TOPICS IN OPERATIONS RESEARCH

AI Engineering Apps

Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment.

• S 11 a.m.-1:30 p.m.
• 1.5 points

TOPICS IN OPERATIONS RESEARCH

Comm for Data Leaders

Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment.

• F 1 p.m.-4 p.m.
• 1.5 points

TOPICS IN OPERATIONS RESEARCH

Gen AI Rec Sys

Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment.

• M 7 p.m.-9:30 p.m.
• Instructor: Kenneth Goodman
  Info
• 1.5 points

TOPICS IN OPERATIONS RESEARCH

AI Startup Fundamentals

Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment.

• T 6:10 p.m.-8:40 p.m.
• Instructor: Owen E Davis
  Info
• 1.5 points

Business Analytics Practitioners Seminar

Business Analytics Semina

The IEOR Department hosts Business Analytics Practitioners Seminars throughout the Fall and Spring semesters. There are 5-6 seminars per semester, for a total of 10-12 seminars a year. These seminars feature a variety of academics and professionals in the field of Business Analytics; they are meant to educate and expose students to the latest research and practice. 
First year BA students are required to attend all BA seminars during their first year
as well as submit journals about the material.

• R 7 p.m.-9 p.m.
• Instructor: Uday Menon
  Info
• 0 points

MSBA Quantitative Bootcamp

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

• Instructor: Michael Miller
  CULPA: 18 h-index: 14 Info
• 0 points

QUANTITATIVE RISK MANAGEMENT

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

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Agostino Capponi
  h-index: 19 Info
• 3 points

PRICING MODELS FOR FIN ENGIN

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.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Michael Miller
  CULPA: 18 h-index: 14 Info
• 3 points

BUSINESS ANALYTICS

This course focuses on how to identify, evaluate, and capture business analytic opportunities that create value. The course covers basic analytic methods alongside case studies on organizations that successfully deployed these techniques. The first part of the course is on using data to develop insights and predictive capabilities with machine learning techniques. The second part focuses on the use of A/B testing, causal inference, ethics, and optimization to support decision-making.

• TR 11:30 a.m.-12:45 p.m.
• Instructor: Uday Menon
  Info
• 3 points

INTRO TO FINANCIAL ENGINEERING

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

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Xunyu Zhou
  h-index: 54 Info
• 3 points

STOCHASTIC MODELS FOR FIN ENG

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

• MW 8:40 a.m.-9:55 a.m.
• Instructor: David D Yao
  CULPA: 5 Info
• 3 points

FOUNDATIONS FR FINANCIAL ENGIN

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

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Wenpin Tang
  h-index: 6 Info
• 3 points

GLOBAL CAPITAL MARKETS

• M 6 p.m.-8:30 p.m.
• Instructor: Siddhartha Ghosh Dastidar
  CULPA: 2 Info
• 3 points

TOPICS IN QUANTATIVE FINANCE

AI Investment Mgmt

The course examines how large language models are transforming investment management practice. It opens with a grounding in core AI techniques (prompting strategies, Retrieval-Augmented Generation, and agentic frameworks) before mapping these onto institutional investment and wealth management workflows.
The bulk of the course explores specific applications: extracting and monitoring risk factors from equity data and unstructured text using AI, building real-time news monitoring systems that score sentiment and flag portfolio-relevant events, and using LLMs to generate stress-test scenarios. Later sessions cover multi-agent systems for automated research and trading, including agent roles, guardrails, and human oversight, and how these techniques can enhance classical portfolio construction methods (risk parity, mean-variance optimization, Black-Litterman). The course includes guest lectures by industry practitioners discussing the industry's AI adoption trends.

• T 7:10 p.m.-9:40 p.m.
• Instructor: Cyril Shmatov
  Info
• 1.5 points

TOPICS IN QUANTITATIVE FINANCE

ALTERNATIVE INVESTMENTS

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

• R 1:10 p.m.-3:40 p.m.
• Instructor: Khosrow Dehnad
  CULPA: 2 Info
• 1.5 points

FOR EXCH/RELATD DERIVATVS INST

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

• T 7:10 p.m.-9:40 p.m.
• Instructor: David Derosa
  CULPA: 4 h-index: 9 Info
• 1.5 points

STRUCTURED&HYBRID PRODUCTS

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

• R 7:10 p.m.-9:40 p.m.
• Instructor: Alireza Javaheri
  Info
• 3 points

Programming for Financial Engineering

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

• W 7:10 p.m.-9:40 p.m.
• Instructor: Sebastien Donadio
  Info
• 3 points

Deep Learning for OR and FE

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

• FS 9 a.m.-6 p.m.
• Instructor: Ali Hirsa
  h-index: 8 Info
• 3 points

Applied Financial Risk Management

Applied Financial Risk Mg

Introduces risk management principles, practical implementation and applications, standard market, liquidity, and credit risk measurement techniques, and their drawbacks and limitations. Note: restricted to IEOR students only.

• T 7 p.m.-9 p.m.
• Instructor: Allan M Malz
  CULPA: 1 Info
• 3 points

Financial Engineering Practitioners Semi

FE Seminar Series

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

• M 7:05 p.m.-9:05 p.m.
• Instructor: Ali Hirsa
  h-index: 8 Info
• 0 points

MSFE Quantitative and Computational Boot

MSFE Quant/Comp Bootcamp

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

• 0 points

MASTERS RESEARCH OR PROJECT

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

• Instructor: Anish Agarwal
  Info
• Instructor: Shipra Agrawal
  h-index: 24 Info
• Instructor: Eric Balkanski
  h-index: 13 Info
• Instructor: Daniel Bienstock
  h-index: 38 Info
• Instructor: Agostino Capponi
  h-index: 19 Info
• Instructor: Rachel Cummings
  h-index: 16 Info
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• Instructor: Adam N Elmachtoub
  CULPA: 1 h-index: 13 Info
• Instructor: Yuri Faenza
  h-index: 11 Info
• Instructor: Vineet Goyal
  h-index: 21 Info
• Instructor: Ali Hirsa
  h-index: 8 Info
• Instructor: Anran Hu
  Info
• Instructor: Garud Iyengar
  CULPA: 6 h-index: 30 Info
• 1-3 points

MASTERS RESEARCH OR PROJECT

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

• Instructor: Garud Iyengar
  CULPA: 6 h-index: 30 Info
• Instructor: Hardeep Johar
  Info
• Instructor: Cedric Josz
  CULPA: 2 h-index: 13 Info
• Instructor: Soulaymane Kachani
  CULPA: 12 Info
• Instructor: Yaren Kaya
  Info
• Instructor: Christian Kroer
  h-index: 16 Info
• Instructor: Daniel Lacker
  CULPA: 4 h-index: 13 Info
• Instructor: Henry Lam
  h-index: 17 Info
• Instructor: Fabrizio Lecci
  Info
• Instructor: Tianyi Lin
  Info
• Instructor: Uday Menon
  Info
• Instructor: Bento Natura
  Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Eric Stratman
  Info
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• Instructor: Wenpin Tang
  h-index: 6 Info
• Instructor: Harry West
  CULPA: 1 h-index: 1 Info
• Instructor: Kaizheng Wang
  CULPA: 1 h-index: 8 Info
• Instructor: Lily Xu
  Info
• Instructor: David D Yao
  CULPA: 5 Info
• Instructor: Yi Zhang
  CULPA: 2 Info
• Instructor: Xunyu Zhou
  h-index: 54 Info
• Instructor: Kelly Katsigris
  Info
• Instructor: Yuchen Hu
  Info
• 1-3 points

FIELDWORK

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

• 1-2 points

FIELDWORK

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

• 1-2 points

FIELDWORK

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

• 1-2 points

OPTIMIZATION I

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.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Vineet Goyal
  h-index: 21 Info
• 4.5 points

Machine Learning and High-Dimensional Da

Mchn lrning&Hgh-dmntnl da

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

• M 7:10 p.m.-9:40 p.m.
• Instructor: Krzysztof M Choromanski
  Info
• 3 points

STOCHASTIC MODELING I

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

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Henry Lam
  h-index: 17 Info
• 4.5 points

OPTIMIZATION & SIMULATION BOOTCAMP

• 0 points

ADVANCED TOPICS IN IEOR

Diffusion Models AI & RL

• T 2:40 p.m.-5:10 p.m.
• Instructor: Xunyu Zhou
  h-index: 54 Info
• 3 points

ADVANCED TOPICS IN IEOR

Algo in a World of ML

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• 3 points

ADVANCED TOPICS IN IEOR

MATCHING MARKETS & ALGORITHMS

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Yuri Faenza
  h-index: 11 Info
• 3 points

ADVANCED TOPICS IN IEOR

Comput. Social Choice

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• 3 points

RESEARCH

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

• Instructor: Anish Agarwal
  Info
• Instructor: Shipra Agrawal
  h-index: 24 Info
• Instructor: Eric Balkanski
  h-index: 13 Info
• Instructor: Daniel Bienstock
  h-index: 38 Info
• Instructor: Agostino Capponi
  h-index: 19 Info
• Instructor: Rachel Cummings
  h-index: 16 Info
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• Instructor: Adam N Elmachtoub
  CULPA: 1 h-index: 13 Info
• Instructor: Yuri Faenza
  h-index: 11 Info
• Instructor: Vineet Goyal
  h-index: 21 Info
• Instructor: Ali Hirsa
  h-index: 8 Info
• Instructor: Anran Hu
  Info
• Instructor: Garud Iyengar
  CULPA: 6 h-index: 30 Info
• 1-6 points

RESEARCH

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

• Instructor: Garud Iyengar
  CULPA: 6 h-index: 30 Info
• Instructor: Hardeep Johar
  Info
• Instructor: Cedric Josz
  CULPA: 2 h-index: 13 Info
• Instructor: Soulaymane Kachani
  CULPA: 12 Info
• Instructor: Yaren Kaya
  Info
• Instructor: Christian Kroer
  h-index: 16 Info
• Instructor: Daniel Lacker
  CULPA: 4 h-index: 13 Info
• Instructor: Henry Lam
  h-index: 17 Info
• Instructor: Fabrizio Lecci
  Info
• Instructor: Tianyi Lin
  Info
• Instructor: Uday Menon
  Info
• Instructor: Bento Natura
  Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Eric Stratman
  Info
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• Instructor: Wenpin Tang
  h-index: 6 Info
• Instructor: Harry West
  CULPA: 1 h-index: 1 Info
• 1-6 points