Probability With Applications

Topics include conditional probability, density and distribution functions from engineering, expectation, conditional expectation, laws of large numbers, central limit theorem, and introduction to Poisson Processes.
Credits: 3
Lecture: 3

Engineering Economy

Introduction to engineering economic decision making, economic decision criteria, discounted cash flow, replacement and timing decisions, risk, depreciation, and income tax.
Credits: 1
Lecture: 1

Basic Statistical Methods

Point and interval estimation of systems parameters, statistical decision making about differences in system parameters, analysis and modeling of relationships between variables.
Credits: 3
Lecture: 3

Methods Quality Improvement

Topics include quality system requirements, designed experiments, process capability analysis, measurement capability, statistical process control, and acceptance sampling plans.
Credits: 3
Lecture: 3

Simulation Analysis & Design

Discrete event simulation methodology emphasizing the statistical basis for simulation modeling and analysis. Overview of computer languages and simulation design applied to various industrial situations.
Credits: 3
Lecture: 3

Supply Chain Modeling: Logistics

Course focuses on engineering design concepts and optimization models for logistics decision making in three modules: supply chain design, planning and execution, and transportation.
Credits: 3
Lecture: 3

Supply Chain Modeling: Manufacturing &Warehousing

Warehousing Design and operation of manufacturing and warehousing facilities.
Credits: 3
Lecture: 3

Cornerstone Design

Structure a complex problem through information gathering, data analysis, industrial engineering and design principles application, project management, while working in and leading diverse teams.

Engineering Optimization

Topics include modeling with networks and graphs; linear, nonlinear, and integer programming; construction of models employing modern modeling languages; and general solution strategies.
Credits: 3
Lecture: 3

Stochastic Manufacturing & Service Systems

Methods for describing stochastic movements of material in manufacturing facilities, supply chain, and equipment maintenance networks. Includes analysis of congestion, delays, and inventory ordering policies.
Credits: 3
Lecture: 3

Statistics & Applications

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression, and analysis of variance. Crosslisted with MATH 3770 and CEE 3770.

Note: ISyE 3770 is not for IE students

Regression and Forecasting

The regression and forecasting models and their applications in various fields of science and engineering.
Credits: 3
Lecture: 3

Decision and Data Analysis

Integrate decision and data analytics together to solve real-world business problems. Hands-on system modeling, data collection and analysis, and reporting writing projects.
Credits: 3
Lecture: 3

Simulation Analysis and Design

Advanced modeling and statistical concepts in discrete-event simulations; Monte Carlo simulation; estimation of error and risk; advanced input modeling techniques; comparison and optimization of systems.
Credits: 3
Lecture: 3

Senior Design

Senior ISYE design project requiring students to formulate project plan with off campus enterprise. Includes specific milestones, targets, and evaluation criteria.
Credits: 4
Lecture: 4

Advanced Logistics

Covers the complicated nature of practical logistics problems, and how these problems can be attacked with industrial engineering tools.
Credits: 3
Lecture: 3

Workflow Design for Manufacturing and Service

Introduction to the modeling, analysis and design of workflow systems in manufacturing systems and service systems such as healthcare systems, banks, service centers and warehouses.

Advanced Modeling and Analysis of Workflow Systems

This course will introduce the students to an in-depth modeling and analysis of the workflow dynamics that shape the operation of a broad range of modern applications, ranging from the contemporary production systems to health-care facilities. More specifically, in its primary developments, the course material will be motivated and presented primarily through applications drawn from the high-volume discrete-part manufacturing domain, for specificity and concreteness. But through additional examples, homework and exam problems, it will be further demonstrated how the presented models and methods are also applicable in many other application domains involving a structured workflow, like various logistics and health care operations, and even the broader service industry.
 

An important attribute of the course is that it does not intend to teach the students only the various models and computational methods that are covered in it, but also the thinking processes and the broader modeling and analytical methodology that underlie the development of the presented results. Developing these perspectives and the corresponding skills is important for an effective
application of the course material, and even the adaptation and extension of this material in order to address various nuances and intricacies in those applications that will be encountered by the students in their professional life. The ongoing advent of Generative Artificial Intelligence (AI) and the proliferation of the various Large Language Models (LLMs) render this more profound understanding of the presented models and methods even more imperative, as the students eventually will be called to navigate an effectively exploit all the corresponding information and capability that is provided by these platforms.

Advanced Optimization

Theory and implementation of practical methods to find good or optimal solutions to optimization problems too large or complex to solve in a straightforward way.
Credits: 3
Lecture: 3

Constraint Programming

This course is an introduction to constraint programming, from its modeling language to its computational methodology and its applications to scheduling, routing, and resource allocation.

OPTIMIZATION FOUNDATIONS FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Continuous optimization models and algorithms are at the heart of many problems of Data Science, Machine Learning, AI, signal processing, economics and other modern contexts. This course introduces foundations of optimization models and algorithms as applied in these settings. Topics include unconstrained optimization, gradient descent and its variants, Newton method, convergence of algorithms, parameter tuning, line-search, stochastic gradient descent and its variants, empirical risk minimization, constrained optimization, duality, convexity, Lagrange duality. We will use examples from linear regression, Lasso regression, logistic regression, support vector machines and neural networks.

Advanced Stochastic Systems

Jackson Networks and Markov Decision Processes with applications to production/inventory systems, customer contact centers, revenue management, and health care.
Credits: 3
Lecture: 3

Supply Chain Economics

Covers include pricing models; revenue management; gaming and equilibrium; principal agent models; auctions; supply chain coordination strategies; and value of information.
Credits: 3
Lecture: 3

Capital Investment Analysis

core concepts and techniques for economic decision analysis of complex capital investment problems that involve dimensions of time, uncertainty and strategy. Students are also introduced to basic terminology, concepts and issues relevant to financial engineering, financial management and corporate finance.
Credits: 3
Lecture: 3

Energy, Efficiency and Sustainability

Analysis and modeling of energy production and use, material and energy efficiency, sustainability, and cost for systems, products, and services.
Credits: 3
Lecture: 3

PUBLIC HEALTH SYSTEMS

Health systems, public health, and medicine have evolved rapidly over the past several decades, driven by advances in data availability, analytics, and technology. These developments create substantial opportunities to improve public health outcomes, support disease prevention, early detection, and treatment, and promote healthier lifestyles at both individual and population levels. Quantitative methods play an increasingly important role in informing complex decision-making in public health and healthcare systems, particularly when resources are limited and trade-offs must be carefully evaluated. However, many decisions in practice continue to rely on experience- or case-based approaches, without fully leveraging modern quantitative decision-making tools. This course provides an overview of key components of health systems and public health, with an emphasis on how quantitative and analytical methods can improve decision quality, enhance efficiency and effectiveness, and ultimately support better health outcomes.

Methods and Applications of Machine Learning

This course introduces the available methods in machine learning, which methods are better in what situations, objective functions and errors, and how to tune parameters.

Online Learning and Decision Making

Fundamental theoretical tools for analyzing online methods, algorithmic techniques for developing computationally efficient methods, applications to real-world problems, and discussion around ethical and legal issues.

Foundations of Modern Data Science

This course develops the mathematical foundations of data science, introducing students to the life cycle of data-driven decision-making based on principles of probabilistic modeling, optimization-based model-fitting, and statistical inference. Topics include frequentist and Bayesian paradigms of data science, low-dimensional parameter estimation and confidence intervals, inference and uncertainty quantification in linear and nonlinear regression, high-dimensional models and fundamental limits of inference, high-dimensional stochastic optimization problems, and multiple hypothesis testing. Time permitting, the course will also cover some basic causal inference and robust data analysis algorithms. The focus will be on looking under the hood of modern data science methodology. While there will be some exposure to implementing these methods on real data, the primary focus will be on understanding these methods and developing judgment for them.

Research Assistantship

Independent research conducted under the guidance of a faculty member
Credits: 1 to 12
Lecture:

Undergraduate Research

Independent research conducted under the guidance of a faculty member
Credits: 1 to 12
Lecture:

Biologically Inspired Design (Cross Listed with BIOL/ME/MSE/PTFE)

We examine evolutionary adaptation as a source for engineering design inspiration, utilizing principles of scaling, adaptability, and robust multi-functionality that characterize biological systems.
Credits: 3
Lecture:

Special Topics

Courses in special topics of timely interest to the profession, conducted by resident or visiting faculty.
Credits: 3
Lecture: 3