Spring 2025

Engineering examples are used as a platform to introduce computer programming skills for problem solving; critical thinking skills.

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.

Analysis of the complex interplay of technical, social, psychological, and environmental factors to address real-world sustainability challenges.

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.

Quantitative aspects of statistical quality control (process control, acceptance sampling). Standardized methodology for root cause identification, solution confirmation and management of quality improvement projects.

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.

Principles of economic analysis and decision analysis for engineering systems, including applications in financing, developing and comparing new technologies.

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.

A logic-based framework for conceptual design, including systematic concept generation and improvement, in innovative product developments.

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

Case-study approach to learning descriptive and predictive data analytical tools for health data. Derive inspiring solutions to real-world problems in various health domain applications.

Study of various aspects of integrated manufacturing and service enterprises including management, design and production functions, interfaces and related resources and information systems.

Application of performance and efficiency tools to streamline hospital operations. Group projects collaborating with hospital staff.

Practice in inventory management, computerized procedures, materials requirements planning. Deterministic and stochastic demand systems with static/dynamic models.

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.

Planning data collection to investigate relationships between product/process design choices (materials, temperatures, etc.) and performance, empirical modeling to predict performance, identification of best design choices.

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.

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

Methods and algorithms for automated text analysis; machine learning; predictive web data analytics; information retrieval; social media data; natural language documents and graphs.

Overview of data management and analytical techniques used in corporate environments and their practical implementation using state-of-the-art Business Intelligence software.

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

Introduction to technologies and disciplines used to collect, integrate, clean and otherwise prepare data into a well-structured, controlled, documented and understood analytic-ready dataset.

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.

Concepts underlying the economic analysis of engineering projects; applications to call and put options; utility theory and mathematical optimizations models; simulation.

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.

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.

Examine principles, strategies, practices and case studies where new technology and innovation are transforming the traditional business paradigms to identify best practices in real-world implementation.

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.

Credit on acceptance of Thesis.

Course content will be selected each semester to reflect current trends and developments in the field of industrial and systems engineering.

Methodologies of planning, conducting, analyzing and interpreting controlled physical or computational experiments for causal inference with broad data science and engineering applications.

Use of analytics to address societal challenges arising in public health, social services and conservation; fairness, interpretability, robustness, optimization, machine learning and causal inference.

Convex sets, convex functions, structures of optimization problems, Lagrangian and conjugate duality. First and second order optimality conditions; applications in engineering and statistics.

Combinatorial optimization, particularly graph problems. Shortest paths, max flow, minimum cost flows, Spanning trees, matroids, submodular functions. Bipartite and general matchings, Polyhedral combinatorics, total unimodularity.

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.

Rational decision making; value and utility functions; distribution theory; invariance relations; copulas; value of information and control; multiattribute utility; decision and game theory; behavioral research.

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.