Industrial Engineering and Operations Research

CEOR E4011 INFRASTRUCTURE SYSTEMS OPTIMIZATION. 3.00 points.

Lect.: 3.

Prerequisites: Basic linear algebra. Basic probability and statistics.
Engineering economic concepts. Basic spreadsheet analysis and programming skills. Subject to instructor's permission. Infrastructure design and systems concepts, analysis, and design under competing/conflicting objectives, transportation network models, traffic assignments, optimization, and the simplex algorithm

CSOR E4231 ANALYSIS OF ALGORITHMS I. 3.00 points.

Prerequisites: (COMS W3134 and COMS W3136) or (COMS W3137 and COMS W3203)

Introduction to the design and analysis of efficient algorithms. Topics include models of computation, efficient sorting and searching, algorithms for algebraic problems, graph algorithms, dynamic programming, probabilistic methods, approximation algorithms, and NP-completeness.

CSOR W4246 ALGORITHMS FOR DATA SCIENCE. 3.00 points.

Prerequisites: COMS W1007 Basic knowledge in programming (e.g. at the level of COMS W1007), a basic grounding in calculus and linear algebra.
Corequisites: COMS W4121

Methods for organizing data, e.g. hashing, trees, queues, lists,priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.

DROM B8000 OPTIMIZATION ＆ SIMULATION BOOTCAMP. 0.00 points.

DROM B8105 HEALTHCARE ANALYTICS. 1.50 point.

Course Description: This course will introduce students to the core concepts of health analytics, the types of questions addressed by healthcare analytics departments, and a broad overview of the most common analysis techniques

DROM B8106 Operations Strategy. 3.00 points.

Prerequisites: DROM B5101 OR DROM B6101
Operations Strategy

DROM B8107 Service Operations Management. 3.00 points.

Prerequisites: DROM B6102 OR DROM B5102

DROM B8116 Risk Management. 3.00 points.

DROM B8123 Demand Analytics. 3.00 points.

Lect: 3.Not offered during 2024-2025 academic year.

Tools to efficiently manage supply and demand networks. Topics include service and inventory trade-offs, stock allocation, pricing, markdown management and contracts, timely product distribution to market while avoiding excess inventory, allocating adequate resources to the most profitable products and selling the right product to the right customer at the right price and at the right time. 

DROM B8131 Sports Analytics. 3.00 points.

Sports analytics refers to the use of data and quantitative methods to measure performance and make decisions to gain advantage in the competitive sports arena. This course builds on the Business Analytics core course and is designed to help students to develop and apply analytical skills that are useful in business, using sports as the application area. These skills include critical thinking, mathematical modeling, statistical analysis, predictive analytics, game theory, optimization and simulation. These skills will be applied to sports in this course, but are equally useful in many areas of business.There will be three main topics in the course: (1) measuring and predicting player and team performance, (2) decision-making and strategy in sports, and (3) fantasy sports and sports betting. Typical questions addressed in sports analytics include: How to rank players or teams? How to predict future performance of players or teams? How much is a player on a team worth? How likely are extreme performances, i.e., streaks? Are there hot-hands in sports performances? Which decision is more likely to lead to a win (e.g., attempt a stolen base or not in baseball, punt or go for it on fourth down in football, dump and chase or not in hockey, pull the goalie or not in hockey)? How to form lineups in daily fantasy sports? How to manage money in sports betting? How to analyze various ``prop'' bets?The main sports discussed in the course will be baseball, football, basketball, hockey, and golf. Soccer, tennis, and other sports will be briefly discussed. Students are welcome to pursue any sport in more detail (e.g., cricket, rugby, auto racing, horse racing, Australian rules football, skiiing, track and field, or even card games such as blackjack, poker, etc.) in a project. Class sessions will involve a mixture of current events, lecture, discussion, and hands-on analysis with computers in class. Each session will typically address a question from a sport using an important analytical idea (e.g., mean reversion) together with a mathematical technique (e.g., regression). Because of the "laboratory" nature of part of the sessions, students should bring their laptops to each class

DROM B8153 People Analytics: Creating Business Value from Data. 1.50 point.

DROM B8510 MANAGERIAL NEGOTIATIONS. 3.00 points.

DROM B8816 Quantitative Pricing ＆ Revenue. 1.50 point.

Prerequisites: Familiar with the content covered in the managerial statistics, business analytics, managerial economics and marketing core courses. Knowledge in managerial statistics, probability, probability distributions, expected value calculations, etc
Quantitative pricing and revenue analytics collectively refers to the set of practices and tools that firms in various industries use to quantitatively model consumer preferences, segment their market, and tactically optimize (often in micro targeted or personalized manner) their product assortment, pricing, and promotion strategies. The origins of this field, often referred to as revenue management as it is also called, are in the airline industry during the late 80s. The prototypical question is how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximize its profitability. In the airline industry, as most of us know, tickets for the same flight may be sold at many different fares, the availability of which is changing as a function of purchase restrictions, the forecasted future demand, and the number of unsold seats. The adoption of such systems has transformed the transportation and hospitality industries, and is increasingly important in retail, telecommunications, entertainment, financial services, health care, manufacturing, as well as on-line advertising, online retailing, and online markets. In parallel, pricing and revenue optimization has become a rapidly expanding practice in consulting services, and a growing area of software and IT development. We will be doing a hands-on dive into the above tools in the context of 2-3 case studies and datasets, in conjunction with lectures to set the stage. The case studies will cover markdown pricing for a retailer, demand and inventory data for a self-storage company, customer research data of a mortgage lender, and peak load pricing data for a highway toll booth.Through this course, students will be able to model and identify opportunities for revenue optimization in different business contexts. As the ensuing outline reveals, most of the topics covered in the course are either directly or indirectly related to customer segmentation, demand modeling, and tactical price optimization.TextbookOne recommended book for the course is by Robert Phillips titled "Pricing and Revenue Optimization. This will primarily be done in teams, much of it in class, and with the help of the TA(s) and the professor. Sample code will be shared for various parts of these analyses. Course deliverables align. Apart from class participation (30％ of the total grade), the other course deliverables consist of a set of in-class (homework) assignments (40％) and a take-home final exam (30％).Class participation: I will routinely ask that you review some material, or watch some short recor

EEOR E4650 CONVEX OPTIMIZATION FOR ENG. 3.00 points.

Lect: 3.

Prerequisites: (ELEN E3801) or ELEN E3801; or instructor's permission.
Theory of convex optimization; numerical algorithms; applications in circuits, communications, control, signal processing and power systems

EEOR E6616 CONVEX OPTIMIZATION. 3.00 points.

Lect: 2.5.

Prerequisites: (IEOR E6613) and (EEOR E4650) EEOR E4650 AND IEOR E6613
Convex sets and functions, and operations preserving convexity. Convex optimization problems. Convex duality. Applications of convex optimization problems ranging from signal processing and information theory to revenue management. Convex optimization in Banach spaces. Algorithms for solving constrained convex optimization problems

ENGI E4300 Design Justice: Human-Centered Design and Social Justice. 3.00 points.

Prerequisites: IEME E4200 or COMS W4170 Or instructor's permission.

Introduction to Human-Centered Design and Innovation. Unpack the role of design in the market economy for an individual consumer, for a designer/developer and for an enterprise or other organization. Consider how designing in good faith for most can lead to injustice for some. Examine how - to use the PROP framework of the Columbia School of Social Work[1] - power, race, oppression, and privilege can be executed through the design process. Equip students with tools to engage in the design process and to facilitate the engagement of others. Explore strategies for guiding design and innovation towards more just solutions. [1] https://socialwork.columbia.edu/about/dei/dei-mission-statement/

IEME E4200 HUMAN-CENTERED DESIGN AND INNOVATION. 3.00 points.

Prerequisites: Instructor's permission.
Open to SEAS graduate and advanced undergraduate students, Business School, and GSAPP. Students from other schools may apply. Fast-paced introduction to human-centered design. Students learn the vocabulary of design methods, understanding of design process. Small group projects to create prototypes. Design of simple product, more complex systems of products and services, and design of business

IEME E4810 INTRO-HUMANS IN SPACE FLIGHT. 3.00 points.

Prerequisites: Department permission and knowledge of MATLAB or equivalent.
Introduction to human spaceflight from a systems engineering perspective. Historical and current space programs and spacecraft. Motivation, cost, and rationale for human space exploration. Overview of space environment needed to sustain human life and health, including physiological and psychological concerns in space habitat. Astronaut selection and training processes, spacewalking, robotics, mission operations, and future program directions. Systems integration for successful operation of a spacecraft. Highlights from current events and space research, Space Shuttle, Hubble Space Telescope, and International Space Station (ISS). Includes a design project to assist International Space Station astronauts

IEOR E1000 Frontiers in Operations Research and Data Analytics. 1.00 point.

Introductory course for overview of modern approaches and ideas of operations research and data analytics. Through a series of interactive sessions, students engage in activities exploring OR topics with various faculty members from the IEOR department

IEOR E2000 Data Engineering with Python. 3.00 points.

Prerequisites: ENGI E1006; Or competency in Python.
Introduction to essential data engineering methods. Potential topics include Arrays, Linked Lists, Stacks and Queues, Trees and Graphs, Hash Tables, Search Algorithms and Efficiency, Relational databases, SQL, NoSQL, and Data Wrangling. Practice both theory and applications using Python programming

IEOR E2261 ACCOUNTING AND FINANCE. 3.00 points.

Lect: 3.

Prerequisites: (ECON UN1105) ECON W1105
For undergraduates only. Examines the fundamental concepts of financial accounting and finance, from the perspective of both managers and investors. Key topics covered include principles of accrual accounting; recognizing and recording accounting transactions; preparation and analysis of financial statements; ratio analysis; pro-forma projections; time value of money (present values, future values and interest/discount rates); inflation; discounted-cash-flow (DCF) project evaluation methods; deterministic and probabilistic measures of risk; capital budgeting

IEOR E3106 STOCHASTIC SYSTEMS AND APPLICATIONS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3658) and For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Must be taken during or before the fifth semester.
Corequisites: IEOR E4404

Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance. IEOR E3106 must be completed by the fifth term. Only students with special academic circumstances may be allowed to take these courses in alternative semesters with the consultation of CSA and Departmental advisers.

IEOR E3402 PRODUCTN-INVENTORY PLAN-CONTRL. 4.00 points.

Lect: 3. Recit: 1.

Prerequisites: (IEOR E3608) and (IEOR E3658) and IEOR E3608 AND IEOR E3658
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Must be taken during (or before) the sixth semester. Inventory management and production planning. Continuous and periodic review models: optimal policies and heuristic solutions, deterministic and probabilistic demands. Material requirements planning. Aggregate planning of production, inventory, and work force. Multi-echelon integrated production-inventory systems. Production scheduling. Term project. Recitation section required

IEOR E3404 SIMULATION MODELING AND ANALYSIS. 4.00 points.

Prerequisites: Knowledge of a programming language such as Python, C, C++ or Matlab

It is strongly advised that Stochastic modeling (IEOR E3106 or IEOR E4106) be taken before this course. This is an introductory course to simulation, a statistical sampling technique that uses the power of computers to study complex stochastic systems when analytical or numerical techniques do not suffice. The course focuses on discrete-event simulation, a general technique used to analyze a model over time and determine the relevant quantities of interest. Topics covered in the course include the generation of random numbers, sampling from given distributions, simulation of discrete-event systems, output analysis, variance reduction techniques, goodness of fit tests, and the selection of input distributions. The first half of the course is oriented toward the design and implementation of algorithms, while the second half is more theoretical in nature and relies heavily on material covered in prior probability courses. The teaching methodology consists of lectures, recitations, weekly homework, and both in-class and take-home exams. Homework almost always includes a programming component for which students are encouraged to work in teams.

IEOR E3608 FOUNDATIONS OF OPTIMIZATION. 3.00 points.

Lect: 3.

Prerequisites: (MATH UN2010)
Corequisites: COMS W3134,COMS W3137

This first course in optimization focuses on theory and applications of linear optimization, network optimization, and dynamic programming.

IEOR E3609 ADVANCED OPTIMIZATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3608) IEOR E3608
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. This is a follow-up to IEOR E3608 and will cover advanced topics in optimization, including integer optimization, convex optimization, and optimization under uncertainty, with a strong focus on modeling, formulations, and applications

IEOR E3658 PROBABILITY FOR ENGINEERS. 3.00 points.

Lect: 3.

Prerequisites: Calculus. For undergraduates only. Required for OR:FE concentration. Must be taken during or before third semester. Students who take IEOR E3658 may not take W4150 due to significant overlap. Recommended: strong mathematical skills.

Introductory course to probability theory and does not assume any prior knowledge of subject. Teaches foundations required to use probability in applications, but course itself is theoretical in nature. Basic definitions and axioms of probability and notions of independence and conditional probability introduced. Focus on random variables, both continuous and discrete, and covers topics of expectation, variance, conditional distributions, conditional expectation and variance, and moment generating functions. Also Central Limit Theorem for sums of random variables. Consists of lectures, recitations, weekly homework, and in-class exams.

IEOR E3700 Research Immersion in OR and Data Analytics. 3.00 points.

Prerequisites: IEOR E3608 AND IEOR E3658 AND IEOR E4307; For IEOR undergraduates only.
An overview of active research areas in Operations Research and Data Analytics, and an introduction to the essential components of research studies. This course helps students develop fundamental research skills, including paper reading, problem formulation, problem-solving, scientific writing, and research presentation. Classes are in seminar format, with students analyzing research papers, developing research projects, and presenting research findings

IEOR E3900 UNDERGRAD RESEARCH OR PROJECT. 1.00-3.00 points.

Prerequisites: Approval by a faculty member who agrees to supervise the work.
Independent work involving experiments, computer programming, analytical investigation, or engineering design

IEOR E3999 FIELDWORK. 1.00-2.00 points.

1-1.5 pts. (up to 2 pts. summer only)

Prerequisites: Obtained internship and approval from faculty advisor. Only for IEOR undergraduate students who need relevant work experience as part of their program of study.
Final reports are required. This course may not be taken for pass/fail credit or audited

IEOR E4003 CORPORATE FINANCE FOR ENGINEERS. 3.00 points.

Lect: 3.

Prerequisites: Probability theory and linear programming. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR.
Introduction to the economic evaluation of industrial projects. Economic equivalence and criteria. Deterministic approaches to economic analysis. Multiple projects and constraints. Analysis and choice under risk and uncertainty

IEOR E4004 OPTIMIZATION MODELS AND METHODS. 3.00 points.

Lect: 3.

Prerequisites: Refer to course syllabus.
A graduate course only for MS＆E, IE, and OR students. This is also required for students in the Undergraduate Advanced Track. For students who have not studied linear programming. Some of the main methods used in IEOR applications involving deterministic models: linear programming, the simplex method, nonlinear, integer and dynamic programming

IEOR E4007 OPT MODELS ＆ METHODS FOR FE. 3.00 points.

Lect: 3.

Prerequisites: Linear algebra. This graduate course is only for M.S. Program in Financial Engineering students.
Linear, quadratic, nonlinear, dynamic, and stochastic programming. Some discrete optimization techniques will also be introduced. The theory underlying the various optimization methods is covered. The emphasis is on modeling and the choice of appropriate optimization methods. Applications from financial engineering are discussed

IEOR E4008 COMPUTATION DISCRETE OPT. 3.00 points.

Prerequisites: Linear programming, basic probability theory.

Discrete optimization problems. Mathematical techniques and testing strengths and limits in practice on relevant applications. Transportation (travelling salesman and vehicle routing) and matching (online advertisement and school allocation) problems.

IEOR E4011 Agentic AI for Operations Research and Financial Engineering. 3.00 points.

Prerequisites: Students should be familiar with Python programming. A basic understanding of probability and statistics at the level of IEOR E4150 (or equivalent) is required.

Introduces agentic AI systems for decision-making in operations research and financial engineering. Covers large language models, retrieval-augmented generation, conversational agents, semantic workflows, and streaming architectures. Emphasizes hands-on design and deployment of end-to-end agentic pipelines, integrating forecasting, optimization, and reasoning in operational problems and financial engineering applications. Applications include supply chain risk, energy systems, inventory management, and portfolio management.

IEOR E4100 STATISTICS ＆ SIMULATION. 1.50 point.

Lecture 1.5

Prerequisites: Understanding of single- and multivariable calculus.
Probability and simulation. Statistics building on knowledge in probability and simulation. Point and interval estimation, hypothesis testing, and regression. A specialized version of IEOR E4150 for MSE and MSBA students who are exempt from the first half of IEOR E4101. Must obtain waiver for E4101

IEOR E4101 PROBABILITY STAT ＆ SIMULATION. 3.00 points.

Prerequisites: Understanding of single- and multivariable calculus. MSE and MSBA students only.
Basic probability theory, including independence and conditioning, discrete and continuous random variable, law of large numbers, central limit theorem, and stochastic simulation, basic statistics, including point and interval estimation, hypothesis testing, and regression; examples from business applications such as inventory management, medical treatments, and finance. A specialized version of IEOR E4150 for MSE and MSBA students

IEOR E4102 STOCHASTIC MODELING FOR MSE. 3.00 points.

Prerequisites: IEOR E4101
Introduction to stochastic processes and models, with emphasis on applications to engineering and management; random walks, gambler’s ruin problem, Markov chains in both discrete and continuous time, Poisson processes, renewal processes, stopping times, Wald’s equation, binomial lattice model for pricing risky assets, simple option pricing; simulation of simple stochastic processes, Brownian motion, and geometric Brownian motion. A specialized version of IEOR E4106 for MSE students

IEOR E4106 STOCHASTIC MODELS. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) or IEOR E4150 This graduate course is only for IE and OR students. Also required for students in the Undergraduate Advanced Track.
Corequisites: IEOR E4403

Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance.

IEOR E4108 SUPPLY CHAIN ANALYTICS. 3.00 points.

Prerequisites: IEOR E3402 or instructor's permission. MS IEOR students only.

Supply chain management, model design of a supply chain network, inventories, stock systems, commonly used inventory models, supply contracts, value of information and information sharing, risk pooling, design for postponement, managing product variety, information technology and supply chain management; international and environmental issues. Note: replaced IEOR E4000 beginning in fall 2018.

IEOR E4111 OPERATIONS CONSULTING. 3.00 points.

Prerequisites: Probability and statistics at the level of IEOR E3658 and E4307 or STAT GU4001, and Deterministic Models at the level of IEOR E3608 or IEOR E4004, or instructor permission. For MS-MS&E students only.

Aims to develop and harness the modeling, analytical, and managerial skills of engineering students and apply them to improve the operations of both service and manufacturing firms. Structured as a hands-on laboratory in which students "learn by doing" on real-world consulting projects (October to May). The student teams focus on identifying, modeling, and testing (and sometimes implementing) operational improvements and innovations with high potential to enhance the profitability and/or achieve sustainable competitive advantage for their sponsor companies. The course is targeted toward students planning careers in technical consulting (including operations consulting) and management consulting, or pursuing positions as business analysts in operations, logistics, supply chain and revenue management functions, positions in general management, and future entrepreneurs.

IEOR E4150 INTRO-PROBABILITY ＆ STATISTICS. 3.00 points.

Lect: 3.

Prerequisites: Calculus, including multiple integration.
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students

IEOR E4177 Think Bigger. 3.00 points.

Innovative solutions to complex problems that are both novel and useful. Focuses on The Think Bigger Innovation Method, uses decision-making theory, cognitive science, and industry practice to facilitate creativity and innovation. Designed to foster new ideas during the beginning of the semester that will then function as the seeds for entrepreneurially minded. Culminates in a final project with presentation of formal and polished pitch of an innovative idea in front of a distinguished panel of successful minds from across the city

IEOR E4199 MSIEOR Quantitative Bootcamp. 0.00 points.

Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students

IEOR E4207 HUMAN FACTORS: PERFORMANCE. 3.00 points.

Lect: 3.

Sensory and cognitive (brain) processing considerations in the design, development, and operations of systems, products, and tools. User or operator limits and potential in sensing, perceiving decision making, movement coordination, memory, and motivation.

IEOR E4208 SEM IN HUMAN FACTORS DESIGN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4207) or IEOR E4207; IEOR E4207 or instructor’s permission.
An elective for undergraduate students majoring in IE. An in-depth exploration of the application potential of human factor principles for the design of products and processes. Applications to industrial products, tools, layouts, workplaces, and computer displays. Consideration to environmental factors, training and documentation. Term project

IEOR E4212 Data Analytics ＆ Machine Learning for OR. 3.00 points.

Surveys tools available in Python for getting (web scraping and APIs) and visualizing data (charts and maps). Introduction to analytics through machine learning (ML algorithms, model evaluation, text analytics, network algorithms, deep learning)

IEOR E4213 AI and Algorithms for Analytics. 3.00 points.

Prerequisites: IEOR E2000 (or sufficient coding background in Python); IEOR 3608 (or CSOR 4231 Analysis of Algorithms I); IEOR E3658 (or another probability course); IEOR E4212 (or machine learning background from other courses

This course explores modern artificial intelligence (AI) algorithms and methods for analytics and operational decision-making. Students will develop a conceptual and methodological understanding of AI tools, with an emphasis on their applications to domains such as healthcare, finance, energy systems, and logistics. Topics include machine learning, neural networks, large language models, reinforcement learning, multi-agent systems, and agentic systems.

IEOR E4307 STATISTICS AND DATA ANALYSIS. 3.00 points.

Lect: 3.

Prerequisites: probability, linear algebra.
Descriptive statistics, central limit theorem, parameter estimation, sufficient statistics, hypothesis testing, regression, logistic regression, goodness-of-fit tests, applications to operations research models

IEOR E4399 MSE Quantitative Bootcamp. 0.00 points.

Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students

IEOR E4402 Corporate Finance, Accounting ＆ Investment Banking. 3.00 points.

Prerequisites: Covers primary financial theories and alternative theories underlying Corporate Finance, such as CAPM, Miller Modigliani, Fama French factors, Smart Beta, etc.
Interpret financial statements, build cash flow models, value projects, value companies, and make Corporate Finance decisions. Additional topics include: cost of capital, dividend policy, debt policy, impact of taxes, Shareholder/Debtholder agency costs, dual-class shares, using option pricing theory to analyze management behavior, investment banking activities, including equity underwriting, syndicated lending, venture capital, private equity investing and private equity secondaries. Application of theory in real-world situations: analyzing financial activities of companies such as General Electric, Google, Snapchat, Spotify, and Tesla

IEOR E4403 QUANTITATIVE CORPORATE FINANCE. 3.00 points.

Lect: 3.

Prerequisites: Probability theory and linear programming
Required for students in the Undergraduate Advanced Track. Key measures and analytical tools to assess the financial performance of a firm and perform the economic evaluation of industrial projects. Deterministic mathematical programming models for capital budgeting. Concepts in utility theory, game theory and real options analysis

IEOR E4404 SIMULATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (STAT GU4001) and Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Also required for MSOR.
Corequisites: IEOR E3106,IEOR E4106

Generation of random numbers from given distributions; variance reduction; statistical output analysis; introduction to simulation languages; application to financial, telecommunications, computer, and production systems. Graduate students must register for 3 points. Undergraduate students must register for 4 points. Note: Students who have taken IEOR E4703 Monte Carlo simulation may not register for this course for credit. Recitation section required.

IEOR E4405 PRODUCTION SCHEDULING. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3608) and (IEOR E3658) and IEOR E3608 AND IEOR E3658; Computer programming.
Required for undergraduate students majoring in IE and OR. Job shop scheduling: parallel machines, machines in series; arbitrary job shops. Algorithms, complexity, and worst-case analysis. Effects of randomness: machine breakdowns, random processing time. Term project

IEOR E4407 GAME THEOR MODELS OF OPERATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4004) or (IEOR E3608) and (IEOR E4106) or (IEOR E3106) and Familiarity with differential equations and computer programming; or instructor’s permission.

Required for undergraduate students majoring in OR:FE and OR. A mathematically rigorous study of game theory and auctions, and their application to operations management. Topics include introductory game theory, private value auction, revenue equivalence, mechanism design, optimal auction, multiple-unit auctions, combinatorial auctions, incentives, and supply chain coordination with contracts. No previous knowledge of game theory is required.

IEOR E4213 AI and Algorithms for Analytics. 3.00 points.

Prerequisites: IEOR E2000 (or sufficient coding background in Python); IEOR 3608 (or CSOR 4231 Analysis of Algorithms I); IEOR E3658 (or another probability course); IEOR E4212 (or machine learning background from other courses

This course explores modern artificial intelligence (AI) algorithms and methods for analytics and operational decision-making. Students will develop a conceptual and methodological understanding of AI tools, with an emphasis on their applications to domains such as healthcare, finance, energy systems, and logistics. Topics include machine learning, neural networks, large language models, reinforcement learning, multi-agent systems, and agentic systems.

IEOR E4418 TRANSPORTATION ANALYTICS ＆ LOGISTICS. 3.00 points.

Lect: 3.

Prerequisites: A background in optimization and/or algorithms is strongly encouraged and may be obtained concurrently. Background in probability and/or stochastic processes is also strongly encouraged and may be obtained concurrently. Prior coding experience encouraged
Transportation, primarily focused on the movement of people, and logistics, primarily focused on the movement of goods, are two of the most fundamental challenges to modern society. To address many problems in these areas, a wide array of mathematical models and analytics tools have been developed. This class will introduce many of the foundational tools used in transportation and logistics problems, relying on ideas from linear optimization, integer optimization, stochastic processes, statistics, and simulation. We will address problems such as optimizing the routes of cars and delivery trucks, positioning emergency vehicles, and controlling traffic behavior. Moreover, we will discuss modern issues such as bicycle sharing, on-demand car and delivery services, and humanitarian logistics. Concepts will be reinforced with technical content as well as real-world data and examples

IEOR E4420 Introduction to Financial Technology. 3.00 points.

Prerequisites: Students should have a good background in calculus, probability, and linear algebra. Some basic prior knowledge of finance will be helpful (but not necessary).

Introduction to the foundations and applications of Financial Technology, with a focus on decentralized finance (DeFi) and blockchain systems. Potential topics include blockchain fundamentals, permissioned and permissionless protocols, DeFi primitives such as AMMs, lending, liquidity staking, MEV, and stablecoins, market microstructure and on-chain data analytics, as well as emerging applications including institutional DeFi, real-world asset tokenization, and AI integration. Emphasizes both theoretical concepts and empirical design, giving students practical experience analyzing and working with blockchain and DeFi data.

IEOR E4500 APPLICATIONS PROGRAMMNG FOR FE. 3.00 points.

Lect: 3.

Prerequisites: Computer programming or instructor’s approval. Required for undergraduate students majoring in OR:FE.
We will take a hands-on approach to developing computer applications for Financial Engineering. Special focus will be placed on high-performance numerical applications that interact with a graphical interface. In the course of developing such applications we will learn how to create DLLs, how to integrate VBA with C/C programs, and how to write multithreaded programs. Examples of problems settings that we consider include simulation of stock price evolution, tracking, evaluation and optimization of a stock portfolio; optimal trade execution. In the course of developing these applications, we review topics of interest to OR:FE in a holistic fashion

IEOR E4501 TOOLS FOR ANALYTICS. 3.00 points.

Corequisites: IEOR E4523

MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH.

IEOR E4502 Python for Analytics. 0.00 points.

Zero-credit course. Primer on Python for analytics concepts. Required for MSBA students

IEOR E4505 OPERATION RES IN PUBLIC POLICY. 3.00 points.

Aims to give the student a broad overview of the role of Operations Research in public policy. The specific areas covered include voting theory, apportionment, deployment of emergency units, location of hazardous facilities, health care, organ allocation, management of natural resources, energy policy, and aviation security. Draws on a variety techniques such as linear and integer programming, statistical and probabilistic methods, decision analysis, risk analysis, and analysis and control of dynamic systems.

IEOR E4506 DESIGN DIGITAL OPERATING MODELS. 3.00 points.

IEOR students only. Understand digital businesses, apply scientific, engineering thinking to digital economy. Data-driven digital strategies and operating models. Sectors: ecommerce, advertising technology, and marketing technology. Automation of the marketing, sales, and advertising functions. Algorithms, patents, and business models. Business side of the digital ecosytem and the digital economy

IEOR E4507 HEALTHCARE OPERATIONS MGT. 3.00 points.

Prerequisites: (IEOR E3608) and (IEOR E3658) and (IEOR E4307) For senior undergraduate Engineering students: SIEO W3600: Introduction to Probability and Statistics and IEOR E3608: Introduction to Mathematical Programming; For Engineering graduate students (MS or PhD): Probability and Statistics at the level of SIEO W4150, and Deterministic Models at the level of IEOR E4004. For Healthcare Management students: P8529: Analytic Methods for Health Services Management.

Prerequisite(s): for senior undergraduate Engineering students: IEOR E3608, E3658, and E4307; for Engineering graduate students (M.S. or Ph.D.): Probability and statistics at the level of IEOR E4150, and deterministic models at the level of IEOR E4004; for healthcare management students: P8529 Analytical methods for health services management. Develops modeling, analytical, and managerial skills of engineering and health care management students. Enables students to master an array of fundamental operations management tools adapted to the management of health care systems. Through real-world business cases, students learn to identify, model, and analyze operational improvements and innovations in a range of health care contexts.

IEOR E4510 PROJECT MANAGEMENT. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4004) or (IEOR E3608) IEOR E4004 OR IEOR E3608
Management of complex projects and the tools that are available to assist managers with such projects. Topics include project selection, project teams and organizational issues, project monitoring and control, project risk management, project resource management, and managing multiple projects

IEOR E4511 Industry Projects in Analytics ＆ Operations Research. 3.00 points.

Teams of students work on real-world projects in analytics. Focus on three aspects of analytics: identifying client analytical requirements; assembling, cleaning and organizing data; identifying and implementing analytical techniques (e.g., statistics and/or machine learning); and delivering results in a client-friendly format. Each project has a defined goal and pre-identified data to analyze in one semester. Client facing class. Class requires 10 hours of time per week and possible client visits on Fridays

IEOR E4520 APPLIED SYSTEMS ENGINEERING. 3.00 points.

Lect: 3.

Prerequisites: B.S. in engineering or applied sciences; professional experience recommended; calculus, probability and statistics, linear algebra.
Introduction to fundamental methods used in systems engineering. Rigorous process that translates customer needs into a structured set of specific requirements; synthesizes a system architecture that satisfies those requirements and allocates them in a physical system, meeting cost, schedule, and performance objectives throughout the product life-cycle. Sophisticated modeling of requirements optimization and dependencies, risk management, probabilistic scenario scheduling, verification matrices, and systems-of-systems constructs are synthesized to define the meta-workflow at the top of every major engineering project

IEOR E4521 SYSTEM ENGI TOOLS/METHODS. 3.00 points.

Prerequisites: B.S. in engineering or applied sciences; probability and statistics, optimization, linear algebra, and basic economics.
Applications of SE tools and methods in various settings. Encompasses modern complex system development environments, including aerospace and defense, transportation, energy, communications, and modern software-intensive systems

IEOR E4523 Data Analytics and Machine Learning. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4501) Prior exposure to some programming language is helpful, and you should either have taken or be concurrently registered in the IEOR E4501 Tools for Analytics class.

IEOR students only; priority to MSBA students. Practical survey of Python tools for acquiring, cleaning, and analyzing data. Techniques for obtaining data from files, web scraping, and APIs (CSV, HTML, JSON, XML); performing core data-cleaning tasks; and using data analysis, machine learning, and visualization libraries (NumPy, Pandas, Matplotlib, Seaborn, TensorFlow/Keras). Introduces foundational machine learning and deep learning concepts, including backpropagation, gradient descent, and implementation of neural networks with TensorFlow/Keras. Covers text mining using word, sentence, and document embeddings. Includes a group project requiring students to collect, store, and analyze a dataset of their choice and build a predictive model.

IEOR E4524 ANALYTICS IN PRACTICE. 3.00 points.

Lect: 3.

MSBA students only. Groups of students will work on real world projects in analytics, focusing on three aspects: identifying client analytical requirements; assembling, cleaning, and organizing data; identifying and implementing analytical techniques (statistics, OR, machine learning); and delivering results in a client-friendly format. Each project has a well-defined goal, comes with sources of data preidentified, and has been structured so that it can be completed in one semester. Client-facing class with numerous on-site client visits; students should keep Fridays clear for this purpose.

IEOR E4525 MACHINE LEARNING FE ＆ OPR. 3.00 points.

Prerequisites: Optimization, applied probability, statistics, and knowledge of or experience in computer programming.
MS IEOR students only. Introduction to machine learning, practical use of ML algorithms and applications to financial engineering and operations. Supervised learning: regression, classification, resampling methods, regularization, support vector machines (SVMs), and deep learning. Unsupervised learning: dimensionality reduction, matrix decomposition, and clustering algorithms

IEOR E4526 ANALYTICS ON THE CLOUD. 3.00 points.

Prerequisites: IEOR E4501 and IEOR E4523 IEOR E4501 AND IEOR E4523
To introduce students to programming issues around working with clouds for data analytics. Class will learn how to work with infrastructure of cloud platforms, and discussion about distributed computing, focus of course is on programming. Topics covered include MapReduce, parallelism, rewriting of algorithms (statistical, OR, and machine learning) for the cloud, and basics of porting applications so that they run on the cloud

IEOR E4530 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Prerequisites: A course in probability and statistics 4101/4150 or equivalent, basic python programming.
This course will cover the basics of game theory and market design, with a focus on how AI and optimization enables large-scale game solving and markets. We will cover the core ideas behind recent superhuman AIs for games such as Poker. Then, we will discuss how AI and game theory ideas are used in marketplaces such as internet advertising, fair course seat allocation, and spectrum reallocation. This is intended to be an advanced MS level and senior undergraduate course for students in Operations Research and Financial Engineering

IEOR E4532 Visualization and Storytelling with Data. 1.50 point.

Data visualization and how to build a story with data. Using complex data or statistics to communicate results effectively. Learn to present analysis and results conscisely and effectively

IEOR E4533 Performance, Objectives, ＆ Results Using Data Analytics. 1.50 point.

OKR framework and different variations. Measurement techniques (A/B testing, validation, correlation, etc.) Identifying what to measure in product experience and business initiatives. Data-driven decision making

IEOR E4534 Applied Analytics: from Data to Decisions. 3.00 points.

Applied Analytics focus querying and transforming data with SQL, defining and visualizing metrics, measuring impact of products / processes. Tools and techniques to convert raw data to business decisions, statistical analysis. Be able to apply these techniques to real-world datasets

IEOR E4535 Analytics Engineering. 3.00 points.

Introduction to the foundations and applications of Analytics Engineering, bridging Data Engineering and Data Analysis. Potential topics include SQL fundamentals, data warehouse setup and maintenance (Snowflake), version control with GitHub, data ingestion and integration (Fivetran), data cleaning and transformations (dbt), data visualization (Preset), and orchestrating modern data architectures. Emphasizes both theoretical understanding and hands-on practice, giving students practical experience building, managing, and analyzing data pipelines and models in real-world scenarios

IEOR E4540 DATA MINING. 3.00 points.

Prerequisites: Linear Algebra, Calculus, Probability, and some basic programming.
Course covers major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classification, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students learn about principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fitting and parameter tuning, and how to apply methods in practice and assess their performance. Emphasizes roles of statistical modeling and optimization in data mining

IEOR E4544 Statistical Methods for Analytics. 3.00 points.

Focus on advanced statistical techniques for a career in data science or business analytics. Covers the use of writing probabilistic models for data-generating processes, using Bayesian Methods/MCMC to solve such problems. Emphasizes problem identification and general problem-solving tools. Special Topics: Survival Analysis, Missing Data, Robust Statistics, Sequential Analysis, Multiple Testing. Assignments are case-based

IEOR E4561 LAUNCH YOUR STARTUP: TECH. 3.00 points.

Tools and knowledge to develop a comprehensive new venture that is scalable, repeatable, and capital efficient. Covers customer discovery, market sizing, pricing, competition, distribution, funding, developing a minimal viable product, and other facets of creating new ventures. A company blueprint and final investor pitch are deliverables

IEOR E4562 Innovate Using Design Thinking. 3.00 points.

How Design Thinking can enhance innovation activities, market impact, value creation, and speed. Topics include: conceptual and practical understanding of design thinking, creative solutions, develop robust practices to lead interdisciplinary teams. Course aims to strengthen individuals and collaborative capabilities to identify customer needs, indirect and qualitative research, create concept hypotheses, develop prototype, defined opportunities into actionable innovation possibilities, and recommendations for client organizations

IEOR E4563 Technology Breakthroughs. 1.50 point.

Technological breakthroughs driven change, disruption, and transformation of the business landscape/society. Course covers overview of deep learning and neural networks; AI and robotics; imaging and vision; photonics; blockchain; smart/digital cities; and the application of these technologies for creating new products and services

IEOR E4564 From Analytics To Action. 1.50 point.

Prerequisites: Prior knowledge in quantitative and analytical methods, such as operations research (e.g., optimization or stochastic processes), business analytics (e.g., data analysis or statistical modeling), or machine learning.

Explores how analytics and AI drive strategy, business models, and organizational transformation. Develops students as “analytics translators” who bridge technical models and business execution using leadership, communication, and change management alongside analytical expertise. Covers frameworks for value creation, organizational design, systems thinking, complexity, innovation, and behavioral barriers such as cognitive biases and resistance to change. Examines how firms integrate analytics into strategy and operations within evolving digital and knowledge-driven economies.

IEOR E4570 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

1.5 points

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit. or Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

IEOR E4571 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

IEOR E4572 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Lect: 3.,Points: 1.5

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4573 TOPICS IN OR. 1.50 point.

Points: 1.5

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4574 TOPICS IN OR. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4575 TOPICS IN OPERATIONS RESEARCH. 1.50 point.

Note to students: 1.5 credits

Prerequisites: IEOR E4004IEOR E4106IEOR E4150 or instructor's permission. Probability and statistics, Basic optimization (e.g., familiarity with linear and convex optimization, gradient descent, basic algorithm design constructs), familiarity with Programming in python (or experience with programming in other languages like C/C++/Matlab and willingness to learn python). Knowledge of machine learning is not required, but some basic familiarity may help.
This course introduces engineering undergraduates to the essential concepts, tools, and skills required to create and manage a successful technology-driven business. Students will learn through a mix of lectures, case studies, assignments, and a final business plan project, guided by a professor with an engineering background and over three decades of real-world startup experience. Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4576 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

1.5 pts

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit. or Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

IEOR E4577 TOPICS IN OPERATIONS RESEARCH. 1.50 point.

Points: 1.5

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission
Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

IEOR E4578 TOPICS IN OPERATION RESEARCH. 1.50 point.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit. or Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

IEOR E4579 TOPICS IN OR. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

IEOR E4599 MSBA Quantitative Bootcamp. 0.00 points.

Primer on quantitative and mathematical concepts. Required for all incoming MSBA students

IEOR E4601 DYNAMIC PRICING/REVENUE MGMT. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) and (IEOR E4004) IEOR E4004 AND STAT W4001
Focus on capacity allocation, dynamic pricing and revenue management. Perishable and/or limited product and pricing implications. Applications to various industries including service, airlines, hotel, resource rentals, etc

IEOR E4602 QUANTITATIVE RISK MANAGEMENT. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) and (IEOR E4106) IEOR E4106 AND STAT W4001
Risk management models and tools; measure risk using statistical and stochastic methods, hedging and diversification. Examples include insurance risk, financial risk, and operational risk. Topics covered include VaR, estimating rare events, extreme value analysis, time series estimation of extremal events; axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models

IEOR E4620 PRICING MODELS FOR FIN ENGIN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4700) IEOR E4700
Required for undergraduate students majoring in OR:FE. Characteristics of commodities or credit derivatives. Case study and pricing of structures and products. Topics covered include swaps, credit derivatives, single tranche CDO, hedging, convertible arbitrage, FX, leverage leases, debt markets, and commodities

IEOR E4630 ASSET ALLOCATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4700) IEOR E4700
Models for pricing and hedging equity, fixed-income, credit-derivative securities, standard tools for hedging and risk management, models and theoretical foundations for pricing equity options (standard European, American equity options, Asian options), standard Black-Scholes model (with multiasset extension), asset allocation, portfolio optimization, investments over longtime horizons, and pricing of fixed-income derivatives (Ho-Lee, Black-Derman-Toy, Heath-Jarrow-Morton interest rate model)

IEOR E4650 BUSINESS ANALYTICS. 3.00 points.

Prerequisites: A solid background in probability and statistics is strongly encouraged. An optimization background is also strongly encouraged and may be obtained concurrently. Prior coding experience is encouraged.
This course focuses on how to identify, evaluate, and capture business analytic opportunities that create value. The course covers basic analytic methods alongside case studies on organizations that successfully deployed these techniques. The first part of the course is on using data to develop insights and predictive capabilities with machine learning techniques. The second part focuses on the use of A/B testing, causal inference, ethics, and optimization to support decision-making

IEOR E4700 INTRO TO FINANCIAL ENGINEERING. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3106) or (IEOR E4106)

Prerequisite(s): IEOR E4106 or E3106. Required for undergraduate students majoring in OR:FE. Introduction to investment and financial instruments via portfolio theory and derivative securities, using basic operations research/engineering methodology. Portfolio theory, arbitrage; Markowitz model, market equilibrium, and the capital asset pricing model. General models for asset price fluctuations in discrete and continuous time. Elementary introduction to Brownian motion and geometric Brownian motion. Option theory; Black-Scholes equation and call option formula. Computational methods such as Monte Carlo simulation.

IEOR E4701 STOCHASTIC MODELS FOR FIN ENG. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) STAT W4001
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Review of elements of probability theory, Poisson processes, exponential distribution, renewal theory, Wald’s equation. Introduction to discrete-time Markov chains and applications to queueing theory, inventory models, branching processes

IEOR E4703 MONTE CARLO SIMULATION METHODS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) IEOR E4701
This graduate course is only for M.S. Program in Financial Engineering students. Multivariate random number generation, bootstrapping, Monte Carlo simulation, efficiency improvement techniques. Simulation output analysis, Markov-chain Monte Carlo. Applications to financial engineering. Introduction to financial engineering simulation software and exposure to modeling with real financial data. Note: Students who have taken IEOR E4404 Simulation may not register for this course for credit

IEOR E4704 Foundations of Financial Technology. 3.00 points.

Prerequisites: Students should have a good background in calculus, probability and linear algebra. Some basic prior knowledge on finance will be helpful (but not necessary).

Introduces emerging area of Financial Technology (FinTech) within decentralized finance (DeFi) and blockchains. Topics: blockchain fundamentals: major (permissionless) blockchain protocols, permissioned blockchains, and their applications; DeFi fundamentals: Layer-2 primitives (AMMs, lending, liquidity staking, MEV, etc) and stablecoins; markets and trading: market microstructure and on-chain data analytics; and emerging applications: institutional DeFi, real-world asset tokenizations (RWAs), and AI integration. Emphasis on both mathematical foundations and empirical designs.

IEOR E4706 FOUNDATIONS FR FINANCIAL ENGIN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) and (IEOR E4702) and Linear algebra
Corequisites: IEOR E4709

This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Discrete-time models of equity, bond, credit, and foreign-exchange markets. Introduction to derivative markets. Pricing and hedging of derivative securities. Complete and incomplete markets. Introduction to portfolio optimization and the capital asset pricing model.

IEOR E4707 FE CONTINUOUS TIME MODELS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701)
Corequisites: IEOR E4709

This graduate course is only for MS program in FE students. Modeling, analysis, and computation of derivative securities. Applications of stochastic calculus and stochastic differential equations. Numerical techniques: finite-difference, binomial method, and Monte Carlo.

IEOR E4709 STATISTICAL ANALYSIS AND TIME SERIES. 3.00 points.

Lect: 3.

Prerequisites: IEOR E4702 Probability
Corequisites: IEOR E4706,IEOR E4707

This graduate course is only for M.S. Program in Financial Engineering students. Empirical analysis of asset prices: heavy tails, test of the predictability of stock returns. Financial time series: ARMA, stochastic volatility, and GARCH models. Regression models: linear regression and test of CAPM, non-linear regression and fitting of term structures.

IEOR E4711 GLOBAL CAPITAL MARKETS. 3.00 points.

Prerequisites: Refer to course syllabus.
An introduction to capital markets and investments providing an overview of financial markets and tools for asset valuation. Topics covered include the pricing of fixed-income securities (treasury markets, interest rate swaps futures, etc.), discussions on topics in credit, foreign exchange, sovereign ad securitized markets—private equity and hedge funds, etc

IEOR E4718 INTRO-IMPLIED VOLATILITY SMILE. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4706) and IEOR E4706; Knowledge of derivatives valuation models.
During the past 15 years the behavior of market options prices have shown systematic deviations from the classic Black-Scholes model. Examines the empirical behavior of implied volatilities, in particular the volatility smile that now characterizes most markets, the mathematics and intuition behind new models that can account for the smile, and their consequences for hedging and valuation

IEOR E4720 TOPICS IN QUANT FINANCE. 3.00 points.

Prerequisites: (IEOR E4700) and IEOR E4700; Additional prerequisites will be announced depending on offering
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets

IEOR E4721 TOPICS IN QUANT FINANCE. 1.50 point.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
This graduate-level course provides a rigorous foundation in quantitative portfolio construction, bridging theory with practical implementation. It develops a systematic framework for constructing optimal portfolios, beginning with the classical Markowitz model and enhancing it to address real-world portfolio objectives and constraints. Students learn how forecasts of asset returns, risk, and market impact are formulated in the optimizer objectives and constraints, and how estimation error affects portfolio construction and performance. They also study the robust estimation of key optimizer inputs such as risk models using modern statistical and econometric methods that handle noise and time-varying dynamics. Students gain hands-on experience constructing portfolios using industry-standard datasets, tools, and risk models. The course also provides a brief introduction to advanced topics such as random matrix theory, multiperiod optimization, and the use of modern machine learning methods, which represent emerging directions in quantitative portfolio research

IEOR E4722 TOPICS IN QUANT FINANCE. 3.00 points.

Prerequisites: IEOR E4700 Additional prerequisites announced depending on offering.

Stochastic control has broad applications in almost every walk of life, including finance, revenue management, energy, health care and robotics. Classical, model-based stochastic control theory assumes that the system dynamics and reward functions are known and given, whereas modern, model-free stochastic control problems call for reinforcement learning to learn optimal policies in an unknown environment. This course covers model-based stochastic control and model-free reinforcement learning, both in continuous time with continuous state space and possibly continuous control (action) space. It includes the following topics: Shortest path problem, calculus of variations and optimal control; formulation of stochastic control; maximum principle and backward stochastic differential equations; dynamic programming and Hamilton-Jacobi-Bellman (HJB) equation; linear-quadratic control and Riccati equations; applications in high-frequency trading; exploration versus exploitation in reinforcement learning; policy evaluation and martingale characterization; policy gradient; q-learning; applications in diffusion models for generative AI.

IEOR E4723 TOPICS IN QUANTATIVE FINANCE. 1.50 point.

Course Points: 1.5

Prerequisites: IEOR E4700; Additional Prerequisites will be announced depending on offering
ESG (Environmental, Social and Corporate Governance) Finance is a rapidly growing area of Investment Management – and Finance more broadly – that has received a lot of attention in the past several years from the investor community, financial regulatory agencies, and the general public alike. This course provides an introduction to ESG Finance from a financial engineer’s perspective. This course also discusses proliferation of newly available data sources and the associated quantitative techniques necessary to process those. A major component of this course is a discussion of Climate Risk, an area of particular focus due to its increasing general importance. The course includes an overview of both recent research and the evolving regulatory landscape in the climate risk space. An in-depth discussion of financial impact assessment of various climate risk-driven scenarios (climate risk stress testing) concludes the course

IEOR E4724 TOPICS IN QUANTATIVE FINANCE. 3.00 points.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
In this course, we will cover the basics of mathematical modeling of interest rates and credit derivatives. In the first part, we will cover basic interest rate derivatives, the Heath-Jarrow-Morton (HJM) framework, classic short rate models (for both interest rates and default intensities), and the numerical techniques used in practice for their calibration. In the second part, we will cover the basics of single-name derivatives modeling, and we will discuss pricing simple credit derivatives. We will also discuss correlation products and the most common techniques used for their pricing. In the third part, we will discuss some recent research papers addressing the use of adjoint algorithmic differentiation for the calculation of risk for interest rate and credit derivatives

IEOR E4725 Topics in Quantitative Finance: Numerical Solutions of Partial Differential Equation. 3 points.

Lect: 3.

Prerequisites: (IEOR E4700) Additional Prerequisites will be announced depending on offering

Networks are ubiquitous in our modern society. Economic and social networks have been used extensively to model a variety of situations, in which individual decision-makers are affected by the choices of their peers in the network. This course will introduce the main mathematical models for the study of these networks. It will discuss game theoretical and dynamic optimization techniques, which can be used to analyze a wide variety of these networks, including their resilience to shocks, the diffusion of information leading to social contagion, and how the strategic behavior of agents shapes the performance of the network

IEOR E4728 TOPICS IN QUANTITATIVE FINANCE. 1.50 point.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
The search for better performance has led investors to explore Alternative Investments that are outside the traditional categories of exchange traded equities, Treasury Bonds, and other investment-grade fixed income products. The field of Alternative Investments covers a wide range of products such as convertible bonds, Preferred Shares, Hedge Funds, Venture Capital, and Cryptocurrencies. There is a growing need in the market for students with knowledge of these products and the practical and theoretical know how of valuing and risk managing these investments given that each product has it own nuances and anomalies. This course presents and studies some major Alternative Investment products and ways to evaluate and risk manage them

IEOR E4729 TOPICS IN QUANTATIVE FINANCE. 1.50 point.

NOTE: If you have previously completed IEOR 4733 Algorithmic Trading or IEOR 4729 Model Based Trading, please note that while this version of 4729 has many new elements, it materially overlaps with these two classes and is intended to replace both.

Prerequisites: IEOR E4700 Additional prerequisites will be announced depending on offering.

Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.

IEOR E4731 CREDIT RISK/CREDIT DERIVATIVES. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) and (IEOR E4707) IEOR E4701 AND IEOR E4707
Introduction to quantitative modeling of credit risk, with a focus on the pricing of credit derivatives. Focus on the pricing of single-name credit derivatives (credit default swaps) and collateralized debt obligations (CDOs). Detail topics include default and credit risk, multiname default barrier models and multiname reduced form models

IEOR E4732 COMPUT METHODS IN FINANCE. 3.00 points.

Prerequisites: (IEOR E4700) IEOR E4700
MS IEOR students only. Application of various computational methods/techniques in quantitative/computational finance. Transform techniques: fast Fourier transform for data de-noising and pricing, finite difference methods for partial differential equations (PDE), partial integro-differential equations (PIDE), Monte-Carlo simulation techniques in finance, and calibration techniques, filtering and parameter estimation techniques. Computational platform will be C /Java/Python/Matlab/R

IEOR E4733 ALGORITHMIC TRADING. 3.00 points.

Prerequisites: IEOR E4700

Large and amorphous collection of subjects ranging from the study of market microstructure, to the analysis of optimal trading strategies, to the development of computerized, high-frequency trading strategies. Analysis of these subjects, the scientific and practical issues they involve, and the extensive body of academic literature they have spawned. Attempt to understand and uncover the economic and financial mechanisms that drive and ultimately relate them.

IEOR E4734 FOR EXCH/RELATD DERIVATVS INST. 1.50 point.

1.5.

Prerequisites: (IEOR E4700) IEOR E4700
Foreign exchange market and its related derivative instruments—the latter being forward contracts, futures, options, and exotic options. What is unusual about foreign exchange is that although it can rightfully claim to be the largest of all financial markets, it remains an area where very few have any meaningful experience. Virtually everyone has traded stocks, bonds, and mutual funds. Comparatively few individuals have ever traded foreign exchange. In part that is because foreign exchange is an interbank market. Ironically the foreign exchange markets may be the best place to trade derivatives and to invent new derivatives—given the massive two-way flow of trading that goes through bank dealing rooms virtually 24 hours a day. And most of that is transacted at razor-thin margins, at least comparatively speaking, a fact that makes the foreign exchange market an ideal platform for derivatives. The emphasis is on familiarizing the student with the nature of the foreign exchange market and those factors that make it special among financial markets, enabling the student to gain a deeper understanding of the related market for derivatives on foreign exchange

IEOR E4735 STRUCTURED＆HYBRID PRODUCTS. 3.00 points.

Prerequisites: Recommended: IEOR E4700
Conceptual and practical understanding of structured and hybrid products from the standpoint of relevant risk factors, design goals and characteristics, pricing, hedging, and risk management. Detailed analysis of the underlying cash-flows, embedded derivative instruments, and various structural features of these transactions, both from the investor and issuer perspectives, and analysis of the impact of the prevailing market conditions and parameters on their pricing and risk characteristics. Numerical methods for valuing and managing risk of structured/hybrid products and their embedded derivatives and their application to equity, interest rates, commodities and currencies, inflation, and credit-related products. Conceptual and mathematical principles underlying these techniques, and practical issues that arise in their implementations in Python and other programming environments. Special contractual provisions encountered in structured and hybrid transactions, and incorporation of yield curves, volatility smile, and other features of the underlying processes into pricing and implementation framework for these products

IEOR E4737 AI Applications in Finance. 3.00 points.

Prerequisites: Proficiency in Python & Tensor, good understanding of calculus, knowledge of linear/matrix algebra, and basic knowledge of statistics

Data, models, visuals; various facets of AI, applications in finance; areas: fund, manager, security selection, asset allocation, risk management within asset management; fraud detection and prevention; climate finance and risk; data-driven real estate finance; cutting-edge techniques: machine learning, deep learning in computational, quantitative finance; concepts: explainability, interpretability, adversarial machine learning, resilience of AI systems; industry utilization

IEOR E4741 Programming for Financial Engineering. 3.00 points.

Covers C programming language, applications, and features for financial engineering, and quantitative finance applications. Note: restricted to IEOR MS FE students only

IEOR E4742 Deep Learning for OR and FE. 3.00 points.

Selected topics of interest in area of quantitative finance. Some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets. Note: open to IEOR students only

IEOR E4745 Applied Financial Risk Management. 3.00 points.

Prerequisites: Probability and statistics, instruments of the financial markets, and asset pricing models

Prerequisites: see notes re: points probability and statistics, instruments of the financial markets, and asset pricing models.
Introduces risk management principles, practical implementation and applications, standard market, liquidity, and credit risk measurement techniques, and their drawbacks and limitations. Note: restricted to IEOR students only

IEOR E4798 Financial Engineering Practitioners Seminar Series. 0.00 points.

Prerequisites: First year MSFE Students
Degree requirement for all MSFE first-year students. Topics in Financial Engineering. Past seminar topics include Evolving Financial Intermediation, Measuring and Using Trading Algorithms Effectively, Path-Dependent Volatility, Artificial Intelligence and Data Science in modern financial decision making, Risk-Based Performance Attribution, and Financial Machine Learning. Meets select Monday evenings

IEOR E4797 Summer Seminar on IEOR Topics. 0.00 points.

Prerequisites: Permission of the instructor

This seminar series exposes IEOR students to emerging research areas, applications, and interdisciplinary methods shaping the field of IEOR today. Students will engage with topics such as financial engineering, machine learning, artificial intelligence, reinforcement learning, operations analytics, and other advanced techniques. Content varies each year based on faculty expertise and emerging trends. The course encourages discussion, critical reading of recent literature, and hands-on exploration of modern methods.

IEOR E4799 MSFE Quantitative and Computational Bootcamp. 0.00 points.

Primer on quantitative and mathematical concepts. Required of all incoming MSFE students

IEOR E4900 MASTERS RESEARCH OR PROJECT. 1.00-3.00 points.

Prerequisites: approval by a faculty member who agrees to supervise the work.
Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design

IEOR E4998 MANAG TECH INNOV ＆ ENTREPRENEURSHIP. 3.00 points.

Lect: 3.

A required course for undergraduate students majoring in OR:EMS. Focus on the management and consequences of technology-based innovation. Explores how new industries are created, how existing industries can be transformed by new technologies, the linkages between technological development and the creation of wealth and the management challenges of pursuing strategic innovation

IEOR E4999 FIELDWORK. 1.00-1.50 points.

Prerequisites: Obtained internship and approval from faculty adviser.
Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. May not be taken for pass/fail credit or audited

IEOR E6608 INTEGER PROGRAMMING. 3.00 points.

N/A

IEOR E6613 Optimization, I. 4.5 points.

Prerequisites: Linear algebra

Theory and geometry of linear programming. The simplex method. Duality theory, sensitivity analysis, column generation and decomposition. Interior point methods. Introduction to nonlinear optimization: convexity, optimality conditions, steepest descent and Newton's method, active set and barrier methods.

IEOR E6614 OPTIMIZATION II. 4.50 points.

Lect: 3.

Prerequisites: Linear algebra
An introduction to combinatorial optimization, network flows and discrete algorithms. Shortest path problems, maximum flow problems. Matching problems, bipartite and cardinality nonbipartite. Introduction to discrete algorithms and complexity theory: NP-completeness and approximation algorithms

IEOR E6617 Machine Learning and High-Dimensional Data Analysis in Operations Research. 3.00 points.

Discusses recent advances in fields of machine learning: kernel methods, neural networks (various generative adversarial net architectures), and reinforcement learning (with applications in robotics). Quasi Monte Carlo methods in the context of approximating RBF kernels via orthogonal transforms (instances of the structured technique). Will discuss techniques such as TD(0), TD(λ), LSTDQ, LSPI, DQN

IEOR E6711 STOCHASTIC MODELING I. 4.50 points.

Prerequisites: (STAT GU4001) or STAT W4001; STAT GU4001 or equivalent.
Advanced treatment of stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering and other engineering applications. Review of elements of probability theory; exponential distribution; renewal theory; Wald’s equation; Poisson processes. Introduction to both discrete and continuous-time Markov chains; introduction to Brownian motion

IEOR E6712 STOCHASTIC MODELING II. 4.50 points.

Prerequisites: (IEOR E6711) or equivalent.

Continuation of IEOR E6711, covering further topics in stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering, and other engineering applications. Topics from among generalized semi-Markov processes; processes with a non-discrete state space; point processes; stochastic comparisons; martingales; introduction to stochastic calculus.

IEOR E8100 ADVANCED TOPICS IN IEOR. 3.00 points.

Prerequisites: Faculty adviser’s permission.
Selected topics in IEOR. Content varies from year to year. May be repeated for credit

IEOR E9101 RESEARCH. 1.00-6.00 points.

Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit