Frontiers in Operations Research and Dat
Frontiers in OR&Data Anal
Introductory course for overview of modern approaches and ideas of operations research and data analytics. Through a series of interactive sessions, students engage in activities exploring OR topics with various faculty members from the IEOR department.
Data Engineering with Python
Introduction to essential data engineering methods. Potential topics include Arrays, Linked Lists, Stacks and Queues, Trees and Graphs, Hash Tables, Search Algorithms and Efficiency, Relational databases, SQL, NoSQL, and Data Wrangling. Practice both theory and applications using Python programming.
FOUNDATIONS OF DATA SCIENCE
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks. At least one project will address a problem relevant to New York City.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Daniel Fernandez
Info - 3 points
PRODUCTN-INVENTORY PLAN-CONTRL
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Must be taken during (or before) the sixth semester. Inventory management and production planning. Continuous and periodic review models: optimal policies and heuristic solutions, deterministic and probabilistic demands. Material requirements planning. Aggregate planning of production, inventory, and work force. Multi-echelon integrated production-inventory systems. Production scheduling. Term project. Recitation section required.
- F 11 a.m.-noon
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Instructor:
Ali Sadighian
CULPA: 3 h-index: 3 Info - 4 points
SIMULATION MODELING AND ANALYSIS
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.
ADVANCED OPTIMIZATION
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. This is a follow-up to IEOR E3608 and will cover advanced topics in optimization, including integer optimization, convex optimization, and optimization under uncertainty, with a strong focus on modeling, formulations, and applications.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Bento Natura
Info - 3 points
PROBABILITY FOR ENGINEERS
Introductory course to probability theory and does not assume any prior knowledge of subject. Teaches foundations required to use probability in applications, but course itself is theoretical in nature. Basic definitions and axioms of probability and notions of independence and conditional probability introduced. Focus on random variables, both continuous and discrete, and covers topics of expectation, variance, conditional distributions, conditional expectation and variance, and moment generating functions. Also Central Limit Theorem for sums of random variables. Consists of lectures, recitations, weekly homework, and in-class exams.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
Info
- 3 points
Research Immersion in OR and Data Analyt
An overview of active research areas in Operations Research and Data Analytics, and an introduction to the essential components of research studies. This course helps students develop fundamental research skills, including paper reading, problem formulation, problem-solving, scientific writing, and research presentation. Classes are in seminar format, with students analyzing research papers, developing research projects, and presenting research findings.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
Adam N Elmachtoub
CULPA: 1 h-index: 13 Info - 3 points
UNDERGRAD RESEARCH OR PROJECT
Independent work involving experiments, computer programming, analytical investigation, or engineering design.
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Instructor:
Anish Agarwal
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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
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Instructor:
Garud Iyengar
CULPA: 6
h-index: 30
Info
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Instructor:
Hardeep Johar
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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
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Instructor:
Henry Lam
h-index: 17 Info -
Instructor:
Fabrizio Lecci
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Instructor:
Tianyi Lin
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Instructor:
Uday Menon
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Instructor:
Bento Natura
Info -
Instructor:
Jay Sethuraman
CULPA: 10 Info -
Instructor:
Karl Sigman
CULPA: 15
Info
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Instructor:
Eric Stratman
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info -
Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info -
Instructor:
Wenpin Tang
h-index: 6 Info -
Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info -
Instructor:
Lily Xu
Info -
Instructor:
David D Yao
CULPA: 5
Info
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Instructor:
Yi Zhang
CULPA: 2 Info -
Instructor:
Xunyu Zhou
h-index: 54 Info -
Instructor:
Christine Chan
Info -
Instructor:
Kelly Katsigris
Info - 1-3 points
OPTIMIZATION MODELS AND METHODS
A graduate course only for MS&E, IE, and OR students. This is also required for students in the Undergraduate Advanced Track. For students who have not studied linear programming. Some of the main methods used in IEOR applications involving deterministic models: linear programming, the simplex method, nonlinear, integer and dynamic programming.
- MW 1:10 p.m.-2:25 p.m.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Yaren Kaya
Info - 3 points
Artificial Intelligence for OR and FE
AI for OR & FE
Introduces modern artificial intelligence (AI), focus on applications to operations and decision-making in domains such as healthcare, finance, energy systems, and supply chain management. Topics: machine learning, neural networks, large language models (LLMs), reinforcement learning, game theory, multi-agent systems, and agentic frameworks.
- MW 10:10 a.m.-11:25 a.m.
- 3 points
STOCHASTIC MODELING FOR MSE
Introduction to stochastic processes and models, with emphasis on applications to engineering and management; random walks, gambler’s ruin problem, Markov chains in both discrete and continuous time, Poisson processes, renewal processes, stopping times, Wald’s equation, binomial lattice model for pricing risky assets, simple option pricing; simulation of simple stochastic processes, Brownian motion, and geometric Brownian motion. A specialized version of IEOR E4106 for MSE students.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Karl Sigman
CULPA: 15
Info
- 3 points
STOCHASTIC MODELS
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.
- MW 5:40 p.m.-6:55 p.m.
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Instructor:
Antonius B Dieker
CULPA: 10 h-index: 19 Info - 3 points
SUPPLY CHAIN ANALYTICS
Supply chain management, model design of a supply chain network, inventories, stock systems, commonly used inventory models, supply contracts, value of information and information sharing, risk pooling, design for postponement, managing product variety, information technology and supply chain management; international and environmental issues. Note: replaced IEOR E4000 beginning in fall 2018.
- TR 1:10 p.m.-2:25 p.m.
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Instructor:
Elioth Sanabria Buenaventura
Info - 3 points
OPERATIONS CONSULTING
Aims to develop and harness the modeling, analytical, and managerial skills of engineering students and apply them to improve the operations of both service and manufacturing firms. Structured as a hands-on laboratory in which students "learn by doing" on real-world consulting projects (October to May). The student teams focus on identifying, modeling, and testing (and sometimes implementing) operational improvements and innovations with high potential to enhance the profitability and/or achieve sustainable competitive advantage for their sponsor companies. The course is targeted toward students planning careers in technical consulting (including operations consulting) and management consulting, or pursuing positions as business analysts in operations, logistics, supply chain and revenue management functions, positions in general management, and future entrepreneurs.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
Soulaymane Kachani
CULPA: 12 Info - 3 points
MSIEOR Quantitative Bootcamp
Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students.
- 0 points
HUMAN-CENTERED DESIGN AND INNOVATION
HUMAN-CENTERED DESIGN & I
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.
- W 11:25 a.m.-12:40 p.m.
- W 4:10 p.m.-5:25 p.m.
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Instructor:
Harry West
CULPA: 1 h-index: 1 Info - 3 points
Policy for Privacy Technologies
Introduction to privacy technologies, their use in practice, and privacy regulations. Potential topics include anonymization, differential privacy, cryptography, secure multi-party computation, and legislation. Course material will be abased in real-world use cases of these tools.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Rachel Cummings
h-index: 16 Info - 3 points
ANALYSIS OF ALGORITHMS I
Prerequisites: COMS W3134, COMS W3136, or COMS W3137, and COMS W3203. Introduction to the design and analysis of efficient algorithms. Topics include models of computation, efficient sorting and searching, algorithms for algebraic problems, graph algorithms, dynamic programming, probabilistic methods, approximation algorithms, and NP-completeness.
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Daniel Bienstock
h-index: 38 Info - 3 points
Corporate Finance, Accounting & Investme
CorpFin, Account & Invest
For students considering working in Investment Banking or in the Finance department of a Corporation. After a brief two-lecture intro to Accounting, it focuses on Corporate Finance. [Important: class will not meet Jan 29, 2026 and will instead have a make-up class on a Friday!] Interpret financial statements, build cash flow models, value projects, value companies, and make Corporate Finance decisions. Additional topics include: cost of capital, dividend policy, debt policy, impact of taxes, Shareholder / Debtholder agency costs, dual-class shares, using option pricing theory to analyze management behavior, investment banking activities, including equity underwriting, syndicated lending, venture capital, private equity investing, private equity secondaries, capital structure arbitrage. Application of theory in real-world situations: analyzing financial activities of companies such as Google, Tesla, etc.
- R 4:30 p.m.-7 p.m.
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Instructor:
Rodney Sunada-Wong
Info - 3 points
SIMULATION
Generation of random numbers from given distributions; variance reduction; statistical output analysis; introduction to simulation languages; application to financial, telecommunications, computer, and production systems. Graduate students must register for 3 points. Undergraduate students must register for 4 points. Note: Students who have taken IEOR E4703 Monte Carlo simulation may not register for this course for credit. Recitation section required.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Henry Lam
h-index: 17 Info - 3 points
PRODUCTION SCHEDULING
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.
- TR 8:40 a.m.-9:55 a.m.
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info - 3 points
OPERATION RES IN PUBLIC POLICY
OR in Public Policy
Aims to give the student a broad overview of the role of Operations Research in public policy. The specific areas covered include voting theory, apportionment, deployment of emergency units, location of hazardous facilities, health care, organ allocation, management of natural resources, energy policy, and aviation security. Draws on a variety techniques such as linear and integer programming, statistical and probabilistic methods, decision analysis, risk analysis, and analysis and control of dynamic systems.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Eric Stratman
Info - 3 points
PROJECT MANAGEMENT
Project Mgmt of Complex System
Management of complex projects and the tools that are available to assist managers with such projects. Topics include project selection, project teams and organizational issues, project monitoring and control, project risk management, project resource management, and managing multiple projects.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Dave Begun
Info - 3 points
Industry Projects in Analytics & Operati
Industry Projs in Analyti
Teams of students work on real-world projects in analytics. Focus on three aspects of analytics: identifying client analytical requirements; assembling, cleaning and organizing data; identifying and implementing analytical techniques (e.g., statistics and/or machine learning); and delivering results in a client-friendly format. Each project has a defined goal and pre-identified data to analyze in one semester. Client facing class. Class requires 10 hours of time per week and possible client visits on Fridays.
- M 9 a.m.-11:30 a.m.
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Instructor:
Michael Robbins
Wikipedia Info - 3 points
Data Analytics and Machine Learning
Data Analytics and ML
IEOR students only; priority to MSBA students. Practical survey of Python tools for acquiring, cleaning, and analyzing data. Techniques for obtaining data from files, web scraping, and APIs (CSV, HTML, JSON, XML); performing core data-cleaning tasks; and using data analysis, machine learning, and visualization libraries (NumPy, Pandas, Matplotlib, Seaborn, TensorFlow/Keras). Introduces foundational machine learning and deep learning concepts, including backpropagation, gradient descent, and implementation of neural networks with TensorFlow/Keras. Covers text mining using word, sentence, and document embeddings. Includes a group project requiring students to collect, store, and analyze a dataset of their choice and build a predictive model.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Uday Menon
Info - 3 points
ANALYTICS IN PRACTICE
MSBA students only. Groups of students will work on real world projects in analytics, focusing on three aspects: identifying client analytical requirements; assembling, cleaning, and organizing data; identifying and implementing analytical techniques (statistics, OR, machine learning); and delivering results in a client-friendly format. Each project has a well-defined goal, comes with sources of data preidentified, and has been structured so that it can be completed in one semester. Client-facing class with numerous on-site client visits; students should keep Fridays clear for this purpose.
- R 7:10 p.m.-9:40 p.m.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
Hardeep Johar
Info -
Instructor:
Yaren Kaya
Info - 3 points
MACHINE LEARNING FE & OPR
MS IEOR students only. Introduction to machine learning, practical use of ML algorithms and applications to financial engineering and operations. Supervised learning: regression, classification, resampling methods, regularization, support vector machines (SVMs), and deep learning. Unsupervised learning: dimensionality reduction, matrix decomposition, and clustering algorithms.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Daniel Bienstock
h-index: 38 Info - 3 points
Visualization and Storytelling with Data
Data Visualization
Data visualization and how to build a story with data. Using complex data or statistics to communicate results effectively. Learn to present analysis and results conscisely and effectively.
- F 9 a.m.-5 p.m.
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Instructor:
Michelle Glaser
Info - 1.5 points
Performance, Objectives, & Results Using
Performance,Objectives&Re
OKR framework and different variations. Measurement techniques (A/B testing, validation, correlation, etc.) Identifying what to measure in product experience and business initiatives. Data-driven decision making.
- FSU 9 a.m.-5 p.m.
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Instructor:
Nicolas Chikhani
Info - 1.5 points
Applied Analytics: from Data to Decision
Applied Analytic Data-Dec
Applied Analytics focus querying and transforming data with SQL, defining and visualizing metrics, measuring impact of products / processes. Tools and techniques to convert raw data to business decisions, statistical analysis. Be able to apply these techniques to real-world datasets.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Fabrizio Lecci
Info - 3 points
DATA MINING
Course covers major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classification, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students learn about principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fitting and parameter tuning, and how to apply methods in practice and assess their performance. Emphasizes roles of statistical modeling and optimization in data mining.
- W 7 p.m.-9:30 p.m.
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Instructor:
Krzysztof M Choromanski
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
Queueing Theory
This course is designed as an introductory exposure to entrepreneurial concepts and practical skills for engineering students (and others) who wish to explore entrepreneurship conceptually or as a future endeavor in their careers. The class will be a mix of lecture, discussion, team-building and in-the-field workshopping of concepts we cover.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Karl Sigman
CULPA: 15
Info
- 3 points
TOPICS IN OPERATIONS RESEARCH
Tech Ethics in AI
The last decade of 20 th century witnessed a rapid convergence of three C’s: Communications, Computers, and Consumer Electronics. This convergence has given us the Internet, smart phones, and an abundance of data with Data Science playing a major role in analyzing these data and providing predictive analytics that lead to actionable items in many fields and businesses. Finance is a field with a large amount of information and data that can utilize the skills of Data Scientists, however, to be effective in this field a data scientist, in addition to analytic knowledge, should also be knowledgeable of the working, instruments, and conventions of financial markets that range from Foreign Exchange to Equities, Bonds, Commodities, Cryptocurrencies and host of other asset classes. The objective of this course is to provide Data Science students with a working knowledge of major areas of finance that could help them in finding a position in the Financial Industry. The wide range of topics covered in this course besides expanding the range of positions where students could be a fit, it gives them more flexibility in their job search. The course will also be of value to them in managing their own finances in the future.
- F 3 p.m.-5:30 p.m.
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Instructor:
Gary Kazantsev
Info - 1.5 points
TOPICS IN OR
Business App of LLMs
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- W 5:40 p.m.-8:10 p.m.
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Instructor:
Mahir Yavuz
Info - 1.5 points
TOPICS IN OR
Analytics Engineering
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Fabrizio Lecci
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
Agentic AI for Analytics
This Columbia University course offers a project-based learning experience focused on systematic quantitative investment. It covers the full data science workflow, from concept to performance evaluation. Students will engage in a real-time financial forecasting competition, using open-source financial and alternative data, to make and present investment decisions. Ideally, this course suits students aspiring to careers as quants or data scientists in the financial sector.
- M 7 p.m.-9:30 p.m.
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Instructor:
Devon Peticolas
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
Comm for Data Leaders
The course focuses on a PRACTICAL study of how to quantify & predict RISK in organizations by using learnings from: Regression analysis; Monte Carlo simulation; Factor analysis; Cohort analysis; Cluster analysis; Time series analysis; Sentiment analysis. Expectation is that incoming students should have a basic understanding of such concepts and statistics. The course will offer meeting & listening to CXO's & top executives from companies who have implemented robust AI & Applied Risk solutions to solve real-world problems in their own industries.
It will give students a great opportunity to learn practical applications of predictive analytics to solve real business problems
- F 1 p.m.-4 p.m.
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Instructor:
Casandra Campbell
Info - 1.5 points
TOPICS IN OPERATION RESEARCH
Forecasting realworld app
By taking this course, students will gain the tools and knowledge to develop a comprehensive new venture that is scalable, repeatable and capital efficient. The course will help students formulate new business ideas through a process of ideation and testing. Students will test the viability of their ideas in the marketplace and will think through the key areas of new venture. The first part of the course will help students brainstorm about new ideas and test the basic viability of those ideas through of process of design and real world tests. After an idea is developed students will work towards finding a scalable, repeatable business model. We will cover customer discovery, market sizing, pricing, competition, distribution, funding, developing a minimal viable product and many other facets of creating a new venture. The course will end with students having developed a company blueprint and final investor pitch. Course requirements include imagination, flexibility, courage, getting out of the building, and passion.
- R 7 p.m.-9:30 p.m.
- 3 points
TOPICS IN OR
MACHINE LEARNING IN PRACT
In this course, you'll leverage student engagement data to create a photo and text recommendation app similar to Instagram/Twitter. This app will utilize AI-generated photos and text and require you to recommend a feed from over 500,000 pieces of AI generated content. We'll explore various techniques to achieve this, including, but not limited to: Candidate Generation (Collaborative filtering, Trending, Cold start, N-tower neural network models, Cross-attention teachers, Distillation, Transfer learning, Random graph walking, Reverse indexes, LLMs as embedding), Filtering (Small online models, Caching, Deduplication, Policy), Prediction/Bidding (User logged activity based prediction (time-series), Multi-gate mixture of experts (MMOE), Regularization, Offline/Online evaluation (NDCG, p@k, r@k), Boosted Trees, Value Based Bidding), Ranking (Re-ranking, Ordering, Diversity, Enrich/Metadata/Personalization, Value Functions), Misc (Data Privacy and AI Ethics, Creator Based Models, Declared, Explicit and implicit topics, Explore/Exploit, Interpret/Understand/Context/Intention).
These concepts are applicable to various recommendation systems, from e-commerce to travel to social media to financial modeling. The instructor's experience at Uber Eats, Facebook, Instagram, and Google will provide valuable insights into real-world use cases.
- T 5:40 p.m.-8:10 p.m.
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Instructor:
Gary Kazantsev
Info - 3 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.
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Instructor:
Uday Menon
Info - 0 points
DYNAMIC PRICING/REVENUE MGMT
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.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Vineet Goyal
h-index: 21 Info - 3 points
ASSET ALLOCATION
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).
- T 2:40 p.m.-5:10 p.m.
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Instructor:
Christopher A Perez
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 2:40 p.m.-3:55 p.m.
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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.
MONTE CARLO SIMULATION METHODS
Monte Carlo Simulation
This graduate course is only for M.S. Program in Financial Engineering students. Multivariate random number generation, bootstrapping, Monte Carlo simulation, efficiency improvement techniques. Simulation output analysis, Markov-chain Monte Carlo. Applications to financial engineering. Introduction to financial engineering simulation software and exposure to modeling with real financial data. Note: Students who have taken IEOR E4404 Simulation may not register for this course for credit.
- TR 8:40 a.m.-9:55 a.m.
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Instructor:
Ali Hirsa
h-index: 8 Info - 3 points
Foundations of Financial Technology
Foundations of FinTech
Introduces emerging area of Financial Technology (FinTech) within decentralized finance (DeFi) and blockchains. Topics: blockchain fundamentals: major (permissionless) blockchain protocols, permissioned blockchains, and their applications; DeFi fundamentals: Layer-2 primitives (AMMs, lending, liquidity staking, MEV, etc) and stablecoins; markets and trading: market microstructure and on-chain data analytics; and emerging applications: institutional DeFi, real-world asset tokenizations (RWAs), and AI integration. Emphasis on both mathematical foundations and empirical designs.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Wenpin Tang
h-index: 6 Info - 3 points
FE CONTINUOUS TIME MODELS
FE: CONTINUOUS TIME MODEL
This graduate course is only for MS program in FE students. Modeling, analysis, and computation of derivative securities. Applications of stochastic calculus and stochastic differential equations. Numerical techniques: finite-difference, binomial method, and Monte Carlo.
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Xunyu Zhou
h-index: 54 Info - 3 points
STATISTICAL ANALYSIS AND TIME SERIES
STATISTICAL ANALYSIS & TIME SE
This graduate course is only for M.S. Program in Financial Engineering students. Empirical analysis of asset prices: heavy tails, test of the predictability of stock returns. Financial time series: ARMA, stochastic volatility, and GARCH models. Regression models: linear regression and test of CAPM, non-linear regression and fitting of term structures.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Agostino Capponi
h-index: 19 Info - 3 points
INTRO-IMPLIED VOLATILITY SMILE
BEYOND BLACK-SCHOLES: IMPLIED
During the past 15 years the behavior of market options prices have shown systematic deviations from the classic Black-Scholes model. Examines the empirical behavior of implied volatilities, in particular the volatility smile that now characterizes most markets, the mathematics and intuition behind new models that can account for the smile, and their consequences for hedging and valuation.
- R 7 p.m.-9:30 p.m.
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Instructor:
Amal Moussa
h-index: 7 Info - 3 points
TOPICS IN QUANT FINANCE
Comp. Portfolio Construct
This graduate-level course provides a rigorous foundation in quantitative portfolio construction, bridging theory with practical implementation. It develops a systematic framework for constructing optimal portfolios, beginning with the classical Markowitz model and enhancing it to address real-world portfolio objectives and constraints. Students learn how forecasts of asset returns, risk, and market impact are formulated in the optimizer objectives and constraints, and how estimation error affects portfolio construction and performance. They also study the robust estimation of key optimizer inputs such as risk models using modern statistical and econometric methods that handle noise and time-varying dynamics. Students gain hands-on experience constructing portfolios using industry-standard datasets, tools, and risk models. The course also provides a brief introduction to advanced topics such as random matrix theory, multiperiod optimization, and the use of modern machine learning methods, which represent emerging directions in quantitative portfolio research.
- W 4:10 p.m.-6:40 p.m.
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Instructor:
Sridhar Gollamudi
Info - 3 points
TOPICS IN QUANTITATIVE FINANCE
Modern Value Investing
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.
- T 1:10 p.m.-3:40 p.m.
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Instructor:
Khosrow Dehnad
CULPA: 2 Info - 1.5 points
COMPUT METHODS IN FINANCE
MS IEOR students only. Application of various computational methods/techniques in quantitative/computational finance. Transform techniques: fast Fourier transform for data de-noising and pricing, finite difference methods for partial differential equations (PDE), partial integro-differential equations (PIDE), Monte-Carlo simulation techniques in finance, and calibration techniques, filtering and parameter estimation techniques. Computational platform will be C++/Java/Python/Matlab/R.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
Alireza Javaheri
Info - 3 points
ALGORITHMIC TRADING
Prerequisite(s): 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.
- T 7 p.m.-9:30 p.m.
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Instructor:
Charles Pehlivanian
Info - 3 points
AI Applications in Finance
Data, models, visuals; various facets of AI, applications in finance; areas: fund, manager, security selection, asset allocation, risk management within asset management; fraud detection and prevention; climate finance and risk; data-driven real estate finance; cutting-edge techniques: machine learning, deep learning in computational, quantitative finance; concepts: explainability, interpretability, adversarial machine learning, resilience of AI systems; industry utilization
- FS 9 a.m.-5 p.m.
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Instructor:
Ali Hirsa
h-index: 8 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 p.m.-9 p.m.
- 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.
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Instructor:
Anish Agarwal
Info -
Instructor:
Shipra Agrawal
h-index: 24 Info -
Instructor:
Eric Balkanski
h-index: 13 Info - 1-3 points