IEOR E Fieldwork. 0 points.

IEOR E2261 Accounting and Finance. 3 points.

Lect: 3.

Prerequisites: (ECON UN1105)

For undergraduates only. This course examines the fundamental concepts of financial accounting and finance, from the perspective of both managers and investors. Key topics covered in this course include principles of accrual accounting; recognizing and recording accounting transactions; preparation and analysis of financial statements, including balance sheets, income statements, cash flow statements, and statements of owners' equity; 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. 

Fall 2020: IEOR E2261
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 2261 001/11352 F 10:10am - 12:40pm
Online Only
Nadejda Zaets 3 106/120

IEOR E3106 Stochastic Systems and Applications. 3 points.

Lect: 3.

Prerequisites: (IEOR E3658) and

For undergraduates only. This course is required for all undergraduate students majoring in IE, OR:EMS, OR:FE and OR.  This class must be taken during (or before) the fifth semester. 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: queuing, 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 advisors.

Fall 2020: IEOR E3106
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 3106 001/11355 T Th 4:10pm - 5:25pm
Online Only
Antonius Dieker 3 88/100

IEOR E3402 Production and Inventory Planning. 4 points.

Lect: 3. Recit: 1.

Prerequisites: (IEOR E3608) and (IEOR E3658) and

For undergraduates only. This course is required for all undergraduate students majoring in IE, OR:EMS, OR:FE and OR. This class must be taken during (or before) the sixth semester. This course equips students with knowledge of fundamental issues in production and inventory planning and control in manufacturing firms while developing students' modeling and analytical skills. The course is targeted toward engineering students planning careers in technical consulting, business analysis in operations, logistics, supply-chain and revenue-management functions, positions in general management and future entrepreneurs. The course will cover inventory management and production planning; material requirements planning; aggregate planning of production, inventory, and work force; multi-echelon integrated production-inventory systems; and production scheduling. Students will have an opportunity to participate in a computer-simulation game where, as operations managers for a company, they work in teams to manage capacity, inventories, scheduling, and purchasing of parts. The recitation section is required.

IEOR E3404 Simulation Modeling and Analysis. 4 points.

Not offered during 2020-21 academic year.

Prerequisites: (IEOR E3658) and (IEOR E4307) and knowledge of a programming language such as Python, C, C++ or Matlab.
Corequisites: IEOR E3106

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 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. 

Fall 2020: IEOR E3608
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 3608 001/12862 M W 11:40am - 12:55pm
Online Only
Vineet Goyal 3 89/100

IEOR E3609 Advanced Optimization. 3 points.

Lect: 3.

Prerequisites: (IEOR E3608)

For undergraduates only. This course is required for all undergraduate students majoring in IE, OR:EMS, OR:FE, OR:A and OR. This course 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 points.

Lect: 3.

Prerequisites: Solid knowledge of calculus, including multiple variable integration.

Prerequisites: Solid knowledge of calculus, including multiple variable integration. For undergraduates only. IEOR majors must take this course during the third or fourth semester. Students who take IEOR E3658 may not take IEOR E4150 due to significant overlap. Recommended: strong mathematical skills. Introductory course to probability theory, and does not assume any prior knowledge of the subject. Teaches foundations required to use probability in applications, but the 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.

Fall 2020: IEOR E3658
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 3658 001/11359 T Th 11:40am - 12:55pm
Online Only
Daniel Lacker 3 90/150

IEOR E3900 Undergraduate Research or Project. 1-3 points.

Prerequisites: approval by a faculty member who agrees to supervise the work.

Independent work involving experiments, computer programming, analytical investigation, or engineering design.

Fall 2020: IEOR E3900
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 3900 001/22658  
Shipra Agrawal 1-3 0/20
IEOR 3900 002/22659  
Eric Balkanski 1-3 0/20
IEOR 3900 003/22660  
Daniel Bienstock 1-3 0/20
IEOR 3900 004/22661  
Agostino Capponi 1-3 0/20
IEOR 3900 005/22662  
Emanuel Derman 1-3 0/20
IEOR 3900 006/22663  
Antonius Dieker 1-3 0/20
IEOR 3900 007/22664  
Adam Elmachtoub 1-3 0/20
IEOR 3900 008/22665  
Yuri Faenza 1-3 0/20
IEOR 3900 009/22666  
Donald Goldfarb 1-3 0/20
IEOR 3900 010/22667  
Vineet Goyal 1-3 0/20
IEOR 3900 011/22668  
Ali Hirsa 1-3 0/20
IEOR 3900 012/22669  
Garud Iyengar 1-3 0/20
IEOR 3900 013/22670  
Hardeep Johar 1-3 0/20
IEOR 3900 014/22671  
Cedric Josz 1-3 0/20
IEOR 3900 015/22672  
Soulaymane Kachani 1-3 1/20
IEOR 3900 016/22673  
Christian Kroer 1-3 0/20
IEOR 3900 017/22674  
Daniel Lacker 1-3 0/20
IEOR 3900 018/22675  
Henry Lam 1-3 0/20
IEOR 3900 019/22676  
Uday Menon 1-3 0/20
IEOR 3900 020/22677  
Jay Sethuraman 1-3 0/20
IEOR 3900 021/22678  
Karl Sigman 1-3 0/20
IEOR 3900 022/22679  
Clifford Stein 1-3 0/20
IEOR 3900 023/22680  
Wenpin Tang 1-3 0/20
IEOR 3900 024/22681  
Van Anh Truong 1-3 0/20
IEOR 3900 025/22682  
Kaizheng Wang 1-3 0/20
IEOR 3900 026/22683  
Ward Whitt 1-3 0/20
IEOR 3900 027/22684  
David Yao 1-3 0/20
IEOR 3900 028/22685  
Yi Zhang 1-3 0/20
IEOR 3900 029/22686  
Xunyu Zhou 1-3 0/20
IEOR 3900 030/22687  
Jenny Mak 1-3 0/20

IEOR E3999 Fieldwork. 1 point.

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.

Fall 2020: IEOR E3999
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 3999 001/11370  
Van Anh Truong, Yi Zhang 1 3/80

IEOR E4000 Professional Development and Leadership for Engineering and Scientists. 0 points.

Prerequisites: Probability theory and linear programming.

The Professional Development and Leadership course aims to enhance and expand Columbia Engineering graduate students’ interpersonal, professional and leadership skills, through six modules, including: (1) professional portfolio; (2) communication skills; (3) business etiquette and networking; (4) leadership, followership and teamwork; (5) life management; and (6) ethics and integrity. Students in the course will build upon and enhance their interpersonal and intrapersonal skills to further distinguishing themselves in the classroom and in their careers. This course is offered at the Pass/D/Fail grading option.

IEOR E4003 Corporate Finance for Engineers. 3 points.

Lect: 3.

This course is required for all undergraduate students majoring in IEOR.  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. 

Fall 2020: IEOR E4003
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4003 001/11375 Th 7:10pm - 9:40pm
Online Only
Maya Waisman 3 102/120

IEOR E4004 Optimization Models and Methods. 3 points.

Lect: 3.

This is 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. 

Fall 2020: IEOR E4004
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4004 001/11378 M W 11:40am - 2:10pm
309 Havemeyer Hall
Cedric Josz 3 69/90
IEOR 4004 002/11384 M W 1:10pm - 3:40pm
501 Northwest Corner
Mingliu Chen 3 43/150
IEOR 4004 003/11385 M W 11:40am - 2:10pm
417 International Affairs Bldg
Garud Iyengar 3 114/90

IEOR E4007 Optimization Models and Methods for Financial Engineering. 3 points.

Lect: 3.

Prerequisites: Linear algebra.

This graduate course is only for MS Program in FE 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. 

Fall 2020: IEOR E4007
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4007 001/11387 M W 10:10am - 12:40pm
417 International Affairs Bldg
3 44/100

IEOR E4008 Computation Discrete Optimization. 3 points.

Not offered during 2020-21 academic year.

Discrete optimization is a powerful tool for modelling a wide range of problems in science, engineering, and many other areas of technological everyday life. It deals with problems where the decisions to be made are discrete, for instance: which cities should be connected with a road, how many airplanes should we build, or to whom should a highly-requested job be given.

IEOR E4009 Non-linear optimization. 3 points.

Lect.: 2.5.Not offered during 2020-21 academic year.

Prerequisites: A course on optimization models and methods (at the level of IEOR 4004) and a course on linear algebra.

Covers unconstrained and constrained nonlinear optimization involving continuous functions. Additionally, fundamental concepts such as optimality conditions and convergence, principal focus on practical optimization methods. 

IEOR E4100 Statistics and Simulation. 1 point.

Lecture 1.5

Prerequisites: Understanding of single- and multi-variable calculus.

Probability simulation. Statistics, build on knowledge in probability and simulation. Point and interval estimation, hypothesis testing, regression. This course is 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. 

Fall 2020: IEOR E4100
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4100 001/11396 M W 10:10am - 12:40pm
501 Schermerhorn Hall
Henry Lam 1 14/40

IEOR E4101 Probability, Statistics and Simulation. 3 points.

Prerequisites: Understanding of singe and multi-variable 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 treatment and finance. This course is a specialized version of IEOR E4575 for MSE and MSBA students. 

Fall 2020: IEOR E4101
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4101 001/11419 M W 10:10am - 12:40pm
501 Schermerhorn Hall
Henry Lam 3 104/150

IEOR E4102 Stochastic Models for Management Science and Engineering. 3 points.

Prerequisites: IEOR E4101

Introduction to stochastic processes and model, 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. This course is a specialized version of IEOR E4106 for MSE students. 

IEOR E4106 Stochastic Models. 3 points.

Lect: 3.

Prerequisites: (STAT GU4001)

This graduate course is only for MS&E, IE and OR students. This is also required for students in the Undergraduate Advanced Track. 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.

Fall 2020: IEOR E4106
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4106 001/11437 M T W Th 10:10am - 11:25am
312 Mathematics Building
Karl Sigman 3 95/180

IEOR E4108 Supply Chain Analytics. 3 points.

Prerequisites: IEOR E3402, IEOR E4000 or instructor’s permission.

Prerequisites: see notes re: points IEOR 3402, IEOR 4000 or permission of instructor

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.

IEOR E4111 Operations Consulting. 3 points.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (STAT GU4001) and Deterministic Models at the level of IEOR E3608 or IEOR E4004, or instructor permission.

This course is for MS-MS&E students only. This course 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. The course is 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.

Fall 2020: IEOR E4111
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4111 001/11443 Th 7:10pm - 9:40pm
417 International Affairs Bldg
Soulaymane Kachani 3 43/100

IEOR E4150 Introduction to Probability and Statistics. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: Calculus, including multiple integration.

This course 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: point and confidence interval estimation, hypothesis tests, linear regression.

Fall 2020: IEOR E4150
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4150 001/11451 M T W Th 8:40am - 9:55am
209 Havemeyer Hall
Antonius Dieker 3 54/60

IEOR E4177 Think Bigger. 3.00 points.

When you have a complex problem that needs solving, you need innovation, a solution that is both novel and useful. This course focuses on The Think Bigger Innovation Method, which utilizes decision-making theory, cognitive science, and industry practice to facilitate creativity and innovation. The course is designed to foster new ideas during the beginning of the semester which will then function as the seeds for an entrepreneurially minded final project. The course culminates in a final project where you will be required to help present a formal and polished pitch of an innovative idea in front of a distinguished panel of successful minds from across the city

Fall 2020: IEOR E4177
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4177 001/22869 W 8:30am - 11:45am
Online Only
Sheena Iyengar 3.00 26/50

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

Fall 2020: IEOR E4199
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4199 001/22052  
Michael Miller 0.00 0/310

IEOR E4207 Human Factors: Performance. 3 points.

Lect: 3.

Prerequisites: Refer to course syllabus.

This course is required for undergraduate students majoring in IE. This course provides a survey of human performance engineering in the design of consumer products, user interfaces and work processes. The goal of the course is to provide the student with the ability to specify human performance variables affecting user performance, safety and satisfaction for a variety of products and task requirements. Topics include task analysis, information processing, anthropometry, control and display design, human computer interaction, usability testing, usability cost/ benefit analysis, forensics, motivation, group dynamics and personnel selection. Course requirements include a research paper and a (group) product redesign project. At the end of the course students will have a deeper understanding of the research and psychological principles underlying human performance capabilities and limitations. The hope is that this course will encourage students to become more of "a user advocate" in their future endeavors.

Fall 2020: IEOR E4207
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4207 001/11456 M 4:10pm - 6:40pm
503 Hamilton Hall
Leon Gold 3 24/50

IEOR E4208 Seminar in Human Factors Design. 3 points.

Lect: 3.

Prerequisites: (IEOR E4207) or IEOR E4207: Human Factors: Performance or the instructor's permission.

This course is an elective undergraduate students majoring in IE. This course is an advance seminar in the field of human factors. A significant part of the course will explore methodologies and tools that facilitate the integration of psychological and human factor research in the design of new products and processes. The first part of the seminar will discuss methodological issues associated with human factor research and product evaluation. The second part of the seminary will address specific "user centered" methodologies (ISO, ANSI) that support the design process. The third part of the course will explore alternative product evaluation techniques. Students will be required to critique scientific research articles in class, and to perform a usability evaluation of a redesigned product. This is a seminar and therefore class participation is important. The opportunity to pursue individual interests in human factors (e.g. consumer, financial and medical product design, human computer interaction, stress, error analysis, usability evaluation, augmented cognition) is strongly encouraged.

IEOR E4211 Applied consulting. 0 points.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (IEOR E4150) or (STAT GU4001) and familiarity with R or SAS.

Basic and advanced techniques in commercial and government consulting. Case studies supported by lectures focused on collecting and analyzing skills, client/market data, client interview techniques, and application of quantitative and qualitative methodologies. Exposure to critical skills on workplan development, interview techniques, presentation deck preparation, costing, and application of analytic techniques to solve complex problems. 

IEOR E4307 Statistics and data analysis. 3 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.

Fall 2020: IEOR E4307
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4307 001/11469 T Th 10:10am - 11:25am
Online Only
Kaizheng Wang 3 74/120

IEOR E4311 Derivatives Marketing and Structures. 1 point.

Points: 1.5 Prerequisites: IEOR E3402, IEOR E4000 or permission of instructor.

Prerequisites: see notes re: points

The course covers topics in Accounting, Relationships among different elements of financial statements, Short term and long term financing alternatives, Using swaps, cap, and floors to manage interest rate risk, Hedging interest risk of corporate finance,


Using options as cheapeners, Structured swaps, Accounting treatment of derivatives, Cash flow hedging, Accrual accounting, Hedging Issuance of a bond using Treasuries, Hedging employee stock options, Preferred


shares and their use in corporate treasury, FX risk and FX translation, Commodities hedging, Operating Lease vs Capital Lease, Credit Risk and Credit Spread and Funding The course has been successfully offered for


the past 2-3 terms under the Fall topics number IEOR E4576. We are now looking to get a dedicated course number for Derivatives Marketing & Structuring.

Fall 2020: IEOR E4311
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4311 001/12863 Th 1:10pm - 3:40pm
633 Seeley W. Mudd Building
Khosrow Dehnad 1 56/80

IEOR E4312 Application of OR & AI Techniques in Marketing. 1 point.

1.5 Points Prerequisites: Working knowledge EXCEL and a high-level language such as Python, R, MATLAB, or VBA and an introductory courses in Probability and Statistics.

Prerequisites: see notes re: points

The course covers working knowledge of quantitative methods and data mining techniques applied to marketing and customer relationship management. Topics include Clustering Methods, Conjoint Analysis and Customer Preferences, Forecasting, Market Share, Product Life Cycle, New Product, Nearest Neighbor, Discriminant Analysis, Decision Tree, Revenue Management, Price and Advertising Elasticity, Resource Allocation and Return on Investment (ROI), Economic Analysis of a Network and its Formation, Networked Markets.

IEOR E4399 MSE Quantitative Bootcamp. 0.00 points.

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

Fall 2020: IEOR E4399
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4399 001/22053  
Michael Miller 0.00 0/130

IEOR E4402 Corporate Finance, Accounting & Investment Banking. 3 points.

This course covers primary financial theories and alternative theories underlying Corporate Finance, such as CAPM, Miller Modigliani, Fama French factors, Smart Beta, etc. Students learn to 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, the impact of taxes, Shareholder / Debtholder agency costs, dual-class shares, how option pricing theory can be used to analyze management behavior, variety of 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 the financial activities of companies such as General Electric, Google, Snapchat, Spotify and Tesla. 

Fall 2020: IEOR E4402
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4402 001/13872 M 7:10pm - 9:40pm
402 Chandler
Rodney Sunada-Wong 3 100/100

IEOR E4403 Quantitative Corporate Finance. 3 points.

Lect: 3.

Prerequisites: Probability theory and linear programming.

This course is 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 and businesses. Deterministic mathematical programming models for capital budgeting. Concepts in utility theory, game theory and real options analysis. 

IEOR E4404 Simulation. 3 points.

Lect: 3.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (STAT GU4001) and computer programming.
Corequisites: IEOR E3106,IEOR E4106

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

Fall 2020: IEOR E4404
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4404 001/11481 T Th 5:40pm - 6:55pm
301 Pupin Laboratories
Yi Zhang 3 76/200

IEOR E4405 Scheduling. 3 points.

Lect: 3.

Prerequisites: (IEOR E3608) and (IEOR E3658) and computer programming.

This course is 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 Theoretic Models of Operations. 3 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.

This course is 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.

Fall 2020: IEOR E4407
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4407 001/11486 M W 10:10am - 11:25am
207 Mathematics Building
Jay Sethuraman 3 75/75

IEOR E4408 Resource Allocation: Models, Algorithms, and Applications. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: (IEOR E3608) or (IEOR E4004) and basic knowledge of nonlinear and integer programming.

Overview of resource allocation models. Single resource allocation with concave returns; equitable resource allocation; lexicographic minmax/maxmin optimization; extensions to substitutable resources; multi-period resource allocation; equitable allocation in multicommodity network flow models; equitable content distribution in networks; equitable resource allocation with discrete decision variables. 

IEOR E4412 Quality Control and Management. 3 points.

Lect: 3.

Prerequisites: (IEOR E3658) and (STAT GU4001) Additional pre-requisite: working knowledge of statistics

This course is required for undergraduate students majoring in IE. Statistical methods for quality control and improvement: graphical methods, introduction to experimental design and reliability engineering and the relationships between quality and productivity. Contemporary methods used by manufacturing and service organizations in product and process design, production and delivery of products and service. 

IEOR E4418 Transportation analytics and logistics. 3 points.

Lect: 3.

Prerequisites: (IEOR E3608 or IEOR E4404 or IEOR E4007 or CSOR W4231 or CSOR W4246) and (IEOR E3106 or IEOR E4307 or SIEO W3600 or IEOR E4100 or IEOR E4101 or IEOR E4150 or STAT GR5701 or STAT GR5703) or permission of instructor.

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. Prerequisites: A course in probability/statistics (e.g., IEOR 3106, IEOR 4307, SIEO 3600, IEOR 4100, IEOR 4101, IEOR 4150, STAT 5701, STAT 5703). A course in optimization (e.g., IEOR 3608, IEOR 4004, IEOR 4007, CSOR 4231, CSOR 4246).

IEOR E4500 Applications Programming for Financial Engineering. 3 points.

Lect: 3.

Prerequisites: Computer programming or instructor's approval.

This course is required for undergraduate students majoring in OR:FE. In this course 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 will 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 will review topics of interest to OR/FE in a holistic fashion.

Fall 2020: IEOR E4500
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4500 001/11489 M W 4:10pm - 5:25pm
207 Mathematics Building
Daniel Bienstock 3 44/100

IEOR E4501 TOOLS FOR ANALYTICS. 3.00 points.

The goal of this course is to provide students with the computing tools that are necessary for data and business analytics. Students will be introduced to the basics of the Python programming language with special emphasis on the data analysis and visualization libraries available in the language (pandas, numpy, scikit-learn, bokeh). They will learn the basics skills for gathering and processing data for analytical exercises using APIs, SQL, and by deconstructing JSON data objects, and will also gain basic familiarity with data manipulation using the Unix shell

Fall 2020: IEOR E4501
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4501 001/11492 T Th 4:10pm - 6:40pm
Online Only
Paul Bulkley-Logston 3.00 142/150

IEOR E4502 Python for Analytics. 0.00 points.

Zero-credit course. Primer on Python for analytics concepts

Fall 2020: IEOR E4502
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4502 001/15479  
Paul Bulkley-Logston 0.00 0/100

IEOR E4505 Operations Research in Public Policy. 3 points.

Prerequisites: (IEOR E3608) or (IEOR E4004) and (IEOR E3106) or (IEOR E4106)

This course 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. The course will draw on a variety techniques such as linear and integer programming, statistical and probabilistic methods, decision analysis, risk analysis, and analysis & control of dynamic systems.

IEOR E4506 Design Digital Operating Models. 3 points.

Focus on the fast evolving sectors of ecommerce, advertising technology, and marketing technology, as venture capitalists chase returns in the ever increasing automation of the marketing, sales, and advertising functions. Understand industry dynamics, algorithms, patents, and business models at the core of the most successful players in the business.  Explore and define the different types of data in the industry and how they are used.

Fall 2020: IEOR E4506
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4506 001/11516 M 7:10pm - 9:40pm
417 International Affairs Bldg
Anthony Effik 3 70/70

IEOR E4507 Healthcare Operations Management. 3 points.

Not offered during 2020-21 academic year.

Prerequisites: (IEOR E3608) and (IEOR E3658) and (IEOR E4307)

Develops modeling, analytical and managerial skills of Engineering students. Enables students to master an array of fundamental Operations Management tools adapted to the management of manufacturing and service systems in banks, hospitals, factories, and government. Special emphasis is placed on healthcare systems. Through real-world business cases, students learn to identify, model and analyze operational improvements and innovations in a range of business contexts, especially healthcare contexts.

Fall 2020: IEOR E4507
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4507 001/11528 M W 11:40am - 12:55pm
Online Only
Van Anh Truong 3 53/80

IEOR E4510 Project Management. 3 points.

Lect: 3.

Prerequisites: (IEOR E4004) or (IEOR E3608)

Management of complex project 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 E4520 Applied Systems Engineering. 3 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-work flow at the top of every major engineering project.

Fall 2020: IEOR E4520
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4520 001/11543 W 4:10pm - 6:40pm
501 Northwest Corner
Ebad Jahangir 3 27/95

IEOR E4522 Python for Operations Research. 1 point.

Lect: 1.5.Not offered during 2020-21 academic year.

IEOR Students Only; Priority to MSOR Students. Introduction to programming in Python, providing a working knowledge of how to use Python to extract knowledge and information from data. Overview of Python libraries for data analysis. Fundamental course for MSOR students in order to engage in higher level analytics courses.

IEOR E4523 Data Analytics. 3 points.

Lect: 3.

Corequisites: IEOR E4501

Co-requisite: IEOR E4501 Tools for Analytics. Survey tools available in Python for getting, cleaning, and analyzing data. Obtain data from files (csv, html, json, xml) and databases (Mysql, PostgreSQL, NoSQL), cover the rudiments of data cleaning, and examine data analysis, machine learning and data visualization packages (numpy, Pandas, Scikit­learn, bokeh) available in Python. Brief overview of natural language processing, network analysis, and big data tools available in Python.  Contains a group project component that will require students to gather, store, and analyze a data set of their choosing.

 

Fall 2020: IEOR E4523
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4523 001/11553 T Th 11:40am - 2:10pm
602 Hamilton Hall
Uday Menon 3 23/80
IEOR 4523 002/11557 M W 2:40pm - 4:10pm
602 Hamilton Hall
Uday Menon 3 79/80

IEOR E4524 Analytics in Practice. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: (IEOR E4523) and IEOR E4501

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, come with the sources of data pre-identified, and have been structured so that they can be completed in one semester. This is a client facing class with numerous on-site client visits; students should keep their Fridays clear for this purpose.

IEOR E4525 Machine Learning for Financial Engineering and Operations. 3 points.

Prerequisites: optimization, applied probability, statistics or simulation.

Introduction to Machine Learning, practical implementation ML algorithms and applications to financial engineering and operations. Probabilistic Tools of Machine Learning, Learning theory, Classification, Resampling Methods and Regularization, Support Vector Machines (SVMs), Unsupervised Learning, Dimensionality Reduction and Clustering algorithms, EM Algorithm, and Neural Networks and Deep Learning

Fall 2020: IEOR E4525
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4525 001/11566 F 10:10am - 12:40pm
Online Only
Christian Kroer 3 120/150

IEOR E4526 Analytics on the Cloud. 3 points.

Prerequisites: IEOR E4501 and IEOR E4523

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

IEOR E4540 Data Mining. 3 points.

The course will cover major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classi
fication, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students will learn about the principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fi
tting and parameter tuning, and how to apply methods in practice and assess their performance. We will emphasize roles of statistical modeling and optimization in data mining. 

 

Fall 2020: IEOR E4540
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4540 001/11568 W 7:10pm - 9:40pm
313 Fayerweather
Krzysztof Choromanski 3 41/75

IEOR E4550 Entrepreneurial Business Creation for Engineers. 3 points.

Lect: 3.

Prerequisites: (IEOR E2261)

This course is required for undergraduate students majoring in OR:EMS. Introduce basic concepts and methodologies that are used by the nonengineering part of the world in creating, funding, investing in, relating to, and operating entrepreneurial ventures. The first half of the course focuses on the underpinning principles and skills required in recognizing, analyzing, evaluating, and nurturing a business idea.The second half focuses on basic legal knowledge necessary in creating a business entity, defending your business assets, and in promoting effective interaction with other individuals and organizations. 

Fall 2020: IEOR E4550
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4550 001/11569 M 4:10pm - 6:40pm
Online Only
David Gulley 3 38/70

IEOR E4555 Design and Agile Project Management Engineering Lab. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

An intensive, team and project­-based seminar in which students will learn: (1) a multi-­disciplinary approach to evidence ­based product design;(2) agile project planning and execution; (3) rapid MVP prototyping; and (4) launch strategy formulation and implementation. Focuses on the practical use of design thinking, design studio, and iterative design sprint methodologies. Systematic approach to Lean User Research, User Experience (UX), and User Interface (UI) design and deployment are integral components of the course curriculum. Mix of startup and enterprise projects that are either application ­drive, data­ driven, or a combination of both. Teams are fully supported in devising prototypes and actualizing their proposed solutions. Note: This course is by application only.

IEOR E4561 Launch Your Startup: Tech. 3 points.

Launch Your Startup focuses on the evaluation, development and potential launch of a new business. Working individually (or on occasion in pairs), students spend the entire term developing an effective and comprehensive presentation of a real business concept by addressing five key issues: in-depth market analysis, product or service design, development of a marketing campaign, assessment of human resource requirements and building a realistic financial forecast. The output will be a comprehensive business plan and a formal presentation of their idea. 


Students are expected to come with a specific business idea or at least a sincere interest in a particular industry in which they would like to explore the possibility of launching a venture. Projects can be based upon students' own ideas, new technologies from the Columbia Innovation Enterprise or other start-ups that have requested assistance from Columbia MBA students. Industry mentors and a board of directors composed of other class participants provide a reality check as students refine their business opportunity into a written and oral presentation ready to seek funding and commence operations. Faculty members assist in identifying projects, but students are responsible for finding appropriate projects. By the second week of class, all students must have an approved venture project.

IEOR E4562 Innovative Using Design Thinking. 3 points.

The course cover topics on 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. The course aims to strengthen individual 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 E4570 Entrepreneurship Bootcamp for Engineers. 1 point.

1.5 points

This course is designed as an introductory exposure to entrepreneurial concepts and practical skills for engineering students who wish to explore entrepreneurship conceptually or as a future endeavor in their careers. The class will be a mix of lecture, guest speakers from the entrepreneurship community, and work-shopping concepts we cover. Grades will be based on class participation and engagement as well as final pitch in which students describe their key takeaways & insights gleaned from the process.

Fall 2020: IEOR E4570
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4570 001/13896  
David Lerner, Adam Royalty 1 26/45

IEOR E4571 Personalization: Theory & Application. 3 points.

Personalization is a key lever for enhancing customer experience across industry verticals, thereby driving user loyalty and consumer value. It is therefore no surprise that personalization is also increasingly one of the core responsibilities of data science teams, and a key focus for many of the machine learning algorithms in the sector. This course will focus on common personalization algorithms and theory, including behaviorbased and content-based recommendation, commonly encountered issues in scaling and coldstarts, and state of the art research. It will also look at how businesses use, and misuse, these techniques in real world applications.

Fall 2020: IEOR E4571
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4571 001/21450 W 7:00pm - 9:30pm
402 Chandler
Brett Vintch, Brianne Cortese 3 83/125

IEOR E4572 Visualization and Story Telling with Data. 3 points.

Lect: 3.,Points: 1.5

Visualization and Story Telling with Data

,

This course will cover principles of data visualization and how to build a story with data. It is too easy to get distracted by complex statistics or massive datasets. This class will teach you to take complex data or statistics and allow you to communicate the results effectively. The final step of any analysis is presenting the result concisely and effectively.  Points: 1.5

Fall 2020: IEOR E4572
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4572 001/11571 T 7:10pm - 9:40pm
417 Mathematics Building
Michelle Glaser 3 39/60

IEOR E4573 Performance, Objectives & Results Using Data Analytics. 3 points.

Points: 1.5

This course will cover how to analyze any business. At the core, we are inundated by data today. But not all of it matters. This class will help you formulate Key Performance Indicators (KPIs) and organize them into Objectives and Key Results (OKRs) so that you’ll be equipped with the strategic and business acumen to help support a product or business in virtually any situation. For example, you might uncover how a tech behemoth like Google organizes itself, and prioritizes projects. But most importantly, you’ll get to uncover what made their success and growth so different: how did they reach 10x, many times over? How do other successful companies get organized around business decisions and strategy? Can you reverse engineer that to analyze (and provide feedback) on any aspect of the company? Hope you join us to find out! 

IEOR E4575 Applied Analytics in Real estate.

Note to students: 1.5 credits

Real estate (encompassing all aspects of the built environment such as finance, architecture, sustainability, design, construction, etc.) is arguably one of the most archaic industries in the world when it comes to the development and adoption of technology. While it might seem that this is a problem with the mentality of the real estate industry towards technology, there are unique challenges associated with collecting, analyzing, and applying data to many functions within the real estate domain. This course will provide an introduction to the current real estate technology landscape, some areas where technology development would be extremely valuable, and the challenges to developing high-level, sophisticated technology for the built environment. We’ll also discuss other industries and organizations that have successfully built complex technology to learn from their approaches. Students will have the opportunity the explore their own ideas for a technology application to the real estate industry or to use an existing company to provide a plan for improved technology utilization.

IEOR E4576 Advanced Factor Investing & Asset Allocation with Machine Learning. 1 point.

1.5 pts

Using a database of one billion data points for nearly ten thousand economic indices, funds, and financial instruments, design and scientifically test investment strategies using modern methods that far exceed traditional econometric studies. We explore conditional probabilities and real-world details (like currency controls, taxes, liquidity, and market impact) that are the failing of most academic studies and make-or-break practical efforts.

IEOR E4577 AI & OR at Scale in the Cloud.

Points: 1.5Not offered during 2020-21 academic year.

Often times, AI and analytics are seen through the lens of model building. Recent work has shown that model building represents only a small portion of the code produced by a data science team. This class focuses on the other aspects of AI: the development process of AI and analytics at scale in the cloud.

IEOR E4578 TOPICS IN OPERATION RESEARCH. 3.00 points.

Prerequisites: Must be registered in one of the MS IEOR Programs
Students in this course will be divided into teams, each of which will work on real-world business analytics, financial engineering, and operations research projects provided by companies like Apple, UBS, and the National Institutes of Health. Each project will be structured with a specific goal and data set. Acceptable results include a solution to the problem, actionable advice for future research, or a presentation of a business case. Project elements include a presentation of a proposal, a solution, a plan for implementation, results of the execution of that plan (including acquiring and wrangling data, analyzing data using machine learning or other techniques, and delivering results suitable for business), and an analysis of results

Fall 2020: IEOR E4578
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4578 001/22051 M 8:40am - 9:55am
501 Northwest Corner
Michael Robbins 3.00 32/40

IEOR E4579 TOPICS IN OR. 1.50 point.

Students will engage, learn and share their experiences in order to make meaning of professional development. The instructional team hopes that the students will obtain the following: gain familiarity and insight to the US job market and US career culture; recognize the skills necessary to compete effectively; increase student professional intelligence, develop own professional self and identify developmental needs; obtain information on employment trends, resources and networking opportunities; refine resume writing, interviewing, and job search skills; establish a collaborative relationship with the instructional team and provide constructive feedback where appropriate to enhance the student's professional development.

IEOR E4600 Applied Integer Programming. 3 points.

Lect: 3.

Prerequisites: Linear programming, linear algebra, and computer programming.

This course is required for undergraduate students majoring in OR. This course covers applications of mathematical programming techniques, especially integer programming, with emphasis on software implementation. This course also covers topics of modeling and solution of problems in supply chain, logistics, routing. Particular emphasis is placed on optimization modeling systems, such as AMPL and OPL and state-of-the-art solvers.

IEOR E4601 Dynamic Pricing and Revenue Management. 3 points.

Lect: 3.

Prerequisites: (STAT GU4001) and (IEOR E4004)

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 points.

Lect: 3.

Prerequisites: (STAT GU4001) and (IEOR E4106)

Risk management models and tools; measure risk using statistical and stochastic methods, hedging and diversification. Examples of this 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 E4611 Decision Models and Applications. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: (IEOR E3608) and (IEOR E4004) or (IEOR E3658) and (IEOR E4307) and (STAT GU4001) or the equivalent. For graduate students: instructor's permission is required.
Corequisites: IEOR E4404

Students are introduced to deterministic and stochastic decision tools used by leading corporations and applied researchers, and apply these software packages to complex, real-world problems in engineering and finance. Building on a basic theoretical understanding of optimization, simulation and game theory obtained in prerequisite classes, students master commercial decision modeling programs such as Premium Solver Professional (linear, integer and non-linear optimization), TreePlan (decision-trees), Crystal Ball (simulation), and OptQuest (optimization under uncertainty). Students are also welcome to complete most modeling assignments with Matlab. After students have mastered the course software, its limitations and the frameworks for applying it, they work in small teams to address (as a mid-term project) one large-scale deterministic project and (as an end-of-semester project) one similarly-complex stochastic problem. While addressing their first projects, students learn effective presentation and project reporting skills, suitable for communicating with CFOs and CEOs. Students present their project analyses to a small panel of industry experts and executives. Throughout the course, the importance of outside-the-model considerations, model limitations and sources of modeling error are stressed, and general frameworks for approaching particular problem types are developed.

IEOR E4615 Service Engineering. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: (SIEO W3600) and (IEOR E3106) or (IEOR E4106) or equivalent.

Focus on service systems viewed as stochastic networks, exploiting the theoretical framework of queueing theory. Includes multi-disciplinary perspectives involving Statistics, Psychology and Marketing. Significant emphasis on data analysis, exploiting data from banks hospitals and call centers to demonstrate the use of decision support tools. Analytical models, flow models of service networks, Little’s law , measuring methods in face-to-face and computerized systems, forecasting methods, stability of service systems, operational quality of service, economies of scale, staffing, complex service networks, skill based routing.

IEOR E4620 Pricing Models for Financial Engineering. 3 points.

Lect: 3.

Prerequisites: (IEOR E4700)

This course is 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.

Fall 2020: IEOR E4620
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4620 001/11576 T 7:10pm - 9:40pm
633 Seeley W. Mudd Building
Michael Miller 3 48/70

IEOR E4630 Asset Allocation. 3 points.

Lect: 3.

Prerequisites: (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 points.

Lect: 3. Recit: 1.

Prerequisites: (STAT GU4001) or (IEOR E4150)

In this course, you will learn how to identify, evaluate, and capture business analytic opportunities that create value. Toward this end, you will learn basic analytical methods and analyze case studies on organizations that successfully deployed these techniques. In the first part of the course, we focus on how to use data to develop insights and predictive capabilities using machine learning and data mining techniques. In the second part, we focus on the use of optimization and simulation to support prescriptive decision-making in the presence of a large number of alternatives and business constraints. Finally, throughout the course, we explore the challenges that can arise in implementing analytical approaches within an organization.

Fall 2020: IEOR E4650
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4650 001/11577 T Th 11:40am - 12:55pm
Online Only
Adam Elmachtoub 3 69/75
IEOR 4650 002/11578 M W 2:40pm - 5:10pm
301 Pupin Laboratories
Van Anh Truong 3 34/70

IEOR E4700 Introduction to Financial Engineering. 3 points.

Lect: 3.

Prerequisites: (IEOR E3106) or (IEOR E4106)

This course is 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. 

Fall 2020: IEOR E4700
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4700 001/11579 M W 1:10pm - 2:25pm
Online Only
Emanuel Derman 3 35/100

IEOR E4701 Stochastic Models for Financial Engineering. 3 points.

Lect: 3.

Prerequisites: (STAT GU4001)

This graduate course is only for MS Program in FE 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. 

Fall 2020: IEOR E4701
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4701 001/11580 T Th 8:40am - 11:10am
Online Only
Daniel Lacker 3 51/120

IEOR E4703 Monte Carlo Simulation. 3 points.

Lect: 3.

Prerequisites: (IEOR E4701)

This graduate course is only for MS Program in FE 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 E4706 Foundations of Financial Engineering. 3 points.

Lect: 3.

Prerequisites: (IEOR E4701) and (IEOR E4702) and linear algebra.

This graduate course is only for MS Program in FE 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.

Fall 2020: IEOR E4706
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4706 001/11581 M T W Th 8:40am - 9:55am
309 Havemeyer Hall
Wenpin Tang 3 45/100

IEOR E4707 Financial Engineering: Continuous-Time Asset Pricing. 3 points.

Lect: 3.

Prerequisites: (IEOR E4701)

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

IEOR E4708 Seminar on Important Papers in Financial Engineering. 3 points.

Lect: 3.

Prerequisites: (IEOR E4703) and (IEOR E4706) and IEOR E4703, E4706, probability and statistics.

Selected topics of special interest for M.S. students interested in financial engineering.

IEOR E4709 Statistical Analysis & Time Series. 3 points.

Lect: 3.

Prerequisites: Probability.
Corequisites: IEOR E4706,IEOR E4702

This graduate course is only for MS Program in FE 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, nonlinear regression and fitting of term structures. 

Fall 2020: IEOR E4709
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4709 001/21452 M W 11:40am - 2:10pm
Online Only
Agostino Capponi 3 40/100

IEOR E4710 Fixed Income and Term Structure Modeling. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: (IEOR E4706) and (IEOR E4707) and computer programming.

Interest rate models and numerical techniques for pricing and hedging interest rate contracts and fixed income securities. Introduction to interest models in discrete and continuous time including lattice models, single- and multi- factor models, Heath-Jarrow-Morton and market models. Martingale and PDE methods for pricing and hedging interest-rate derivatives. Monte-Carlo methods for term structure models. Additional applications from mortgage modeling and fixed income asset allocation and risk management.

IEOR E4711 Global Capital Markets. 3 points.

Prerequisites: Refer to course syllabus.

Fall: Global Capital Markets, taught by Professor S. Dastidar. This course is an introduction to capital markets and investments. It provides an overview of financial markets and teaches you tools for asset valuation that will be very useful in your future career. The extract below is from last year. While I was keen to have similar content, the class complained last year that the topics 2 and 3 were too esoteric for them. Based on what the class feels, I am probably going to replace it with general content on equities (i.e. replace 2 and 3 with 4). This course then becomes very similar to Capital Markets and Investments offered by the Business School. We will cover: 1. The pricing of fixed income securities (treasury markets, interest rate swaps, futures etc) 2. Discussions on topics in credit, foreign exchange, sovereign and securitized markets (we may drop this) 3. Private markets - private equity and hedge funds, etc. (we may drop this) 4. We shall spend some time on equity markets and their derivatives, time permitting. We will aim to take a hands-on approach in this course. The course is somewhat quantitative in nature; you should be prepared to work with data and spreadsheets. Programming is not required. Nevertheless, this is a basic foundations course, and does not assume much prior knowledge in finance. Consequently, the course emphasizes a few cardinal principles while getting into some institutional detail. The word "Global" in the title implies that the concepts we discuss are relevant to all geographies; we will rarely get into specifics of any particular region.

IEOR E4712 Behavioral Finance. 3 points.

Not offered during 2020-21 academic year.

Prerequisites: (IEOR E4700)

Behavioral finance is the application of behavioral psychology to financial decision making. Focus on the portfolio aspect of behavioral finance, and briefly touches others. Compared with the classical theory of portfolio choice, behavioral portfolio choice features human being's psychological biases. It builds both on behavioral preference structures different from mean variance theory and expected utility theory and on systematic biases against rational beliefs such as Bayesian rule.

IEOR E4714 Risk Management, Financial System & Financial Crisis. 1.5 point.

Risk-taking and risk management are at the heart of the financial system, and of the current financial crisis. An introduction to risk management both from an individual financial firm's and from a public policy viewpoint. Overview of the contemporary financial system, focusing on innovations of the past few decades that have changed how financial risk is generated and distributed among market participants, such as the growth of non-bank financial intermediaries, the increased prevalence of leverage and liquidity risk, and the development of structured credit products. Introduction to the basic quantitative tools used in market, credit and liquidity risk management. The two strands of the course are brought together to help understand how the financial crisis arose and is playing out, examining the mechanics of runs and the behavior of asset prices during crises. We also attempt to make sense of the emergency programs deployed by central bankers and other policy makers to address crises historically and today.

IEOR E4715 Commodity Derivatives. 1.5 point.

Commodities markets have been much in the public eye recently as volatility has increased and they changed from markets dominated by physical participants to ones which have a significant investor component. The largest banks either already have profitable commodities franchises already or are actively building them, and money managers and funds are increasingly including these assets in their portfolio mix. The end result is a dramatic increase in focus on these markets from all aspects of the financial markets, including the quantitative end.

IEOR E4718 Beyond black-scholes: the implied volatility smile. 3 points.

Lect: 3.

Prerequisites: (IEOR E4706) and knowledge of derivatives valuation models.

During the past fifteen years the behavior of market options prices have shown systematic deviations from the classic Black-Scholes model. The course 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 of these models for hedging and valuation.

IEOR E4720 Deep Learning. 3 points.

Prerequisites: (IEOR E4700) and 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 Mathematics of Finance for FE Bootcamp. 1.5-3 points.

Prerequisites: IEOR E4700: Introduction to Financial Engineering, additional pre-requisites 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 E4722 Stochastic Control and Financial Applications. 3 points.

Prerequisites: IEOR E4707 Refer to course syllabus.

This course covers stochastic control theory and applications in finance. It includes the following topics: 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; optimal stopping and variational inequalities; continuous-time expected utility maximization; continuous-time mean-variance portfolio selection and properties of efficient strategies; algorithmic and high frequency trading.

Fall 2020: IEOR E4722
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4722 001/11584 M W 8:40am - 9:55am
Online Only
Xunyu Zhou 3 16/80

IEOR E4723 Frontiers of Digital Finance. 1 point.

Course Points: 1.5

Topic Title: Introduction to Bitcoin, Cryptocurrencies, and Blockchain Investing. The use of data analytics, machine learning and AI is exploding globally within all corners of financial services – retail banking, asset and wealth management, payments, remittances, lending, capital markets and insurance. To generate meaningful and sustainable economic value, applications of data science have to be driven by strategic business priorities and guided by principles of responsible corporate citizenship. Such considerations are determined by senior executives knowledgeable about both business and operational AI/data science issues.

Fall 2020: IEOR E4723
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4723 001/11674 Th 1:10pm - 3:40pm
633 Seeley W. Mudd Building
Khosrow Dehnad 1 31/70

IEOR E4724 Python For Financial Engineering Bootcamp. 0 points.

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

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: (IEOR E4706) and (IEOR E4707) IEOR E4706 and IEOR E4707.

The course covers derivations and solutions of partial differential equations under variety of underlying stochastic price processes. Students will gain exposure to applications of partial differential equations to security pricing in different financial markets (i.e. equity derivatives, fixed income securities and credit derivative markets).

IEOR E4726 Applied Financial Risk Management. 3 points.

This course introduces risk management principles, with an emphasis on their practical implementation and application. It presents standard market, liquidity and credit risk measurement techniques, as well as their drawbacks and limitations. The course will convey much of the quantitative and technical material by working through calculation examples using market data and simple models. The example also introduce many sources of financial and statistical data, enabling students to better grasp the realities behind abstract financial concepts.


Students will understand risk management techniques from the viewpoint of practitioners, such as banks and other intermediaries. Many of these techniques have been adopted into financial regulatory standards. Especially since the crisis, regulatory standards have exerted great influence over firms’ risk management practices. The course will help understand this interaction, and the role of risk management in regulatory compliance.

IEOR E4727 Programming for Financial Engineering. 3 points.

Prerequisites: Refer to course syllabus.

This course covers features of the C++ programming language which are essential in financial engineering and its applications. We start by covering basic C++ programming features and then move to some more advance features. We utilize these features for financial engineering and quantitative finance applications.

IEOR E4729 Model Based Learning: Computational Methods of Analysis and Executio. 3 points.

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.

During the past few decades, financial markets have undergone a rapid expansion due to increases in computer power and proliferation of trading algorithms. At the same time there has been a significant increase in the number of trading venues, alternative trading mechanisms and protocols. All this has led to the development of a number of new and novel quantitatively-driven investment styles, mostly based on automated computer-driven trading strategies executed on electronic platforms.

,

This course will cover a variety of topics related to algorithmic trading, with the goal of giving students both a theoretical foundation and practical hands-on experience. Each class will introduce one or more concepts and then provide practical examples and simple implementations using Python, pandas and related tools. 

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The class will begin with an examination of market microstructure, trading mechanisms and transaction costs and move on to univariate time series analysis, forecasting, and an examination of trading strategies, including the application of multi-factor models, execution algorithms and an analysis of statistical arbitrage approaches. 

IEOR E4731 Credit Risk Modeling and Derivatives. 3 points.

Lect: 3.

Prerequisites: (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). Details topics include default and credit risk, multiname default barrier models and multiname reduced form models. 

IEOR E4732 Computational Methods in Finance. 3 points.

Prerequisites: (IEOR E4700)

Applications to 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, calibration and calibration techniques, filtering and parameter estimation techniques. Computational platform: C++/Java/Python/Matlab/R.

IEOR E4733 Algorithmic Trading. 3 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 Foreign Exchange and Its Related Derivative Instruments. 1.5 point.

1.5.

Prerequisites: (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. Yet ironically the foreign exchange markets may be the best place to trade derivatives and invent new derivatives - given the massive two-way flow of trading that goes though bank dealing rooms virtually twenty-four 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 and Hybrid Products. 3 points.

Lect: 3.

Prerequisites: (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 the Microsoft Excel/VBA and other programming environments. Special contractual provisions often encountered in structured and hybrid transactions, and attempt to incorporate yield curves, volatility smile, and other features of the underlying processes into pricing and implementation framework for these products.

Fall 2020: IEOR E4735
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4735 001/11591 Th 7:10pm - 9:40pm
207 Mathematics Building
Alireza Javaheri 3 90/100

IEOR E4736 Event-driven finance. 3 points.

Lect: 3.

Prerequisites: (IEOR E4700) or equivalent.

The course takes a long deep look at the actual behavior of real stocks and options in the presence of commonplace, but singular events, such as earnings take-overs, hard-to-borrowness, expirations, etc. The course introduces concepts to propose trading schema (we organize tests via the very extensive and robust IVY options/stock database) and carry out tests efficiently and accurately. It exposes students to the striking differences between the model-based (static, thermodynamic/SDE model solutions) behavior predicted for stocks and options and their real (often quite different) behavior. They will become familiar with computational techniques for modeling and testing proposals for trading strategies. 

IEOR E4738 Programming for FE 1: Tools for Building Financial Data and Risk Systems. 3 points.

Lect: 3.

Prerequisites: Familiarity with object-oriented programming.

Object-oriented programming and database development for building financial data and risk systems; Python and Python's scientific libraries; basic database theory, querying and construction database; basic risk management and design of risk systems.

IEOR E4739 Programming for FE 2: Implementing High Performance Financial Systems. 3 points.

Lect: 2.5.

Prerequisites: (IEOR E4738)

Developing effective software implementations in C programming language; modeling of portfolio optimization; modeling of price impact trading models; review of synchronization of programs using threads; review of synchronization of programs using sockets; implementation of high-performance simulations in finance. 

IEOR E4741 Programming for Financial Engineering. 3 points.

The course covers C++ programming language, applications and features for financial engineering and quantitative finance applications.

IEOR E4745 Applied Financial Risk Management. 3 points.

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

Prerequisites: see notes re: points

The course introduces risk management principles, practical implementation and applications, standard market, liquidity and credit risk measurement techniques, and their drawbacks and limitations.

IEOR E4798 Financial Engineering Seminar Series. 0 points.

Degree requirement for all MSFE first year students. Seminar Series for Financial Engineering Practitioners. Topics in the field of Financial Engineering. Past seminar topics include Evolving Financial Intermediation, Measuring and Using Trading Algorithms Effectively, Path-Dependent Volatility, Arti¹cial Intelligence and Data Science in modern financial decision making, Risk-Based Performance Attribution, and Financial Machine Learning. Meets select Monday evenings.

Fall 2020: IEOR E4798
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4798 001/15487 M 6:00pm - 7:30pm
Online Only
Ali Hirsa, Laura Radev 0 50/100

IEOR E4900 Master's Research or Project. 1-3 points.

Prerequisites: Approval by a faculty member who agrees to supervise the work.

Independent work involving experiments, computer programming, analytical investigation, or engineering design.

Fall 2020: IEOR E4900
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4900 001/22688  
Shipra Agrawal 1-3 0/20
IEOR 4900 002/22689  
Eric Balkanski 1-3 0/20
IEOR 4900 003/22690  
Daniel Bienstock 1-3 0/20
IEOR 4900 004/22691  
Agostino Capponi 1-3 0/20
IEOR 4900 005/22692  
Emanuel Derman 1-3 0/20
IEOR 4900 006/22693  
Antonius Dieker 1-3 0/20
IEOR 4900 007/22694  
Adam Elmachtoub 1-3 0/20
IEOR 4900 008/22695  
Yuri Faenza 1-3 0/20
IEOR 4900 009/22696  
Donald Goldfarb 1-3 0/20
IEOR 4900 010/22697  
Vineet Goyal 1-3 0/20
IEOR 4900 011/22698  
Ali Hirsa 1-3 2/20
IEOR 4900 012/22699  
Garud Iyengar 1-3 1/20
IEOR 4900 013/22700  
Hardeep Johar 1-3 0/20
IEOR 4900 014/22701  
Cedric Josz 1-3 0/20
IEOR 4900 015/22702  
Soulaymane Kachani 1-3 0/20
IEOR 4900 016/22703  
Christian Kroer 1-3 0/20
IEOR 4900 017/22704  
Daniel Lacker 1-3 0/20
IEOR 4900 018/22705  
Henry Lam 1-3 0/20
IEOR 4900 019/22706  
Uday Menon 1-3 0/20
IEOR 4900 020/22707  
Jay Sethuraman 1-3 0/20
IEOR 4900 021/22708  
Karl Sigman 1-3 0/20
IEOR 4900 022/22709  
Clifford Stein 1-3 0/20
IEOR 4900 023/22710  
Wenpin Tang 1-3 0/20
IEOR 4900 024/22711  
Van Anh Truong 1-3 0/20
IEOR 4900 025/22712  
Kaizheng Wang 1-3 0/20
IEOR 4900 026/22713  
Ward Whitt 1-3 0/20
IEOR 4900 027/22714  
David Yao 1-3 0/20
IEOR 4900 028/22715  
Yi Zhang 1-3 0/20
IEOR 4900 029/22716  
Xunyu Zhou 1-3 0/20
IEOR 4900 030/22717  
Jenny Mak 1-3 0/20

IEOR E4998 Managing Technological Innovation and Entrepreneurship. 3 points.

Lect: 3.

This is 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. 0.5-2 points.

Prerequisites: Obtained internship and approval from faculty advisor.

Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. This course may not be taken for pass/fail credit or audited.

Fall 2020: IEOR E4999
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 4999 001/11597  
Emanuel Derman, Ali Hirsa 0.5-2 78/500

IEOR E6602 Nonlinear Programming. 3 points.

Lect: 3.

Prerequisites: PhD-level Linear Programming.

Convex sets and functions, convex duality and optimality conditions. Computational methods: steepest descent, Newton and quasi-newton methods for unconstrained problems, active set, penalty set, interior point, augmented Lagrangian and sequential quadratic programming methods for constrained problems. Introduction to non-differentiable optimization and bundle methods.

IEOR E6613 Optimization, I. 4.5 points.

Prerequisites: Refer to course syllabus.

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.

Fall 2020: IEOR E6613
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 6613 001/11599 M W 10:10am - 11:25am
303 Seeley W. Mudd Building
Vineet Goyal 4.5 29/40

IEOR E6614 Optimization, II. 4.5 points.

Lect: 3.

Prerequisites: Refer to course syllabus.

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 E6703 Advanced Financial Engineering. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: Probability theory and advanced stochastic models at the SIEO GR6501 level.

Review of basic mathematics, including renewal theory and stochastic calculus. Martingale approach to Black-Scholes formula. Optimal stopping and American options. Pricing of continuous and discrete exotic options. Term structure models and pricing of bond options. Jump diffusion models. Applications, including pricing of real and electricity options and hedging of real options.

IEOR E6711 Stochastic models, I. 4.5 points.

Prerequisites: (STAT GU4001) or Refer to course syllabus.

This is the first course in a two-course sequence introducing students to the theory of stochastic processes. The fall term starts with a review of probability theory and then treats Poisson processes, renewal processes, discrete-time Markov chains and continuous-time Markov chains. The spring term emphasizes martingales and Brownian motion. Although the course does not assume knowledge of measure theory or measure-theoretic probability, the focus is on the mathematics. Proofs are emphasized. This course sequence is intended for our first-year doctoral students. Indeed, one of the two qualifying exams at the end of the first year covers the material taught in this course sequence. The course is intended to provide students background, so that they will be able to effectively conduct research.

Fall 2020: IEOR E6711
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 6711 001/11611 M W 8:40am - 9:55am
Online Only
Henry Lam 4.5 26/40

IEOR E6712 Stochastic models, II. 4.5 points.

Prerequisites: (IEOR E6711) or Refer to course syllabus.

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. 1-3 points.

Prerequisites: Faculty adviser's permission.

Selected topics of current research interest. May be taken more than once for credit. 

Fall 2020: IEOR E8100
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 8100 001/11612 M W 2:40pm - 3:55pm
Online Only
Shipra Agrawal 1-3 36/40
IEOR 8100 003/11616 M W 4:10pm - 5:25pm
203 Mathematics Building
Eric Balkanski 1-3 12/40

IEOR E9101 Research. 1-6 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. This course may be repeated for credit.

Fall 2020: IEOR E9101
Course Number Section/Call Number Times/Location Instructor Points Enrollment
IEOR 9101 001/22718  
Shipra Agrawal 1-6 1/20
IEOR 9101 002/22719  
Eric Balkanski 1-6 0/20
IEOR 9101 003/22720  
Daniel Bienstock 1-6 0/20
IEOR 9101 004/22721  
Agostino Capponi 1-6 0/20
IEOR 9101 005/22722  
Emanuel Derman 1-6 0/20
IEOR 9101 006/22723  
Antonius Dieker 1-6 0/20
IEOR 9101 007/22724  
Adam Elmachtoub 1-6 0/20
IEOR 9101 008/22725  
Yuri Faenza 1-6 2/20
IEOR 9101 009/22726  
Donald Goldfarb 1-6 0/20
IEOR 9101 010/22727  
Vineet Goyal 1-6 2/20
IEOR 9101 011/22728  
Ali Hirsa 1-6 1/20
IEOR 9101 012/22729  
Garud Iyengar 1-6 1/20
IEOR 9101 013/22730  
Hardeep Johar 1-6 0/20
IEOR 9101 014/22731  
Cedric Josz 1-6 0/20
IEOR 9101 015/22732  
Soulaymane Kachani 1-6 0/20
IEOR 9101 016/22733  
Christian Kroer 1-6 1/20
IEOR 9101 017/22734  
Daniel Lacker 1-6 0/20
IEOR 9101 018/22735  
Henry Lam 1-6 3/20
IEOR 9101 019/22736  
Uday Menon 1-6 0/20
IEOR 9101 020/22737  
Jay Sethuraman 1-6 0/20
IEOR 9101 021/22738  
Karl Sigman 1-6 0/20
IEOR 9101 022/22739  
Clifford Stein 1-6 1/20
IEOR 9101 023/22740  
Wenpin Tang 1-6 0/20
IEOR 9101 024/22741  
Van Anh Truong 1-6 1/20
IEOR 9101 025/22742  
Kaizheng Wang 1-6 0/20
IEOR 9101 026/22743  
Ward Whitt 1-6 0/20
IEOR 9101 027/22744  
David Yao 1-6 2/20
IEOR 9101 028/22745  
Yi Zhang 1-6 0/20
IEOR 9101 029/22746  
Xunyu Zhou 1-6 1/20
IEOR 9101 030/22747  
Jenny Mak 1-6 0/20

CSOR E4010 Graph Theory: A Combinatorial View. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: Linear Algebra, or instructor's permission.

Graph Theory is an important part of the theoretical basis of operations research. A good understanding of the basic fundamentals of graph theory is necessary in order to apply the theory successfully in the future. This is an introductory course in graph theory with emphasis on its combinatorial aspects. It covers basic definitions, and some fundamental concepts in graph theory and its applications. Topics include trees and forests graph coloring, connectivity, matching theory and others. This course will provide a solid foundation for students in the IEOR department, on which further courses may build.

CSOR W4231 Analysis of Algorithms I. 3 points.

Lect: 3.

Prerequisites: (COMS W3134 or COMS W3136COMS 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.

Fall 2020: CSOR W4231
Course Number Section/Call Number Times/Location Instructor Points Enrollment
CSOR 4231 001/11668 T Th 10:10am - 12:40pm
Online Only
Mihalis Yannakakis 3 126/135
CSOR 4231 002/11694 M T W Th 5:40pm - 6:55pm
Online Only
Alexandr Andoni 3 115/150
CSOR 4231 V02/21558  
Alexandr Andoni 3 14/99

COSA E9800 Data Science Doctoral Seminar. 1 point.

Not offered during 2020-21 academic year.

The Data Science Doctoral Seminar is a 1-credit course that meets weekly. The purpose is to expose the doctoral students to a breadth of ideas in data science across disciplinary domains. The syllabus combines guest lectures from academic data scientists in the greater NYC area and faculty at Columbia, along with a selection of related readings chosen by the guest lecturers. As part of this seminar, students will be expected to engage in active open discussion about the topics and readings covered in class, as well as discuss how such topics apply to their own respective research areas.

IEME E4200 Human-Centered Design & Innovation. 3 points.

Lect: 2.5 hrs. Prerequisite: Instructor’s permission.

Prerequisites: see notes re: points By application and instructor approval.

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 E4310 The Manufacturing Enterprise. 3 points.

Lect: 3.

The strategies and technologies of global manufacturing and service enterprises. Connections between the needs of a global enterprise, the technology and methodology needed for manufacturing and product development, and strategic planning as currently practiced in industry.  

MEIE E4810 Introduction to Human Spaceflight. 0 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. 

MSIE W6408 Inventory Theory. 3 points.

Lect: 3.Not offered during 2020-21 academic year.

Prerequisites: Probability theory, dynamic programming.

Construction and analysis of mathematical models used in the design and analysis of inventory systems. Deterministic and stochastic demands and lead times. Optimality of (s, S) policies. Multiproduct and multiechelon systems. Computational methods.