Fall 2025

A combination of plant tours, laboratory experiences, and lecture are used to introduce the philosophy, subject matter, aims, goals, and techniques of industrial and systems engineering.

Techniques for modeling uncertainty: discrete and continuous random variables, probability distributions, expectations and conditional probability; Poisson processes, Markov chains and queuing decision models.

Sampling distributions; confidence intervals and hypothesis testing; analysis of variance; regression; data aggregation; prediction and classification.

Managing a project intended to create an engineered system. Methods, processes, tools. Lessons-learned from actual practice. Case studies from real projects. Presentations by student teams.

Modeling, solution analysis and applications of complex decision problems using linear, dynamic and integer optimization.

Methodology to store, organize, cleanse, harmonize and stage enterprise data for analytics. Report, visualize, slice and dice, forecast and predict trends.

Systems as complex collaborative ensembles of interconnected components. Theory and practice of requirements, design, implementation, testing, deployment, operation and disposal. Case studies from real projects.

Role of humans in technological systems; human-systems integration; ergonomics; anthropometry and workstation design; work methods and measurement; human error, safety and accident analysis.

Data models for industry applications. Modeling and designing robust databases. Implementing and querying databases with SQL. Innovations in database applications.

Production, resource and service planning, forecasting, scheduling, vendor and location selection, inventory; integrated decision systems for the control of production, services and supply chains.

Modeling of complex discrete event systems, input and output analysis, system evaluation via experimentation on the simulation model, applications to industrial and systems engineering problems.

Design, modeling and analysis of complex systems, challenges they present and methods for resolving those challenges. Impact of technology on work and organizational design.

Basic principles that govern the dynamic socio-technical interactions of open technology innovations on competitive global markets, emphasizing real-world practices to uncover blue-ocean innovation opportunities.

Individual research and readings. Not available for graduate credit.

Investigate client organization operations, evaluate alternative designs to meet client needs, complete preliminary design review.

Develop the detailed design including countermeasures for risks, complete critical design review, implement the project plan, close-out the project.

Framing engineering management situations with statistical methods. Experiments, distributions, regression, ANOVA, hypothesis, introduction to Bayesian analysis.

Fundamental accounting principles, financial analysis and financial control, strategic management and a review of alternative project delivery methods.

Strategies to improve hospital operations, including supply chains, staffing, informatics, data analytics and patient flow.

Theories of management and their application to software projects. Economic analysis of software products and processes. Software cost and schedule estimation, planning and control.

Dependencies among long, intermediate and short-range planning and scheduling of production systems considering objectives, technological constraints and solution algorithms.

Methods, processes and tools for managing projects to create engineered systems. Project management; engineering economics; risk management. Industry-drawn large-scale engineering case studies. Team project presentations.

Principles of quality management, quality philosophies and frameworks, role of leadership and strategic planning to improve quality, process management, measurements to improve quality and performance.

Supervised learning. Linear regression, cross validation, ridge and lasso regression, logistic regression, k-nearest-neighbors, decision trees, random forest and gradient-boosting models, support vector machines, neural networks.

Fundamentals of Linear Programming, Integer Programming, quadratic and nonlinear programming for analytics and data science problems.

Consulting project concepts, frameworks, analytical tools and managerial skills with a focus on the use of data analytics, design thinking and insight-driven frameworks.

Data preprocessing, data cleaning, data summarization, dimension reduction, data visualization, anomaly detection, classification and clustering. Modeling dependencies using association rules.

Linear programming models for resource allocation; simplex and revised simplex methods; duality; sensitivity; transportation problems; selected extensions to large scale, multiobjective and special structured models.

Modern portfolio theory and optimal decision-making problems in finance. Market microstructure and high-frequency trading. Machine learning models for quantitative finance.

Stochastic processes: conditional expectation, Poisson process, discrete time Markov chains, continuous time Markov chains with queueing applications, non-Markovian queues and reliability.

Project-based course covers developing scalable analytics solutions using major cloud platforms, focusing on data management, machine learning and enterprise applications.

Design and management of engineering teams. Group decision-making, motivation, leadership, infrastructure requirements, performance measurement, team diversity, conflict and integration.

Principles and practices of technology development and implementation, with application to products and systems in manufacturing and services.

Modern developments in data representation/analysis, nonconvex optimization, high-dimensional probability, sketching, clustering, low-rank models, inverse problems, applications in imaging, machine learning, computer vision and neuroscience.

Essentials of data engineering, focusing on data architecture, data warehousing, ETL processes, big data technologies, and data quality.

Explores fundamental techniques such as recursion, Fourier transform ordering, dynamic programming for efficient algorithm construction. Examples include arithmetic, algebraic, graph, pattern matching, sorting, searching algorithms.

Economic evaluations of engineering systems for both government and private industry; quantitative techniques for evaluating non-monetary consequences; formal treatment of risk and uncertainty.

Decisions under uncertainty; utility theory; multiattribute utility; probability elicitation; conjugate distributions; decision analysis applications, framing, modeling using spreadsheets or software; cognitive biases, behavioral decision theory.

Statistical methods for building intelligent and adaptive systems that improve performance from experiences; Focus on theoretical understanding of these methods and their computational implications. Recommended preparation: Undergraduate level training or coursework in linear algebra, multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required.

Concepts and methods to analyze the environmental impacts of industrial systems, including lifecycle assessment, material flow analysis, design for environment and sustainable consumption.

Introduction to modeling and analysis of stochastic systems, with an emphasis on discrete-event simulation of non-Markovian systems.

The role enterprise resource planning systems (ERPs) play in organization; integrating and coordinating the many business processes such as demand planning, procurement, production and sales.

Concepts of risk analysis, risk in engineered systems, environmental risk, security risks; fault trees, event trees, risk simulations; risks and decision-making.

Research leading to the master's degree; maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Stochastic processes, conditional expectation, Poisson processes, renewal processes, regenerative processes, and discrete and continuous time Markov chains.

Stochastic processes, reversed continuous-time Markov chains, coupling, martingales, random walks, Brownian motion, stochastic order relations and probability inequalities.

Understanding optimization problems with constraints and extensions to the treatment of problems involving nonlinearity, uncertainty, discrete variables, logical constraints, multi-agent non-cooperative games and hierarchical problems.

Large-scale optimization algorithms that arise in machine learning: stochastic optimization, accelerated methods, parallelization, nonsmooth optimization, online optimization, variance reduction, min-max games.

Current research, guest speakers in the field; review papers; guidance in preparing research proposals and special projects. Duplicates credit in the former ISE-650abc. Open only to Fifth Year Seniors and Master’s Students.

Fundamental techniques for design and analysis of algorithms. Dynamic programming; network flows; theory of NP-completeness; linear programming; approximation, randomized, and online algorithms; basic cryptography. Prerequisite: CSCI 570. Recommended preparation: familiarity with algorithms and discrete mathematics. Open only to doctoral students.

Research leading to the doctorate. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.