ACCOUNTING AND FINANCE
For undergraduates only. Examines the fundamental concepts of financial accounting and finance, from the perspective of both managers and investors. Key topics covered include principles of accrual accounting; recognizing and recording accounting transactions; preparation and analysis of financial statements; ratio analysis; pro-forma projections; time value of money (present values, future values and interest/discount rates); inflation; discounted-cash-flow (DCF) project evaluation methods; deterministic and probabilistic measures of risk; capital budgeting.
- TR 1:10 p.m.-2:25 p.m.
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Instructor:
Christopher A Perez
Info - 3 points
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
STOCHASTIC SYSTEMS AND APPLICATIONS
Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance. IEOR E3106 must be completed by the fifth term. Only students with special academic circumstances may be allowed to take these courses in alternative semesters with the consultation of CSA and Departmental advisers.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info - 3 points
FOUNDATIONS OF OPTIMIZATION
This first course in optimization focuses on theory and applications of linear optimization, network optimization, and dynamic programming.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Eric Balkanski
h-index: 13 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 2:40 p.m.-3:55 p.m.
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Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 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
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
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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
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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:
Karl Sigman
CULPA: 15
Info
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info -
Instructor:
Wenpin Tang
h-index: 6 Info - 1-3 points
UNDERGRAD RESEARCH OR PROJECT
Independent work involving experiments, computer programming, analytical investigation, or engineering design.
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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 - 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 10:10 a.m.-11:25 a.m.
- MW 2:40 p.m.-3:55 p.m.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Shipra Agrawal
h-index: 24 Info -
Instructor:
Yaren Kaya
Info - 3 points
OPT MODELS & METHODS FOR FE
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.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Tianyi Lin
Info - 3 points
PROBABILITY STAT & SIMULATION
Basic probability theory, including independence and conditioning, discrete and continuous random variable, law of large numbers, central limit theorem, and stochastic simulation, basic statistics, including point and interval estimation, hypothesis testing, and regression; examples from business applications such as inventory management, medical treatments, and finance. A specialized version of IEOR E4150 for MSE and MSBA students.
- TR 10:10 a.m.-11:25 a.m.
- TR 11:40 a.m.-12:55 p.m.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Henry Lam
h-index: 17 Info -
Instructor:
Yi Zhang
CULPA: 2 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 1:10 p.m.-2:25 p.m.
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Instructor:
David D Yao
CULPA: 5
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.
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Instructor:
Soulaymane Kachani
CULPA: 12 Info - 3 points
INTRO-PROBABILITY & STATISTICS
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students.
- MW 8:40 a.m.-9:55 a.m.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Antonius B Dieker
CULPA: 10 h-index: 19 Info - 3 points
MSIEOR Quantitative Bootcamp
Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students.
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Instructor:
Michael Miller
CULPA: 18 h-index: 14 Info - 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.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
Harry West
CULPA: 1 h-index: 1 Info - 3 points
Data-driven Decision Modeling
Introduction to modeling, estimating, and solving decision-making problems in the context of artificial intelligence and analytics. Potential topics include choice models, quantity models, online learning using multi-armed bandits, dynamic decision modeling, dynamic games, and Bayesian learning theory. Practice both theory and applications using Python programming.
HUMAN FACTORS: PERFORMANCE
Required for undergraduate students majoring in IE. Sensory and cognitive (brain) processing considerations in the design, development, and operations of systems, products, and tools. User or operator limits and potential in sensing, perceiving decision making, movement coordination, memory, and motivation.
- M 4:10 p.m.-6:40 p.m.
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Instructor:
Leon S Gold
CULPA: 6
Info
- 3 points
Data Analytics & Machine Learning for OR
Data Analytics & ML for O
Surveys tools available in Python for getting (web scraping and APIs) and visualizing data (charts and maps). Introduction to analytics through machine learning (ML algorithms, model evaluation, text analytics, network algorithms, deep learning).
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Hardeep Johar
Info - 3 points
STATISTICS AND DATA ANALYSIS
Descriptive statistics, central limit theorem, parameter estimation, sufficient statistics, hypothesis testing, regression, logistic regression, goodness-of-fit tests, applications to operations research models.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Fabrizio Lecci
Info - 3 points
Corporate Finance, Accounting & Investme
CorpFin, Account & Invest
Interpret financial statements, build cash flow models, value projects, value companies, and make Corporate Finance decisions. Additional topics include: cost of capital, dividend policy, debt policy, impact of taxes, Shareholder/Debtholder agency costs, dual-class shares, using option pricing theory to analyze management behavior, investment banking activities, including equity underwriting, syndicated lending, venture capital, private equity investing and private equity secondaries. Application of theory in real-world situations: analyzing financial activities of companies such as General Electric, Google, Snapchat, Spotify, and Tesla.
- R 5:40 p.m.-7:55 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 8:40 a.m.-9:55 a.m.
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Instructor:
Henry Lam
h-index: 17 Info - 3 points
GAME THEOR MODELS OF OPERATION
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.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Jay Sethuraman
CULPA: 10 Info - 3 points
APPLICATIONS PROGRAMMNG FOR FE
We will take a hands-on approach to developing computer applications for Financial Engineering. Special focus will be placed on high-performance numerical applications that interact with a graphical interface. In the course of developing such applications we will learn how to create DLLs, how to integrate VBA with C/C++ programs, and how to write multithreaded programs. Examples of problems settings that we consider include simulation of stock price evolution, tracking, evaluation and optimization of a stock portfolio; optimal trade execution. In the course of developing these applications, we review topics of interest to OR:FE in a holistic fashion.
TOOLS FOR ANALYTICS
MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH.
Frontiers of Tough Tech
Survey of frontier research in technological areas to address world's most important challenges. Areas include advanced materials, biotech, and sustainability and climate. Convergence of breakthrough research with entrepreneurship, industry, and policy.
- T 2:40 p.m.-3:55 p.m.
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Instructor:
Harry West
CULPA: 1 h-index: 1 Info - 3 points
DESIGN DIGITAL OPERATING MODELS
IEOR students only. Understand digital businesses, apply scientific, engineering thinking to digital economy. Data-driven digital strategies and operating models. Sectors: ecommerce, advertising technology, and marketing technology. Automation of the marketing, sales, and advertising functions. Algorithms, patents, and business models. Business side of the digital ecosytem and the digital economy.
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Anthony Effik
Info - 3 points
HEALTHCARE OPERATIONS MGT
Prerequisite(s): for senior undergraduate Engineering students: IEOR E3608, E3658, and E4307; for Engineering graduate students (M.S. or Ph.D.): Probability and statistics at the level of IEOR E4150, and deterministic models at the level of IEOR E4004; for healthcare management students: P8529 Analytical methods for health services management.
Develops modeling, analytical, and managerial skills of engineering and health care management students. Enables students to master an array of fundamental operations management tools adapted to the management of health care systems. Through real-world business cases, students learn to identify, model, and analyze operational improvements and innovations in a range of health care contexts.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Yaren Kaya
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
IEOR students only; priority to MSBA students. 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, Scikit-lern, 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.
- MW 8:40 a.m.-9:55 a.m.
- MW 11:40 a.m.-12:55 p.m.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Uday Menon
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.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Christian Kroer
h-index: 16 Info - 3 points
ANALYTICS ON THE CLOUD
To introduce students to programming issues around working with clouds for data analytics. Class will learn how to work with infrastructure of cloud platforms, and discussion about distributed computing, focus of course is on programming. Topics covered include MapReduce, parallelism, rewriting of algorithms (statistical, OR, and machine learning) for the cloud, and basics of porting applications so that they run on the cloud.
- M 10:10 a.m.-12:55 p.m.
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Instructor:
Hardeep Johar
Info - 3 points
Reinforcement Learning
Markov Decision Processes (MDP) and Reinforcement Learning (RL) problems. Reinforcement Learning algorithms including Q-learning, policy gradient methods, actor-critic method. Reinforcement learning while doing exploration-exploitation dilemma, multi-armed bandit problem. Monte Carlo Tree Search methods, Distributional, Multi-agent, and Causal Reinforcement Learning.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Shipra Agrawal
h-index: 24 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.
- MW 11:40 a.m.-12:55 p.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 7:10 p.m.-9:40 p.m.
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Instructor:
Krzysztof M Choromanski
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
ENTREPRENEURSHIP BOOTCAMP
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.
- F 9 a.m.-5 p.m.
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Instructor:
David B Lerner
Info - 1.5 points
TOPICS IN OPERATIONS RESEARCH
FINANCE & STRUCTURING FOR DS
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.
- R 1:10 p.m.-3:40 p.m.
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Instructor:
Khosrow Dehnad
CULPA: 2 Info - 3 points
TOPICS IN OR
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.
- 3 points
TOPICS IN OPERATIONS RESEARCH
BusinessAnalytics Seminar
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.
- R 5:40 p.m.-7:10 p.m.
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Instructor:
Uday Menon
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
Data-driven Method Financ
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 7:10 p.m.-9:40 p.m.
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Instructor:
Naftali Cohen
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
APPLIED RISK ANALYTICS
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
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Amit Arora
Info - 1.5 points
TOPICS IN OPERATION RESEARCH
STARTUP FUNDAMENTALS
A project-based course in Forecasting, predicting a time series into the future, to prepare students for real-world applications including articulating the business case, value creation, problem statement, and the iterative development of solutions including building a data pipeline, exploration, modeling, and visualizations. The course will use Statistical methods, Machine Learning, and Deep Learning with Transformer-based methods to predict a time series. It will use nuggets of signal processing to augment Machine Learning models to characterize and filter orders of dynamics in the time series data.
- T 6 p.m.-8:30 p.m.
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Instructor:
Owen E Davis
Info - 3 points
TOPICS IN OR
MODERN RECOMMENDATION SYS
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.
- M 6 p.m.-8:30 p.m.
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Instructor:
Kenneth Goodman
Info - 3 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.
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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 7:10 p.m.-9:40 p.m.
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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.
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Instructor:
Charles R Guetta
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.
-
Instructor:
Charles R Guetta
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 10:10 a.m.-11:25 a.m.
- 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 11:40 a.m.-12:55 p.m.
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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 1:10 p.m.-2:25 p.m.
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Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
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.
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Instructor:
Wenpin Tang
h-index: 6 Info - 3 points
GLOBAL CAPITAL MARKETS
An introduction to capital markets and investments providing an overview of financial markets and tools for asset valuation. Topics covered include the pricing of fixed-income securities (treasury markets, interest rate swaps futures, etc.), discussions on topics in credit, foreign exchange, sovereign ad securitized markets—private equity and hedge funds, etc.
- M 6 p.m.-8:30 p.m.
-
Instructor:
Siddhartha Ghosh Dastidar
CULPA: 2 Info - 3 points
TOPICS IN QUANTATIVE FINANCE
FINANCIAL ENG. FOR ESG FINANCE
ESG (Environmental, Social and Corporate Governance) Finance is a rapidly growing area of Investment Management – and Finance more broadly – that has received a lot of attention in the past several years from the investor community, financial regulatory agencies, and the general public alike. This course provides an introduction to ESG Finance from a financial engineer’s perspective. This course also discusses proliferation of newly available data sources and the associated quantitative techniques necessary to process those. A major component of this course is a discussion of Climate Risk, an area of particular focus due to its increasing general importance. The course includes an overview of both recent research and the evolving regulatory landscape in the climate risk space. An in-depth discussion of financial impact assessment of various climate risk-driven scenarios (climate risk stress testing) concludes the course.
- T 7:10 p.m.-9:40 p.m.
-
Instructor:
Cyril Shmatov
Info - 1.5 points
TOPICS IN QUANTATIVE FINANCE
TERM STRUCTURES& CREDIT MODELS
In this course, we will cover the basics of mathematical modeling of interest rates and credit derivatives. In the first part, we will cover basic interest rate derivatives, the Heath-Jarrow-Morton (HJM)
framework, classic short rate models (for both interest rates and default intensities), and the numerical techniques used in practice for their calibration. In the second part, we will cover the basics
of single-name derivatives modeling, and we will discuss pricing simple credit derivatives. We will also discuss correlation products and the most common techniques used for their pricing. In the third part, we will discuss some recent research papers addressing the use of adjoint algorithmic differentiation for the calculation of risk for interest rate and credit derivatives.
- W 5:40 p.m.-8:10 p.m.
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Instructor:
Luca Capriotti
Info - 3 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 7:10 p.m.-9:40 p.m.
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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.
- R 4:10 p.m.-6:40 p.m.
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Instructor:
David Derosa
CULPA: 4 h-index: 9 Info - 1.5 points
STRUCTURED&HYBRID PRODUCTS
Prerequisite(s): 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 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.
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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.
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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.
- R 10:10 a.m.-11:25 a.m.
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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.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Allan M Malz
CULPA: 1 Info - 3 points