of Engineering Project Management. Students will be thoroughly trained in the following topics: Project Selection, Organization Structure and Culture, Project Definition, Project Time and Cost Estimation, Project Networks, Project Risk Management, Resource Scheduling, Project Crashing, Leadership, Team Management, Outsourcing, Progress Monitoring and Project Evaluation, and Project Closure. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the Critical Path Method with case studies of contemporary engineering projects. Students will learn how to better control financial, physical, and human resources, improve stakeholder relations, shorten development times, lower costs, and increase profit. Offered Fall, Spring, or Summer as needed. 3 credits. Levels: Graduate, Undergraduate

• SSIE 566 - Designing With Experiments

  Basics of applying statistical design, and the design function, statistical experimental design, control of experimental setting, Taguchi methods and analysis of results. Prerequisites: SSIE 505 or equivalents, or permission of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 568X - Flexible & Hybrid Electronics

  Flexible and hybrid electronics integrate ultra-thin silicon semiconductor technology with non-traditional thin skin-like substrates establishing wearable devices for both human and machine. This course will cover design, materials, fabrication methods and evaluation of printed and thinned silicon-based electronics for applications such as smart energy, human health, and industrial sensing. Emphasis will be placed on fundamental understanding of the hard-soft materials interface, advances in materials printing, and approaches to make these systems robust for use. The course will promote understanding of the engineering requirements that lead to systems design for both manufacturing and reliability. Prerequisites: undergraduate degree in the sciences or engineering. 3 credits. Levels: Graduate, Undergraduate

• SSIE 569 - Materials for Manufacturing

  This course provides an overview of the materials used in manufacturing that are of significant economic, technical and industrial relevance. A special emphasis will be given to polymeric materials currently used in electronics manufacturing as well as those materials emerging for use in additive manufacturing and 3D printing. Selected materials will be classified by composition, properties, manufacturing methods, applications, economics and related supply chains. Prerequisites: undergraduate engineering or science background that includes the equivalent of freshman chemistry and physics or permission of the instructor. Crosslisted with ISE 469. 3 credits. Levels: Graduate, Undergraduate

• SSIE 578 - Processes for Electr Mfg.

  The purpose is for the students to gain a broad knowledge and understanding of the basics of printed circuit board manufacturing and assembly. The course offers an introduction to surface mount and insertion mount components, materials and processes as well as to PCB design and manufacturing. Lectures will introduce to assembly process flows and component types, PCB construction and defects, solder paste printing and equipment, placement processes and equipment, reflow and ovens, flip chip assembly and underfilling, defects and mitigation, reliability optimization and testing. Efforts will be made to include visits to local industrial assembly facilities as well as equipment on campus. The overall goal is to provide the students with a basis for communicating and working with subject matter experts. Prerequisite: Graduate standing or consent of department chair. Crosslisted with ISE 473. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 585 - Additive Mfg Processes & Sys

  This course aims to inculcate a comprehensive understanding of additive manufacturing (AM), and will address the following consequential aspects of AM: (1) Materials and Process Mechanics; (2) Machines, Machine Design, and Mechanisms; (3) Quality control, Sensing, and Process Monitoring; and (4) Design for AM and novel trans-disciplinary applications. Prerequisites: Graduate standing or permission of the instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 592 - Tech, Dev, Curriculum I

  This course is a 32-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to a broad review of engineering fundamentals, with emphasis on interdisciplinary topics related to Electronic Systems products and processes, technologies, applications, and problem solving techniques. Coursework includes a team-project and presentation to engineering management. Term offered varies. Variable credit. Levels: Graduate, Undergraduate

• SSIE 593 - Tech Dev Curriculum

  This course is a 16-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to challenging students with problems very similar to those frequently facing Electronic Systems engineers. Coursework includes a technical project requiring the application of systems, software, and hardware engineering skills. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 594 - Industrial Internship

  Industrial engineering, systems science and other professional experience. Daily log book, memo progress reports and a formal final report required. Prerequisite: consent of department chair. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 595 - Termination Project

  Project acceptable both to student and to a faculty committee. Prerequisites: consent of instructor and committee members. Term offered varies. 1-6 credits Levels: Graduate, Undergraduate

• SSIE 598 - MS Termination Project

  In depth study and analysis of a selected topic in health or other service system, or manufacturing system asapproved by the project advisor. Course requires a formal report, defense, and presentation. Term offered varies. 1-6 credits Levels: Graduate, Undergraduate

• SSIE 599 - Thesis

  Training in the methods of research. Oral examination required. Minimum of six credits total. Bound thesis goes in University Libraries and department library. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 605 - Appl'd Multivar. Data Analysis

  Course introduces different multivariate data analysis and modeling tools, which can be used for simultaneously analyzing data with multiple dependent variables. It is designed to emphasize applied methodologies and applications in multivariate data analysis, especially in engineering fields. Topics to be covered include: multivariate regression, logistic regression, multivariate analysis of variance (MANOVA), principal components analysis, cluster analysis, canonical correlation, factor analysis, and discriminant analysis. The effective use of advanced data analysis software, such as SAS, for solving real-world engineering problems will also be addressed. Prerequisite: SSIE 505 or its equivalent. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 612 - Adv. Topics In Integrated Mfg.

  The continual need to improve quality and productivity and remain competitive in a global market requires the comprehensive integration of people, equipment, computers and information within a manufacturing systems engineering framework. Course studies manufacturing integration issues with a special focus on integrating elements such as process planning, group technology, concurrent engineering, product quality, cost analysis, flexible manufacturing, inventory control, information flow and management, and global computer-integrated manufacturing (CIM) concept. Prerequisite: SSIE 512 or equivalent or permission of instructor. Term offered varies. 3 credits. Levels: Graduate

• SSIE 613 - Adv Stats Method for Dat Analy

  This course will cover advanced data analysis methods with emphasis of their applications in industrial and systems engineering. There are three major components to the course. The first component is a brief review on basic univariate and multivariate analysis. The second component is an introduction to advanced statistical analysis methodologies such as time series modeling and forecasting, confirmatory factor analysis, multi-group discriminant analysis, multivariate analysis of variance, and structural equation modeling. The third component of the course focuses on real applications of these methods in research and data-driven decision making. Prerequisite: SSIE 505 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 615 - Advanced Supply Chains

  Course deals with modeling of supply chains, concentrating on production and operations. Quantitative models will be developed and analyzed to study the benefits of information sharing, joint planning, and coordination among the various components of a supply chain. Strategic uses of information and various strategies of supply chains, like appropriate contacts, component commonality, and postponement will be covered. The material will also include service industry, and designing and managing globally dispersed entities. A major activity would be students working in teams, and identifying relevant problems and developing models to study them. Prerequisite: SSIE 515, and/or SSIE 520 or permission of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 616 - Adv Topics Applied Soft Compu

  Course is designed to follow a currently offered course, SSIE 519: Applied Soft Computing. Both courses are designed to cover relatively new approaches to machine intelligence and systems analysis known collectively as soft computing. The 519 course already introduces various types of fuzzy inference systems, neural networks, and genetic algorithms, along with several synergistic approaches for combining them, including "neuro and fuzzy" techniques, neuro-fuzzy models, the use of neural models in fuzzy systems design, genetic auto-tuning techniques, genetic training of neural nets, fuzzified neural nets, and neural genetic fuzzy models. Naturally, with so many new approaches developing in this field, it is possible in an entry-level graduate course only to cover the main topics in depth and to offer only a general overview on the more advanced hybrid approaches. The purpose of SSIE 616 is to allow students to pursue these advanced approaches to a much greater depth. The emphasis will be on applications, including modeling, prediction, design, control, databases, and data mining, just as is already the case in the 519 course. Prerequisite: SSIE 519. Offered in the Spring semester. 3 credits. Levels: Graduate

• SSIE 617 - Fuz. Sets, Fuz.Logic & Fuz.Sys

  Course consists of two parts. The first part covers fundamentals of fuzzy set theory and the associated fuzzy logic. The second part is devoted to applications of the theory. Topics of the theoretical part include basic concepts of fuzzy set theory and fuzzy logic; representations of fuzzy sets; extension principle that facilitates fuzzifications of classical mathematical concepts; aggregation operations on fuzzy sets; the concept of a fuzzy number and arithmetic operations on fuzzy numbers; fuzzy relations; fuzzy relation equations; basic ideas of fuzzy logic; possibility theory based on fuzzy sets; and information aspects of fuzzy sets. In the application part, methods of constructing fuzzy sets in various application contexts are overviewed and representative applications of fuzzy sets and fuzzy logic are examined. The application areas covered include systems science; approximate reasoning in expert systems; database and information retrieval systems; pattern recognition and image processing; decision making; medicine; economics; psychology; and various areas of engineering. Prerequisites: SSIE 505 or equivalent and calculus and discrete mathematics, or permission of instructor. 3 credits Levels: Graduate, Undergraduate

• SSIE 631 - Foundations Of Neural Networks

  Covers theory and practical applications of artificial neural networks. Neural networks are a broad class of computing mechanisms with active research in many disciplines, including all types of engineering, physics, psychology, biology, mathematics, business, medicine and computer science. Emphasizes the practical use of neural networks for industrial problems such as pattern recognition, predictive models, pattern classification, optimization and clustering. Topics include learning rules, paradigms and validation. Prerequisites: SSIE 505 or equivalent and SSIE 520, and knowledge of at least one programming language. Term offered varies. 3 credits. Levels: Graduate

• SSIE 633 - Advanced Human Factors

  This course is intended as an advanced course in human factors/ergonomics engineering. The course is project/research oriented in nature to provide the graduate students a foundation from which they can explore areas of their own interests. Focused topics include fundamentals and dynamics of human performance modeling, particularly using digital humans; advanced human factors research and development, including ethics, methods, and analysis tools; human-machine systems modeling and design; human reliability analysis; adaptive hybrid systems; and control theory for humans. Application areas will include quality/process control in manufacturing, healthcare, transportation, aviation, and military systems. Prerequisite: SSIE 533 (or equivalent) or permission of the instructor. Offered in the Spring semester. 3 credits. Levels: Graduate

• SSIE 637 - Advanced Topics in Health Syst

  This course is intended as an advanced course in health systems and health care delivery. This course is oriented to provide the graduate students with an in-depth study of the application of industrial and system engineering principles for continuous process improvement in the health care. Concepts that are addressed and studied include, but are not limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. SSIE 537 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate

• SSIE 641 - Adv Topics in Network Science

  This course provides concepts, models, methods and tools developed in the rapidly advancing field of Network Science. Instructions will be largely based on primary literature published recently. Topics to be discussed will include: Complex network topologies, methods for network analysis, visualization and simulation, models of dynamical/adaptive networks, techniques for mathematical analysis, network stability and robustness, and applications to social, biological and engineering systems. Prerequisites: SSIE 523 or permission of the instructor. Students taking this course should have solid knowledge of linear algebra, probability and statistics, and differential equations. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 643 - Advanced Engineering Analytics

  The explosion of wireless sensing, smart devices as well as advanced communication techniques have generated the vast amount of big data. While data are the foundation of the Digital Age, Data Analytics is imperative to organize, analyze and interpret the valuable information underlying the massive data. This course provides an overview of advanced analytics algorithms, with applications across various engineering domains, including manufacturing, healthcare, and smart energy. The major topics include model assessment and selection, advanced regression (ridge/Lasso, mixed effects, nonparametric), support vector machine and kernel methods, ensemble learning (stochastic gradient boosting), dimension reduction (subspace learning, non-negative matrix factorization, functional PCA), anomaly detection (unsupervised, high-dimension data), data visualization (multi-dimensional scaling and manifold learning). Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 644 - Found Of Adaptive Optimization

  This course is a survey of the newer, most common adaptive search methods. This is a project- and research-oriented course designed to give graduate students a foundation from which to explore areas of their own interest. Focused topics include simulated annealing, genetic algorithms, evolution strategies, tabu search, ant colony methods, and particle swarm optimization. Other search methods such as genetic programming, evolutionary programming and random search methods will be briefly covered. Major emphasis is on NP complete combinatorial problems found in engineering. Issues such as solution encodings, stochastic convergence, selection methods, local and global search methods are discussed. Prerequisite: SSIE 505 or equivalent, SSIE 520, and knowledge of at least one programming language. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 650 - Systems Optimization

  Broad spectrum of models and methods for systems optimization. Motivating examples; classical constrained and unconstrained methods; search techniques; linear programming; network and transportation systems; introduction to integer programming. Prerequisite: SSIE 550, SSIE 553 or consent of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 660 - Stochastic Systems

  Advanced topics in conditional probability, discrete-state Markov chains, exponential and Poisson processes, Erlang processes, basic queueing models and several important variations, congestion problems in banks, airline reservation systems, waiting lines in machine job shops, problems encountered on design and analysis of multiprogramming and timesharing computer systems, renewal theory, continuous transition Markov chains, introduction to system reliability. Modeling and methodology. Emphasis on application. Prerequisite: SSIE 505 or equivalent. 3 credits Levels: Graduate

• SSIE 661 - Advanced Issues In Quality

  The topic of quality has taken more and more of a critical nature for manufacturing systems. Course has two components. The first is a practical application of the concepts of quality, including the design and execution of experiments in a real setting. The second is the analysis and study of future issues in the field of quality, such as the development of loss equations, cost of high quality, and people and high quality. Prerequisite: SSIE 561 or a general design of experiments course. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 664 - Advanced Eng Management

  This course is specifically designed for engineering management students who are interested in advanced concepts related to leadership and management in highly technical and complex teams, departments, and organizations. Students will gain an advanced understanding of the engineering and management skills needed to excel in their future positions, create new opportunities, and grow as respected, competent engineering leaders, strategic planners, policy makers and engineers in today's complex systems and global economy. Also underlined are discussions of advanced engineering leadership attributes, steps to acquire these attributes, and strategies to develop core competencies. The course is organized into four major sections: Introduction to Management Challenges for Engineers, Functions of Engineering Management, Business Essentials for Engineering Managers, and Engineering Management in the New Millennium. Course contents are designed to achieve the T-shaped competencies with both broadbased perspectives and in-depth analytical skills. Such a background is viewed as essential for advanced engineering professionals and managers to exert a strong leadership role in the dynamic and challenging marketplace. This course also covers the latest research and development in the engineering management literature. A significant component of this course requires students to conduct and complete comprehensive literature reviews and detailed project case studies. Prerequisites: SSIE 564X Engineering Project Management. Term offered varies. 3 credits. . Levels: Graduate, Undergraduate

• SSIE 671 - Integer Programming

  This course aims to provide a unifying exposition of mathematical theory and algorithms to solve integer linear programs, application of which includes scheduling, production planning, network optimization, transportation and assignment problems, etc. In particular, Integer Programming (IP) is a requisite when tackling problems involving a number of interrelated “yes-or-no” questions, which is of great importance in decision making optimization. The course will cover various topics such as 1) Introduction to Integer Programming, 2) Simplex and Beale Tableaus, 3) Cutting Plane Techniques, 4) Dual Fractional MILP Cut, 5) Dual All–Integer Integer Programming, 6) Primal All–Integer Integer Programming, and 7) Branch and Bound Enumeration. Prerequisites: SSIE 553 (Operations Research), or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 673 - Adv Iss In Proc For Electr Pkg

  Addresses advanced topics within the processes associated with the manufacture and assembly of printed circuit boards. Addresses advanced issues in bare-board manufacture, including printed circuit board structures and processes, interconnection technology-related considerations, materials and processes used in bare-board manufacture, and reliability issues. The impact of board-pad metallurgies and surface coating on the solder joint is addressed. In the assembly area, the focus is on advanced topics in the surface mount printed circuit board assembly area. Ultrafine pitch assembly and area array assembly are addressed. Topics include solder paste deposition, component and materials issues, solderability, placement, reflow, process yield related issues, the impact of moisture, encapsulation and product reliability. Concepts such as concurrent engineering and design for manufacturing are also addressed. On completion, students have an acceptable understanding of the advanced scientific and/or engineering concepts that relate to the manufacture and assembly of surface mount printed circuit boards. Assists engineers in scientific problem solving and process development for the printed circuit board assembly domain. Prerequisite: SSIE 578 or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 697 - Independent Study

  Supervised by department faculty member. Student must obtain consent of instructor, who then determines description of program, number of credits, frequency of meeting and location. Appropriate paperwork must be submitted to complete registration. Term offered varies. Credits vary. Levels: Graduate

• SSIE 698 - Predissertation Research

  Exploratory research oriented toward PhD dissertation. Term offered varies. Credits vary. Levels: Graduate

• SSIE 699 - Dissertation

  Research for and preparation of PhD dissertation. Term offered varies. Credits vary. Levels: Graduate

• SSIE 700 - Continuous Registration

  Required to maintain matriculation through any spring or fall semester when no other courses are taken. If minimal one-credit registration is not maintained, student must reapply for admission. Term offered varies. Typically 1 credit. Levels: Graduate

• SSIE 701 - Pract/Teaching &Research Asst

  Required for all funded graduate assistants. Research or teaching supervised by faculty advisor. Term offered varies. Credits vary Levels: Graduate

• SSIE 707 - Research Skills

  Development of research skills required within graduate program. May not be applied toward course credits for any graduate degree. Term offered varies. Credits vary. Levels: Graduate